International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Swarm algorithm based adaptive filter design to remove power line interferenc...eSAT Journals
Abstract
ECG signal is having wide importance in the biomedical field, but for proper diagnosis of ECG always a noise free ECG signal is needed. Many researchers have already developed filters for getting appropriate desirable ECG signal and till today many researchers are still developing different filters using different algorithms in order to get clearer ECG signal for proper diagnosis. Noises and Interferences get added in the ECG by different ways, at the time of ECG Acquisition or at the time of ECG signal recording.
In this paper newly adapted algorithm is used for the filtering of ECG signal that is a Swarm algorithm which is used for the Error signal optimization from the original corrupted ECG signal. This algorithm is implemented with Adaptive filter to removes Power Line Interference noise having Frequency component of 50 Hz. The ECG signal considered may be retrieved from ECG acquisition system or from MIT-BIH database.
Keywords: Adaptive Filter, SWARM Algorithm, MIT-BIH Database, Matlab, ECG Signal and Power line Noise Signal etc.
This document describes a study that uses Kohonen neural network (KNN) to automatically identify the cutoff frequency for denoising electrocardiogram (ECG) signals. The methodology involves collecting noisy ECG data, removing baseline wandering using empirical mode decomposition, transforming the signal to the frequency domain using fast Fourier transform, applying KNN to cluster the frequency coefficients and identify the cutoff frequency, and filtering the signal using a finite impulse response low pass filter with the identified cutoff frequency. The results show that the KNN approach more effectively denoises the ECG signals compared to conventional filtering methods by identifying a lower cutoff frequency that removes more noise.
1) The document describes the design of an embedded wireless ECG system using IEEE 802.11G for wireless transmission. It acquires ECG signals from electrodes, amplifies and filters the signals, digitizes them using a PIC microcontroller, and transmits the data wirelessly.
2) The received data is processed using MATLAB to remove power line interference through an EMI filter designed using a core algorithm. This allows specialists to remotely monitor patients' ECG signals.
3) The EMI filter effectively tracks variations in interference frequency and amplitude to extract the power line signal from the ECG, improving over existing techniques. This allows clean ECG signals to be obtained with minimal computational resources.
Computer Based Model to Filter Real Time Acquired Human Carotid PulseCSCJournals
This document describes a computer-based model developed in Simulink to filter real-time acquired human carotid pulse signals. The model uses digital filtering techniques like FIR filters, IIR notch filters, spectrum analysis, and convolution to filter noise from carotid pulses acquired non-invasively using a piezoelectric sensor. The techniques are tested on real-time carotid data and results show the designed filters and techniques accurately filter noise and provide an easy way to acquire and analyze bio-signals on a computer in real-time.
The abnormal condition of electrical activity of
the heart given by ECG (Electrocardiogram) shows the cardiac
diseases affecting the human being. The P, QRS, T wave shape,
amplitude and time intervals between its various peaks contains
useful information about the nature of disease.
This paper presents wavelet technique to analyze ECG signal.
Discrete Wavelet Transform (DWT) is employed as noise
removal and feature extraction tool to achieve efficient design.
Daubechies wavelet of order 10 has been designed using Verilog
Hardware Description Language (HDL) and ModelSim Altera
6.4a is used as simulator. MIT-BIH database has been used for
the analysis
Motion artifacts reduction in cardiac pulse signal acquired from video imaging IJECEIAES
This study examines the possibility of remotely measuring the cardiac pulse activity of a patient, which could be an alternative technique to the classical method. This type of measurement is non-invasive. However, several limitations may deteriorate the accuracy of the results, including changes in ambient illumination, motion artifacts (MA) and other interferences that may occur through video recording. The paper in hand presents a new approach as a remedy for the aforementioned problem in cardiac pulse signals extracted from facial video recordings. Partitioning provides the basis for the presented MA reduction method; the acquired signals are partitioned into two sets for each second and every partition is shifted to the mean level and then all the partitions are recombined again into one signal, which is followed by low-pass filtering for enhancement. The proposed compared with ordinary pulse oximetry Photoplethysmographic (PPG) method. The resulted correlation coefficient was found (0.957) when calculated between the results of the proposed method and the ordinary one. Experiments were implemented using a common camera by creating a dataset from 11 subjects. The ease of implementation of this method with a simple that can be used to monitor the cardiac pulse rates in both home and the clinical environments.
An ECG-on-Chip with 535-nW/Channel Integrated Lossless Data Compressor for Wi...ecgpapers
Abstract—This paper presents a low-power ECG recording
system-on-chip (SoC) with on-chip low-complexity lossless ECG
compression for data reduction in wireless/ambulatory ECG
sensor devices. The chip uses a linear slope predictor for data
compression, and incorporates a novel low-complexity dynamic
coding-packaging scheme to frame the prediction error into
fixed-length 16 bit format. The proposed technique achieves an
average compression ratio of 2.25× on MIT/BIH ECG database.
Implemented in a standard 0.35 μm process, the compressor uses
0.565 K gates/channel occupying 0.4 mm for four channels, and
consumes 535 nW/channel at 2.4 V for ECG sampled at 512 Hz.
Small size and ultra-low-power consumption makes the proposed
technique suitable for wearable ECG sensor applications.
This document presents a method for extracting myopotentials (EMG noise) from an ECG signal using a median filter and adaptive wavelet Wiener filter. The ECG signal is first processed with a median filter to reduce noise. Then, an adaptive wavelet Wiener filter is applied which uses statistical characteristics of the signal and noise in the wavelet domain to estimate noise-free wavelet coefficients. Simulation results show the proposed method achieves a higher signal-to-noise ratio of 13.7 dB compared to other filtering methods like the adaptive wavelet Wiener filter alone, wavelet Wiener filter, and wavelet filter. The median filter provides better myopotential reduction than the other techniques.
Swarm algorithm based adaptive filter design to remove power line interferenc...eSAT Journals
Abstract
ECG signal is having wide importance in the biomedical field, but for proper diagnosis of ECG always a noise free ECG signal is needed. Many researchers have already developed filters for getting appropriate desirable ECG signal and till today many researchers are still developing different filters using different algorithms in order to get clearer ECG signal for proper diagnosis. Noises and Interferences get added in the ECG by different ways, at the time of ECG Acquisition or at the time of ECG signal recording.
In this paper newly adapted algorithm is used for the filtering of ECG signal that is a Swarm algorithm which is used for the Error signal optimization from the original corrupted ECG signal. This algorithm is implemented with Adaptive filter to removes Power Line Interference noise having Frequency component of 50 Hz. The ECG signal considered may be retrieved from ECG acquisition system or from MIT-BIH database.
Keywords: Adaptive Filter, SWARM Algorithm, MIT-BIH Database, Matlab, ECG Signal and Power line Noise Signal etc.
This document describes a study that uses Kohonen neural network (KNN) to automatically identify the cutoff frequency for denoising electrocardiogram (ECG) signals. The methodology involves collecting noisy ECG data, removing baseline wandering using empirical mode decomposition, transforming the signal to the frequency domain using fast Fourier transform, applying KNN to cluster the frequency coefficients and identify the cutoff frequency, and filtering the signal using a finite impulse response low pass filter with the identified cutoff frequency. The results show that the KNN approach more effectively denoises the ECG signals compared to conventional filtering methods by identifying a lower cutoff frequency that removes more noise.
1) The document describes the design of an embedded wireless ECG system using IEEE 802.11G for wireless transmission. It acquires ECG signals from electrodes, amplifies and filters the signals, digitizes them using a PIC microcontroller, and transmits the data wirelessly.
2) The received data is processed using MATLAB to remove power line interference through an EMI filter designed using a core algorithm. This allows specialists to remotely monitor patients' ECG signals.
3) The EMI filter effectively tracks variations in interference frequency and amplitude to extract the power line signal from the ECG, improving over existing techniques. This allows clean ECG signals to be obtained with minimal computational resources.
Computer Based Model to Filter Real Time Acquired Human Carotid PulseCSCJournals
This document describes a computer-based model developed in Simulink to filter real-time acquired human carotid pulse signals. The model uses digital filtering techniques like FIR filters, IIR notch filters, spectrum analysis, and convolution to filter noise from carotid pulses acquired non-invasively using a piezoelectric sensor. The techniques are tested on real-time carotid data and results show the designed filters and techniques accurately filter noise and provide an easy way to acquire and analyze bio-signals on a computer in real-time.
The abnormal condition of electrical activity of
the heart given by ECG (Electrocardiogram) shows the cardiac
diseases affecting the human being. The P, QRS, T wave shape,
amplitude and time intervals between its various peaks contains
useful information about the nature of disease.
This paper presents wavelet technique to analyze ECG signal.
Discrete Wavelet Transform (DWT) is employed as noise
removal and feature extraction tool to achieve efficient design.
Daubechies wavelet of order 10 has been designed using Verilog
Hardware Description Language (HDL) and ModelSim Altera
6.4a is used as simulator. MIT-BIH database has been used for
the analysis
Motion artifacts reduction in cardiac pulse signal acquired from video imaging IJECEIAES
This study examines the possibility of remotely measuring the cardiac pulse activity of a patient, which could be an alternative technique to the classical method. This type of measurement is non-invasive. However, several limitations may deteriorate the accuracy of the results, including changes in ambient illumination, motion artifacts (MA) and other interferences that may occur through video recording. The paper in hand presents a new approach as a remedy for the aforementioned problem in cardiac pulse signals extracted from facial video recordings. Partitioning provides the basis for the presented MA reduction method; the acquired signals are partitioned into two sets for each second and every partition is shifted to the mean level and then all the partitions are recombined again into one signal, which is followed by low-pass filtering for enhancement. The proposed compared with ordinary pulse oximetry Photoplethysmographic (PPG) method. The resulted correlation coefficient was found (0.957) when calculated between the results of the proposed method and the ordinary one. Experiments were implemented using a common camera by creating a dataset from 11 subjects. The ease of implementation of this method with a simple that can be used to monitor the cardiac pulse rates in both home and the clinical environments.
An ECG-on-Chip with 535-nW/Channel Integrated Lossless Data Compressor for Wi...ecgpapers
Abstract—This paper presents a low-power ECG recording
system-on-chip (SoC) with on-chip low-complexity lossless ECG
compression for data reduction in wireless/ambulatory ECG
sensor devices. The chip uses a linear slope predictor for data
compression, and incorporates a novel low-complexity dynamic
coding-packaging scheme to frame the prediction error into
fixed-length 16 bit format. The proposed technique achieves an
average compression ratio of 2.25× on MIT/BIH ECG database.
Implemented in a standard 0.35 μm process, the compressor uses
0.565 K gates/channel occupying 0.4 mm for four channels, and
consumes 535 nW/channel at 2.4 V for ECG sampled at 512 Hz.
Small size and ultra-low-power consumption makes the proposed
technique suitable for wearable ECG sensor applications.
This document presents a method for extracting myopotentials (EMG noise) from an ECG signal using a median filter and adaptive wavelet Wiener filter. The ECG signal is first processed with a median filter to reduce noise. Then, an adaptive wavelet Wiener filter is applied which uses statistical characteristics of the signal and noise in the wavelet domain to estimate noise-free wavelet coefficients. Simulation results show the proposed method achieves a higher signal-to-noise ratio of 13.7 dB compared to other filtering methods like the adaptive wavelet Wiener filter alone, wavelet Wiener filter, and wavelet filter. The median filter provides better myopotential reduction than the other techniques.
Denoising of Radial Bioimpedance Signals using Adaptive Wavelet Packet Transf...iosrjce
In recent years, the accurate computer aided diagnosis of the cardiovascular diseases is gaining
momentum. In addition to accuracy, non-invasiveness of the measurement techniques has become the need of
the hour. Impedance cardiography is one such method which has become a synonym for indirect assessment of
monitoring the stroke volume, cardiac output and other hemodynamic parameters by monitoring the blood
volume changes of the body. Changes occurring in the blood volume within a certain body segment due to
various physiological processes are captured in terms of the impedance variations of that segment. But this
method is affected by electrical noise such as power line hum and motion and respiratory artifacts due to
movement of the subject while acquiring the bioimpedance signal. This can cause errors in the automatic
extraction of the characteristic points for estimation the hemodynamic parameters. This paper presents two
algorithms for baseline wander removal from the bioimpedance waveform obtained at the radial pulse of the left
hand, one based on wavelet packet decomposition and the other based on the Kalman filter. The impedance
signals have been acquired by using the peripheral pulse analyzer. The results for the wavelet packet decomposition are found to be better than that of the Kalman filter.
Cardio Logical Signal Processing for Arrhythmia Detection with Comparative An...IRJET Journal
This document summarizes research on detecting cardiac arrhythmias by analyzing electrocardiogram (ECG) signals. ECG signals are often contaminated with power line interference that must be removed using a notch filter before features can be extracted. The researchers compare the impact of different Q-factor values for the notch filter on the QRS complex of the ECG. They detect the QRS complex using difference operation method and then calculate features of the R-peak like sharpness and slope. A linear classifier is then used to classify signals as normal or arrhythmic based on these features.
The document discusses ECG signal analysis and abnormality detection using artificial neural networks. It defines normal and abnormal ECG signals, describing abnormalities like bradycardia and tachycardia. Two algorithms are described for detecting abnormalities: one analyzes heart rate and the other detects general heart diseases. An ANN system is used for ECG analysis and classification, taking spectral entropy, Poincare plot geometry, and largest Lyapunov exponent as inputs to classify eight cardiac conditions.
International Journal of Computational Engineering Research(IJCER) ijceronline
This paper proposes a lightweight, low-cost wearable ECG monitoring device using digital signal processing. An ECG acquisition system is designed using electrodes, instrumentation amplifiers, and filters to capture and preprocess the ECG signal. A PIC microcontroller detects the R-peak in the ECG to calculate heart rate. Results from testing on patients matched clinical analysis. The system aims to remotely monitor patients at low cost to detect cardiac issues earlier. Future work includes transmitting the digital ECG data wirelessly to doctors for remote monitoring and analysis.
IRJET- A Comparative Analysis of CMOS Amplifiers for ECG SignalsIRJET Journal
The document compares different amplifiers for amplifying electrocardiography (ECG) signals, including differential amplifiers, operational amplifiers, and operational transconductance amplifiers (OTAs). It analyzes the gain, common mode rejection ratio (CMRR), and other performance parameters of each amplifier through simulations and calculations. The OTA is found to have higher gain and comparable CMRR to operational amplifiers. Overall, the OTA provides better amplification of low-level ECG signals and is well-suited for biomedical applications requiring low noise and power efficient amplification.
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
This document summarizes a study that developed a low-cost electrocardiograph (ECG) machine with adjustable sensitivity and paper speed options. The ECG machine uses an ATmega16 microcontroller and includes a 12-channel ECG amplifier built with instrumentation amplifiers. Testing was conducted on 10 subjects and an ECG phantom to evaluate the machine's performance at different sensitivities (0.5 mV, 1 mV, 2 mV) and paper speeds (25 mm/s, 50 mm/s). Results showed the machine measured heart rates from the phantom within 2% error of the standard values and sensitivity measurements were also within specifications. The developed low-cost ECG machine achieved diagnostic-level performance with adjustable options, addressing
This document presents the design of a telemedical system for remote monitoring of cardiac insufficiency. The system includes an electrocardiography (ECG) device that collects and digitizes ECG signals. The ECG signals undergo digital signal processing including autocorrelation analysis. Graphical interfaces allow patients and doctors to view ECG data and attenuation coefficients derived from autocorrelation analysis. Data is transmitted between parties using TCP/IP protocol. The system aims to facilitate remote monitoring of cardiac patients to reduce hospitalizations through early detection of health changes.
The document describes a new pulse oximeter system called OxiSense that uses a custom low-power application specific integrated circuit (ASIC) for signal conditioning. Key features include testability of analog and digital modules in the ASIC, a new embedded signal processing algorithm, and wireless data transmission. The ASIC consumes 176 μW for analog front-end processing and 23 μW for digital control. The complete prototype system operates from a battery and consumes 8 mW total power while achieving accurate oxygen saturation and heart rate measurements compared to commercial devices.
FPGA based Heart Arrhythmia’s Detection AlgorithmIDES Editor
Electrocardiogram (ECG) signal has been widely used
for heart diagnoses .In this paper, we presents the design of
Heart Arrhythmias Detector using Verilog HDL based on been
mapped on small commercially available FPGAs (Field
Programmable Gate Arrays). Majority of the deaths occurs
before emergency services can step in to intervene. In this
research work, we have implemented QRS detection device
developed by Ahlstrom and Tompkins in Verilog HDL. The
generated source has been simulated for validation and tested
on software Verilogger Pro6.5. We have collected data from
MIT-BIH Arrhythmia Database for test of proposed digital
system and this data have given MIT-BIH data as an input of
our proposed device using test bench software. We have
compared our device output with MATLAB output and
calculating the error percentage and got desire research key
point of RR interval between the peaks of QRS signal. The
proposed system also investigated with different database of
MIT-BIH for detect different heart Arrhythmias and proposed
device give output exactly same according to our QRS detection
algorithm.
Wavelet based Signal Processing for Compression a Methodology for on-line Tel...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
Implementation and demonstration of li fi technologyeSAT Journals
Abstract Li-Fi is a wireless communication system in which light is used as a carrier signal instead of traditional radio frequency as in Wi-Fi. Li-Fi is a technology that uses light emitting diodes to transmit data wirelessly. Li-Fi is a form of Visible Light Communication (VLC). VLC uses rapid pulses of light to transmit information wirelessly that cannot be detected by the human eye. This paper demonstrates the working of Li-Fi by simulating a simple circuit which gave us the required output. Li-Fi technology was first demonstrated by Harald Hass, a German Physicist from the University of Edinburgh Keywords—Li-Fi, VLC, Optical Communication, Wireless Communication, LED, Visible Light Spectrum.
This document describes a digitally tunable lowpass-notch filter designed for brain signal measurement applications. The filter aims to suppress power line interference at 60Hz while passing EEG signals between 1-40Hz. It uses a combination of a notch filter and high-order lowpass filter with a tunable notch frequency implemented using a fifth-order transconductance-capacitor design. The filter was prototyped on a test board and simulation and measurement results showed the notch frequency could be digitally tuned by changing the voltage on programmable capacitor switches in the filter design.
Artifact elimination in ECG signal using wavelet transformTELKOMNIKA JOURNAL
Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
This presentation discusses signal analysis of an electrocardiogram (ECG) using MATLAB. It introduces ECG and its importance in measuring heart rate. The document outlines the process of acquiring an ECG signal through electrodes and converting it to a digital signal for processing. Key steps discussed include filtering the energy signal to highlight peaks, detecting peaks to measure intervals between R waves, and computing the heart rate frequency from these intervals. In conclusion, it argues that simple digital filters and algorithms make this a feasible method for real-time heart rate measurement applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Implementation of Embedded System in Patient Monitoring Systemijsrd.com
This paper deals with the measuring of multi-parameter to measure ECG, temperature, evoked potential, respiration rate which uses sensors to measure the patient condition continuously in ICU. For each parameter it uses separate sensors .this multi-channel parameter uses special type of sensors called infracted rays (IR) which are not harmful to human body. All this signals are collected from the patient's body then it is send to the computer and it is diagnosed by the doctor .It reduces the work for the doctors and it gives accurate values. If any abnormalities in the patient's body it produces alarm and it alerts the doctors. This paper also deals with online videography i.e the doctors can view the patient's condition anywhere from the hospital's. Results are stored in the secondary storage system in computer for future reference. the results are obtained in the form of graph, waveforms.
The research of_portable_ecg_monitoring_system_with_usb_host_interfaceArhamSheikh1
This document describes a portable ECG monitoring system with a USB host interface. The system uses a microprocessor and USB host interface chip to collect and store ECG signals. It has a circuit to acquire high-quality ECG data and amplify low ECG voltages. Software implements USB protocols to recognize and configure USB drives, identify file systems, and write collected ECG data to the drive in real-time using bulk transfer methods. The portable design with a USB host allows connection to additional modules as needed.
This document discusses the design and implementation of a digital filter to remove power line noise from electrocardiogram (ECG) signals. It begins with an introduction to ECG signals and the types of noise that interfere with the signals, including power line noise. The document then covers the design of the digital filter, including choosing an infinite impulse response (IIR) Chebyshev type 1 filter to meet the specifications of sharp transition and high attenuation. MATLAB and Verilog simulations are used to test the designed digital filter on ideal and real ECG signals and evaluate the filtering performance.
ECG SIGNAL DE-NOISING USING DIGITAL FILTER TECHNIQUESIRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including discrete wavelet transforms (DWT) and low-pass filters (LPF). It evaluates these methods combined with moving mean, linear regression, and Savitzky-Golay smoothing on ECG signals corrupted with baseline wander noise, muscle noise, and motion artifact noise. The results show that LPF with moving mean smoothing achieved the best performance in terms of mean square error and signal-to-noise ratio, indicating it most effectively removed noise from the ECG signals.
Revealing and evaluating the influence of filters position in cascaded filter...nooriasukmaningtyas
In this paper, a new optimization on windowing technique based on finite
impulse response (FIR) filters is proposed for revealing and evaluating the
Influence of filters position in cascaded filter tested on the ECG signal denoising. baseline wander (BLW), power line interference (PLI) and
electromyography (EMG) noises are gettingremoved. The performance of the
adopted method is evaluated on the PTB diagnostic database. Subsequently,
the comparisons are based on signal to noise ratio (SNR) improvement and
mean square error (MSE) minimization. Where the Rectangular, and Kaiser
windows have been used for the more potent performances. The disparity
average (DA) of SNR values is detected; in both Kaiser and Rectangular
windows are assessed by ±0.38046dB and ±0.70278dB respectively, while
the MSE values were constant. The excellent configuration or filters position
(H-B-L) of the filtration system is selected according to high measurements
of SNR and low MSE too, to de-noise the ECG signals. First of all, this
applied approach has led to 31.30 dB SNR improvement with MSE
minimization of 26. 43%. This means that there is a significant contribution
to improving the field of filtration.
Denoising of Radial Bioimpedance Signals using Adaptive Wavelet Packet Transf...iosrjce
In recent years, the accurate computer aided diagnosis of the cardiovascular diseases is gaining
momentum. In addition to accuracy, non-invasiveness of the measurement techniques has become the need of
the hour. Impedance cardiography is one such method which has become a synonym for indirect assessment of
monitoring the stroke volume, cardiac output and other hemodynamic parameters by monitoring the blood
volume changes of the body. Changes occurring in the blood volume within a certain body segment due to
various physiological processes are captured in terms of the impedance variations of that segment. But this
method is affected by electrical noise such as power line hum and motion and respiratory artifacts due to
movement of the subject while acquiring the bioimpedance signal. This can cause errors in the automatic
extraction of the characteristic points for estimation the hemodynamic parameters. This paper presents two
algorithms for baseline wander removal from the bioimpedance waveform obtained at the radial pulse of the left
hand, one based on wavelet packet decomposition and the other based on the Kalman filter. The impedance
signals have been acquired by using the peripheral pulse analyzer. The results for the wavelet packet decomposition are found to be better than that of the Kalman filter.
Cardio Logical Signal Processing for Arrhythmia Detection with Comparative An...IRJET Journal
This document summarizes research on detecting cardiac arrhythmias by analyzing electrocardiogram (ECG) signals. ECG signals are often contaminated with power line interference that must be removed using a notch filter before features can be extracted. The researchers compare the impact of different Q-factor values for the notch filter on the QRS complex of the ECG. They detect the QRS complex using difference operation method and then calculate features of the R-peak like sharpness and slope. A linear classifier is then used to classify signals as normal or arrhythmic based on these features.
The document discusses ECG signal analysis and abnormality detection using artificial neural networks. It defines normal and abnormal ECG signals, describing abnormalities like bradycardia and tachycardia. Two algorithms are described for detecting abnormalities: one analyzes heart rate and the other detects general heart diseases. An ANN system is used for ECG analysis and classification, taking spectral entropy, Poincare plot geometry, and largest Lyapunov exponent as inputs to classify eight cardiac conditions.
International Journal of Computational Engineering Research(IJCER) ijceronline
This paper proposes a lightweight, low-cost wearable ECG monitoring device using digital signal processing. An ECG acquisition system is designed using electrodes, instrumentation amplifiers, and filters to capture and preprocess the ECG signal. A PIC microcontroller detects the R-peak in the ECG to calculate heart rate. Results from testing on patients matched clinical analysis. The system aims to remotely monitor patients at low cost to detect cardiac issues earlier. Future work includes transmitting the digital ECG data wirelessly to doctors for remote monitoring and analysis.
IRJET- A Comparative Analysis of CMOS Amplifiers for ECG SignalsIRJET Journal
The document compares different amplifiers for amplifying electrocardiography (ECG) signals, including differential amplifiers, operational amplifiers, and operational transconductance amplifiers (OTAs). It analyzes the gain, common mode rejection ratio (CMRR), and other performance parameters of each amplifier through simulations and calculations. The OTA is found to have higher gain and comparable CMRR to operational amplifiers. Overall, the OTA provides better amplification of low-level ECG signals and is well-suited for biomedical applications requiring low noise and power efficient amplification.
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
This document summarizes a study that developed a low-cost electrocardiograph (ECG) machine with adjustable sensitivity and paper speed options. The ECG machine uses an ATmega16 microcontroller and includes a 12-channel ECG amplifier built with instrumentation amplifiers. Testing was conducted on 10 subjects and an ECG phantom to evaluate the machine's performance at different sensitivities (0.5 mV, 1 mV, 2 mV) and paper speeds (25 mm/s, 50 mm/s). Results showed the machine measured heart rates from the phantom within 2% error of the standard values and sensitivity measurements were also within specifications. The developed low-cost ECG machine achieved diagnostic-level performance with adjustable options, addressing
This document presents the design of a telemedical system for remote monitoring of cardiac insufficiency. The system includes an electrocardiography (ECG) device that collects and digitizes ECG signals. The ECG signals undergo digital signal processing including autocorrelation analysis. Graphical interfaces allow patients and doctors to view ECG data and attenuation coefficients derived from autocorrelation analysis. Data is transmitted between parties using TCP/IP protocol. The system aims to facilitate remote monitoring of cardiac patients to reduce hospitalizations through early detection of health changes.
The document describes a new pulse oximeter system called OxiSense that uses a custom low-power application specific integrated circuit (ASIC) for signal conditioning. Key features include testability of analog and digital modules in the ASIC, a new embedded signal processing algorithm, and wireless data transmission. The ASIC consumes 176 μW for analog front-end processing and 23 μW for digital control. The complete prototype system operates from a battery and consumes 8 mW total power while achieving accurate oxygen saturation and heart rate measurements compared to commercial devices.
FPGA based Heart Arrhythmia’s Detection AlgorithmIDES Editor
Electrocardiogram (ECG) signal has been widely used
for heart diagnoses .In this paper, we presents the design of
Heart Arrhythmias Detector using Verilog HDL based on been
mapped on small commercially available FPGAs (Field
Programmable Gate Arrays). Majority of the deaths occurs
before emergency services can step in to intervene. In this
research work, we have implemented QRS detection device
developed by Ahlstrom and Tompkins in Verilog HDL. The
generated source has been simulated for validation and tested
on software Verilogger Pro6.5. We have collected data from
MIT-BIH Arrhythmia Database for test of proposed digital
system and this data have given MIT-BIH data as an input of
our proposed device using test bench software. We have
compared our device output with MATLAB output and
calculating the error percentage and got desire research key
point of RR interval between the peaks of QRS signal. The
proposed system also investigated with different database of
MIT-BIH for detect different heart Arrhythmias and proposed
device give output exactly same according to our QRS detection
algorithm.
Wavelet based Signal Processing for Compression a Methodology for on-line Tel...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
Implementation and demonstration of li fi technologyeSAT Journals
Abstract Li-Fi is a wireless communication system in which light is used as a carrier signal instead of traditional radio frequency as in Wi-Fi. Li-Fi is a technology that uses light emitting diodes to transmit data wirelessly. Li-Fi is a form of Visible Light Communication (VLC). VLC uses rapid pulses of light to transmit information wirelessly that cannot be detected by the human eye. This paper demonstrates the working of Li-Fi by simulating a simple circuit which gave us the required output. Li-Fi technology was first demonstrated by Harald Hass, a German Physicist from the University of Edinburgh Keywords—Li-Fi, VLC, Optical Communication, Wireless Communication, LED, Visible Light Spectrum.
This document describes a digitally tunable lowpass-notch filter designed for brain signal measurement applications. The filter aims to suppress power line interference at 60Hz while passing EEG signals between 1-40Hz. It uses a combination of a notch filter and high-order lowpass filter with a tunable notch frequency implemented using a fifth-order transconductance-capacitor design. The filter was prototyped on a test board and simulation and measurement results showed the notch frequency could be digitally tuned by changing the voltage on programmable capacitor switches in the filter design.
Artifact elimination in ECG signal using wavelet transformTELKOMNIKA JOURNAL
Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
This presentation discusses signal analysis of an electrocardiogram (ECG) using MATLAB. It introduces ECG and its importance in measuring heart rate. The document outlines the process of acquiring an ECG signal through electrodes and converting it to a digital signal for processing. Key steps discussed include filtering the energy signal to highlight peaks, detecting peaks to measure intervals between R waves, and computing the heart rate frequency from these intervals. In conclusion, it argues that simple digital filters and algorithms make this a feasible method for real-time heart rate measurement applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Implementation of Embedded System in Patient Monitoring Systemijsrd.com
This paper deals with the measuring of multi-parameter to measure ECG, temperature, evoked potential, respiration rate which uses sensors to measure the patient condition continuously in ICU. For each parameter it uses separate sensors .this multi-channel parameter uses special type of sensors called infracted rays (IR) which are not harmful to human body. All this signals are collected from the patient's body then it is send to the computer and it is diagnosed by the doctor .It reduces the work for the doctors and it gives accurate values. If any abnormalities in the patient's body it produces alarm and it alerts the doctors. This paper also deals with online videography i.e the doctors can view the patient's condition anywhere from the hospital's. Results are stored in the secondary storage system in computer for future reference. the results are obtained in the form of graph, waveforms.
The research of_portable_ecg_monitoring_system_with_usb_host_interfaceArhamSheikh1
This document describes a portable ECG monitoring system with a USB host interface. The system uses a microprocessor and USB host interface chip to collect and store ECG signals. It has a circuit to acquire high-quality ECG data and amplify low ECG voltages. Software implements USB protocols to recognize and configure USB drives, identify file systems, and write collected ECG data to the drive in real-time using bulk transfer methods. The portable design with a USB host allows connection to additional modules as needed.
This document discusses the design and implementation of a digital filter to remove power line noise from electrocardiogram (ECG) signals. It begins with an introduction to ECG signals and the types of noise that interfere with the signals, including power line noise. The document then covers the design of the digital filter, including choosing an infinite impulse response (IIR) Chebyshev type 1 filter to meet the specifications of sharp transition and high attenuation. MATLAB and Verilog simulations are used to test the designed digital filter on ideal and real ECG signals and evaluate the filtering performance.
ECG SIGNAL DE-NOISING USING DIGITAL FILTER TECHNIQUESIRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including discrete wavelet transforms (DWT) and low-pass filters (LPF). It evaluates these methods combined with moving mean, linear regression, and Savitzky-Golay smoothing on ECG signals corrupted with baseline wander noise, muscle noise, and motion artifact noise. The results show that LPF with moving mean smoothing achieved the best performance in terms of mean square error and signal-to-noise ratio, indicating it most effectively removed noise from the ECG signals.
Revealing and evaluating the influence of filters position in cascaded filter...nooriasukmaningtyas
In this paper, a new optimization on windowing technique based on finite
impulse response (FIR) filters is proposed for revealing and evaluating the
Influence of filters position in cascaded filter tested on the ECG signal denoising. baseline wander (BLW), power line interference (PLI) and
electromyography (EMG) noises are gettingremoved. The performance of the
adopted method is evaluated on the PTB diagnostic database. Subsequently,
the comparisons are based on signal to noise ratio (SNR) improvement and
mean square error (MSE) minimization. Where the Rectangular, and Kaiser
windows have been used for the more potent performances. The disparity
average (DA) of SNR values is detected; in both Kaiser and Rectangular
windows are assessed by ±0.38046dB and ±0.70278dB respectively, while
the MSE values were constant. The excellent configuration or filters position
(H-B-L) of the filtration system is selected according to high measurements
of SNR and low MSE too, to de-noise the ECG signals. First of all, this
applied approach has led to 31.30 dB SNR improvement with MSE
minimization of 26. 43%. This means that there is a significant contribution
to improving the field of filtration.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Noise reduction in ecg by iir filters a comparative studyIAEME Publication
The document describes a study comparing different digital filters for reducing noise in electrocardiogram (ECG) signals. ECG data was obtained from a database and noise was added, including 50Hz interference and high/low frequency noise. Fourth-order Butterworth, Chebyshev 1, Chebyshev 2, and elliptic filters were applied digitally. Butterworth filtering performed best by introducing minimum distortion while reducing noise, as determined by analyzing signal power and waveform distortion before and after filtering. The document aims to find the most effective digital filter for denoising ECG signals.
IRJET- R Peak Detection with Diagnosis of Arrhythmia using Adaptive Filte...IRJET Journal
The document presents a method for detecting R peaks in electrocardiogram (ECG) signals with high accuracy by combining adaptive filtering and Hilbert transform. Adaptive filtering reduces noise and estimates the fundamental signal, while Hilbert transform eliminates signal distortion and shows time dependency. Features are then extracted from the ECG, including RR interval, heart rate, QRS width, and PR interval. These features can be used to diagnose arrhythmias based on irregular heart rhythms. A graphical user interface was also developed to conveniently display the output waveform, features, and type of arrhythmia diagnosis. When tested on data from the MIT-BIH arrhythmia database, the proposed method achieved a sensitivity of 99.22% and positive predict
This document discusses using a triangular window-based FIR digital filter to remove powerline interference from ECG signals. ECG signals are often corrupted by noise such as powerline interference that can interfere with diagnosis. The authors designed a triangular window FIR filter with a modified triangular window function to filter ECG signals. They tested the filter on simulated noisy ECG data and found it successfully removed the 50Hz powerline interference, reducing the noise power. Analysis of the filter's magnitude, phase, and frequency responses indicated it provides stable and linear phase filtering required for ECG signal processing. The triangular window FIR filter is an effective technique for denoising ECG signals by removing powerline interference.
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
ECG signal denoising using a novel approach of adaptive filters for real-time...IJECEIAES
Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.
IRJET- Design Simulation and Analysis of Efficient De-Noising of ECG Signals ...IRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including adaptive filtering algorithms and a patch-based method. It first provides background on ECG signals and sources of noise that can interfere with diagnosis. Adaptive filters like least mean square (LMS) and recursive least squares (RLS) are introduced to update filter coefficients based on the signal environment. Simulation results show an ECG signal contaminated with powerline noise can be effectively filtered using LMS. The document also explores a patch-based nonlocal means method previously used for image denoising and applies it to remove noise from ECG signals.
A Wireless ECG Plaster for Real-Time Cardiac Health Monitoring in Body Senso...ecgpapers
In this paper we present a wireless ECG plaster
that can be used for real-time monitoring of ECG in cardiac
patients. The proposed device is light weight (25 grams),
wearable and can wirelessly transmit the patient’s ECG signal to
mobile phone or PC using ZigBee. The device has a battery life of
around 26 hours while in continuous operation, owing to the
proposed ultra-low power ECG acquisition front end chip. The
prototype has been verified in clinical trials.
ECG Signal Denoising using Digital Filter and Adaptive FilterIRJET Journal
1. The document discusses methods for denoising electrocardiogram (ECG) signals, including digital filters and adaptive filters.
2. It evaluates the performance of Savitzky-Golay filters, band pass filters, and adaptive noise cancellation techniques for removing noise from ECG signals and improving the signal-to-noise ratio.
3. The key filters discussed are Savitzky-Golay filters, Tompkins filters, Butterworth band pass filters, and least mean square adaptive filters, analyzing their ability to reduce noise like powerline interference, baseline drift, and motion artifacts from ECG data.
Suppression of power line interference correction of baselinewanders andIAEME Publication
This document summarizes a research paper that proposes a new method for enhancing electrocardiogram (ECG) signals based on the Constrained Stability Least Mean Square (CSLMS) algorithm. The CSLMS algorithm is applied to an adaptive noise cancellation filter to remove two dominant artifacts from ECG signals: high-frequency noise and baseline wander. Simulation results on ECG data from the MIT-BIH database show that the CSLMS method provides better denoising and artifact removal compared to the conventional LMS algorithm, improving signal-to-noise ratio by 3-6 decibels. The CSLMS algorithm exhibits smaller excess mean squared error and faster convergence than LMS, resulting in less signal distortion in
This document presents an adaptive algorithm for removing baseline wander (artifacts) from radial bioimpedance signals using wavelet packet transform. Bioimpedance signals are affected by respiratory and motion artifacts that cause baseline drift. Existing artifact removal methods are not fully effective due to spectral overlap between the signal and artifacts. The proposed method adaptively decomposes the signal using wavelet packets to distinguish the signal from the artifacts based on their energies at different scales. Simulation results on bioimpedance signals from 5 subjects show the method reduces variance by 71-94% and increases SNR, outperforming other wavelet functions. The algorithm effectively removes artifacts while preserving the bioimpedance signal characteristics.
This document discusses signal conversion systems for capturing and digitizing biomedical signals. It covers sampling theory, including the Nyquist sampling theorem. A typical analog-to-digital conversion system is described, involving sensors, amplifiers, filters, a sample-and-hold circuit and ADC. Requirements for converting biomedical signals include high accuracy, appropriate sampling rate, gain, speed, low power and small size. Common circuit elements for analog-digital and digital-analog conversion are also outlined.
Fpga based computer aided diagnosis of cardiac murmurs and soundseSAT Publishing House
This document describes an FPGA-based system for computer-aided diagnosis of cardiac murmurs and sounds. It discusses the use of an adaptive line enhancer with LMS algorithm implemented on an FPGA to eliminate noise from phonocardiogram signals. The key steps are:
1) A custom transducer is used to convert heart sounds to electrical signals which are then conditioned with amplification and filtering.
2) The conditioned analog signals are acquired using an ADC on an FPGA board and processed digitally on the FPGA using an adaptive filter to remove noise.
3) The processed digital signals are sent to MATLAB over Ethernet for further analysis.
2.4GHZ CLASS AB POWER AMPLIFIER FOR HEALTHCARE APPLICATIONijbesjournal
This document describes the design of a 2.4GHz class AB power amplifier for healthcare applications using a 0.18um CMOS process. The power amplifier consists of two stages - a driver stage and an output stage. It can transmit 10dBm of output power to a 50Ω load with a power gain of 10dB and power added efficiency of 7.5% at 1dB compression. The total power consumption is 0.135W, meeting the design requirements. The power amplifier is intended for use in wireless medical sensor networks to closely monitor patient vital signs.
The document describes a step-by-step DC component eliminator developed to remove the DC component from photoplethysmography (PPG) signals without distorting the shape of the AC component. The eliminator uses an analog comparator and operational amplifier. It allows recording of the PPG signal for 24-hour monitoring while accurately measuring the small AC component using a high-resolution analog-to-digital converter. Experimental results show the eliminator preserves the shape of the AC component after signal reconstruction.
Heart rate detection using hilbert transformeSAT Journals
Abstract The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA). Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR interval tachogram. The HRV provides information about the sympathetic-parasympathetic autonomic stability and consequently about the risk of unpredicted cardiac death. The heart beats in ECG signal are detected by detecting R-Peaks in ECG signals and used to determine useful information about the various cardiac abnormalities. The temporal locations of the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological signals and wrong placement of ECG electrodes, the QRS complex may be missed or falsely detected and may lead to poor results in calculating heart beat in turn inter-beat intervals. We have studied the effects of number of common elements of QRS detection methods using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocessed using band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold and finally the RR-intervals are calculated to determine Heart Rate. We have implemented our method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmia database. Our MATLAB implementation results in the detection of QRS complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV. Index Terms: ECG, QRS complex, R-Peaks, HRV, Heart Rate signal, RSA, Hilbert Transform, Arrhythmia, MIT/BIH, MATLAB and Lynn’s filters
Design and development of electro optical system for acquisition of ppg signa...eSAT Publishing House
This document describes the design and development of an electro-optical photoplethysmography (PPG) system for acquiring PPG signals to assess the cardiovascular system. The system uses a light emitting diode and photodetector in a finger probe to non-invasively measure blood volume changes with each heartbeat. The acquired PPG signals are digitized and wirelessly transmitted using ZigBee technology. Preliminary testing on normal subjects found the pulse rates measured by the developed system to be accurate and in good agreement with a standard pulse oximeter system. The wireless PPG system allows for remote monitoring of cardiovascular parameters and has potential applications in ambulatory monitoring and postoperative care.
IRJET- Congestive Heart Failure Recognition by Analyzing The ECG Signals usi...IRJET Journal
This document summarizes a study that analyzed ECG signals to recognize congestive heart failure using wavelet coefficients. ECG data from 20 different disease cases were collected and filtered to remove noise. A wavelet transform was applied to obtain wavelet coefficients, which were used to estimate cardiac diseases from the 3D wavelet plot. The zero crossing algorithm was also used to calculate heart rate from the number of zero crossings in the ECG signal. Results found that wavelet coefficients and 3D plots provided useful information for analyzing ECG signals and recognizing different heart conditions and diseases.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
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It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Astute Business Solutions | Oracle Cloud Partner |
Hk3613091316
1. Gaurav Gupta et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.1309-1316
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
Design Analysis of IIR Filter for Power Line Interference
Reduction in ECG Signals
Gaurav Gupta, Rajesh Mehra
M.E Scholar, Electronics & Communication Engineering National Institute of Technical Teachers Training &
Research, Chandigarh, India.
Associate Professor, Department of Electronics & Communication Engineering National Institute of Technical
Teachers Training & Research, Chandigarh, India.
Abstract
In this paper, interference due to 50Hz power line in the electrocardiogram (ECG) measurement is reduced with
the help of IIR filters. Pick up of hum from power line is a very common phenomenon in the measurement of
ECG signals. It is of prime need to reduce the variations coming due to power line so that one can analyze the
most of the critical points in the measured signal. Power Line Interference (PLI) may seriously degrade the ECG
signal, rendering the ECG analysis inaccurate. By employing digital filter approach the effect of PLI is
drastically reduced, thus, avoiding the conditions like changing the recording sites or installing expensive
shields. The developed IIR filter has been designed and simulated using MATLAB with Butterworth and
Chebyshev techniques. Both filters have been analyzed and compared in terms of their performance. It is found
in the results that Butterworth IIR filter gives a more satisfactory response.
Keywords- ECG, IIR, notch filters, PLI.
I. Introduction
In the area of Electrocardiogram (ECG)
analysis and filtration, the role of digital filters has a
rich history. An electrocardiogram (ECG) is a
graphical record of bioelectrical signal generated by
the human body during cardiac cycle. ECG
graphically gives useful information that relates to the
heart functioning by means of a base line and waves
representing the heart voltage changes during a period
of time, usually a short period [1]-[3]. Putting leads on
specific part of the human body, it is possible to get
changes of the bioelectrical heart signal where one of
the most basic forms of organizing them is known as
Einthoven lead system. The ECG has a special value
in the following clinical situations :
a. Auricular and ventricular hypertrophy.
b. Myocardial Infarction (heart attack).
c. Arrhythmias.
d. Generalized suffering affecting heart and blood
pressure.
e. Electrolytic transformations.
In spite of the special value, the ECG is
considered only a laboratory test. It is not an absolute
truth concerning the cardiac pathologies diagnosis.
There are examples of patients presenting string heart
diseases which present a normal ECG, and also
perfectly normal patients getting an abnormal ECG
[4]. Therefore, an ECG must always be interpreted
with the patient clinical information.
According to [5] a signal can be analyzed and
processed in two domains, time and frequency. ECG
signal is one of the human body signals which can be
analyzed and worked in time domain or frequency
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domain. Figure 1 presents typical waves in an ECG
signal. P, Q, R, S, T and U are specific wave forms
identified in the time domain of an ECG signal. The
QRS complex, formed by Q, R and S waves,
represents a relevant wave form because the heart rate
can be identified locating two successive QRS
complex.
Figure 1 Typical waveform of ECG signal.
Frequency values of an ECG signal vary
from 0 Hz to 100 Hz [3] whereas the associated
amplitude values vary from 0.02 mV to 5 mV.ECG
signals are very weak in amplitude and low in
frequency. Hence they are susceptible to two major
types of noises generated by biological and
environmental resources. Biological sources of noise
causes base line drift and amplitude modulation of
ECG signal. Environmental sources of noise may
cause PLI, instrumentation noise generated by
electronic devices. For removing these unwanted noise
added to the ECG signals, digital filtering is done. The
computational requirement is a major factor which
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must be considered in practical applications. Digital
filtering techniques are more flexible as they are built
using software and can be changed at any design
stage, which is not possible with their analog
counterpart.
Many of the researchers have worked on the
methods to reduce the interferences. The use of digital
filters in the analysis and filtration of ECG signals is
being recommended [6]. The IIR filters were used for
reducing the noise in the ECG signals.IIR filters
presents a good solution to filtering the ECG for noise
reduction. High pass, low pass and band stop filters
were used to reduce all the noise below 0.5Hz, above
100Hz, and power line interference at 50Hz,
respectively. An efficient FFT method to estimate PLI
in ECG is also being recommended [7]. Power
spectrum of ECG signals can also e calculated by
other conventional methods also. It is important to
measure the power spectrum of ECG signals in order
to estimate the noise level being present at each and
every spectral component.
Parabolic filters were also being tested to
reduce the effects of 50Hz noise [8]. Parabolic filters
presents a distinct advantage in band stop filtering.
There are many factors which should be considered
while justifying the role of any filter in noise removal,
such as, stability in that particular region, phase
linearity (specially in case of IIR filters) and the
number of filter coefficients used. Digital filter
structure such as window based FIR filter is proposed
to maximally remove the noise from the ECG signal
[9]. FIR filters presents an additional advantage of
phase linearity in the region of interest, while, it
presents the problem of increased number of filter
coefficients, which in turn increases the hardware.
The use of ICA theory is employed to
minimize the effect of PLI [10]. Power line
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interference occurs due to the presence of ac power
lines near the measurement site. Due to these power
lines, the 50Hz humming noise get added to the
measured ECG signal. This causes the problem in
measuring the information from the observed ECG.
Experiments on accuracy of 50 Hz interference
subtraction from an electrocardiogram were also being
conducted [11].
A simple self tuned notch filters were
considered for removing interference[12]. Notch
filters presents a very high attenuation to a particular
frequency of interest. Estimation of noise in the ECG
by subtraction procedure is also being done [13].
II. IIR filters
A digital filter is a mathematical algorithm
implemented in hardware/software that operates on a
digital input signal to produce a digital output signal
for the purpose of achieving a filtering objective. The
term digital filter used in this paper refers to the
software filter. Digital filters play a crucial role in
digital signal processing, e.g., biomedical signal
processing . The transfer function for IIR filter is:
Where a and b are the coefficients of the filter. Order
of the filter is M.
The first step in ECG analysis is to record the
electrical activity of the heart. This is done by putting
electrodes on the surface of the body.ECG amplifiers
are used for real time applications. The flow chart for
the step by step by procedure involved in the proposed
digital filtering is given below.
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START
Read ECG signal and add simulated noise to it
Plot the signal in time domain
Choose design parameter of filter for noise reduction
Design Butterworth & Chebyshev type1 digital IIR filter
Filter the noisy ECG signal with the proposed filter
Calculate the PSD of the filtered data with the periodogram method using MATLAB
END
Figure 2 Flowchart for implementation procedures.
III. Proposed IIR Filter Design and
Simulation
Digital IIR filters can be designed to be high
pass, low pass, band pass or band stop. The band stop
filter can be modified to work as a notch filter, which
of our concern in this paper. Digital IIR filters are
being designed from its analog counterpart by various
methods like impulse invariant, matched z-transform
or bilinear z-transform. Analog counterparts of digital
IIR filters are butterworth, chebyshev1and
chebyshev2. We will design the digital IIR filter with
butterworth and chebyshev1 filters and present a
comparative analysis of the both
A 50Hz butterworth notch filter is designed
for power line interference reduction. Figure 3 shows
that magnitude response is flat in the pass band.
Figure 4 shows that phase response is linear in the
pass band, thus making it suitable for the real time
applications. Figure 5 shows the pole zero plot of the
system. It is clear from the figure 5 that system is
stable as no poles are outside the unit circle.
Figure 3 Magnitude response of butterworth notch filter.
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Figure 4 Phase response of butterworth notch filter.
Figure 5 Pole zero plot of butterworth notch filter.
Next, a chebyshev type 1 bandstop filter is
designed for power line interference removal. Figure 6
shows that magnitude response is flat in the pass band.
Figure 7 shows that phase response is linear
in the pass band, thus making it suitable for the real
time applications. Figure 8 shows the pole zero plot of
the system. It is clear from the figure 5 that system is
stable as no poles are outside the unit circle.
Figure 6 Magnitude response of chebyshev type 1 filter.
Figure 7 Phase response of chebyshev type 1 filter.
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Figure 8 Pole zero plot of chebyshev type 1 filter.
The chebyshev filter is designed so as to stop
the interference coming from the power line. Usually
the noise from power line is not exactly concentrated
at 50Hz. It is centered around 50 Hz. So, a bandstop
filter presents additional advantage.
IV. Results & Discussions
ECG signal before filtration is shown by
figure 9. This ECG signal is corrupted by noise.
Figure 10 shows filtered ECG signal. The filtration is
done using butterworth digital IIR filter for removing
50Hz noise.
Figure 9 ECG signal before filtration.
Figure 10 ECG signal after butterworth filtering.
The power spectral density of the noisy ECG
signal is shown by figure 11. Figure 12 shows the
power spectral density of filtered ECG signal. At the
frequency of 50Hz, frequency spectrum before
filtration shows a signal power of -80dB. After
application of digital butterworth filtering, frequency
spectrum shows a signal power of -108 dB. This result
clearly exhibits the utility of the proposed butterworth
filter in reducing PLI at 50Hz.
Figure 11 Power spectral density of noisy ECG signal.
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Figure 12 Power spectral density of butterworth filtered ECG signal.
ECG signal before filtration is show by figure
13. This ECG signal is corrupted by noise and
contains noise at various spectral components. Figure
14 shows filtered ECG signal. The filtration is done
using Chebyshev type 1 IIR filter for removing 50Hz
noise. The filter is bandstop type so as to stop the
noise centered around 50Hz.
Figure 13 Noisy ECG signal.
Figure 14 ECG signal after Chebyshev type 1 filtering.
The power spectral density of the noisy ECG
signal is shown in figure 15. Figure 16 shows the
power spectral density of filtered ECG signal. At the
frequency of 50Hz , frequency spectrum before
filtration shows a signal power of -82dB. After
application of digital Chebyshev type 1 filtering,
frequency spectrum shows a signal power of -100 dB.
This result clearly exhibits the utility of the proposed
filter in reducing PLI at 50Hz.
Figure 15 Power spectral density of noisy ECG signal.
Figure 16 Power spectral density of ECG signal after Chebyshev type 1 filtering.
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The comparative analysis of the proposed
digital IIR filtering methods for PLI removal in ECG
signals is shown in table 1. It is clear from the table
that digital Butterworth filter presents a larger
attenuation to the interference signals at 50Hz. Thus, it
finds more suitability for removing 50Hz power line
interference.
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Table 1 Comparative analysis of the two proposed IIR
filters.
Filter type
Before
After
Attenuation
filtration
filtration
at 50Hz
Power at Power
at
50Hz (dB) 50Hz (dB)
Butterworth -80
-108
28dB
Chebyshev
-82
-100
18dB
type 1
The combined power spectrum density of
ECG signal is shown by figure 17, before and
filtration. The filter used is Butterworth IIR filter. It is
clear from the figure that noise is removed up to a
satisfactory extent. Figure 18 shows the combined
power spectrum density of signals, before and
filtration using digital Chebyshev type 1 filter.
Figure 17 PSD after and before butterworth filtering.
Figure 18 PSD after and before chebyshev type 1 filtering.
V. Conclusion
The proposed method provides an easy and
less complex tool for reducing 50Hz PLI removal. The
ECG is the most commonly studied potential
interface, mainly due to its fine temporal resolution,
ease of use, portability and low set-up cost. IIR filters
provides a simple way to reduce the interference due
to power line without losing any relevant data. The
comparative analysis clearly shows the utility of
Butterworth filter in PLI removal. Attenuation of 28
dB is offered at 50Hz by filtering with IIR
Butterworth filter, which is fairly a large value and is
suitable to stop noise at 50Hz. Chebyshev type 1
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bandstop filter offers 18dB of attenuation at 50Hz. But
this filter finds more suitability when the noise due to
power line is present not only at 50Hz but it is
centered around it. In the latter case, it is clearly
visible from figure 18, Chebyshev type 1 bandstop
filter is offering large attenuation to frequencies
around 50Hz,
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ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.1309-1316
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