The document discusses removing noise from ECG signals using adaptive filtering techniques. It focuses on using an LMS algorithm to remove powerline interference at 50 Hz from ECG signals. The LMS algorithm is tested with different filter tap lengths and step sizes to determine the optimal parameters for noise cancellation. Additional filtering using notch filters is also explored to remove harmonics and high frequency noise. The results show that the LMS algorithm effectively removes powerline interference from ECG signals.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.
Novel method to find the parameter for noise removal from multi channel ecg w...eSAT Journals
Abstract In general, electrocardiogram (ECG) waveforms are affected by noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist. It is very difficult to remove the noise from 12 channel ECG waveforms using standard noise removal methodologies. Removal of the noise from ECG waveforms is majorly classified into two types in signal processing namely Digital filters and Analog filters. Digital filters are more accurate than analog filters because analog filters introduce nonlinear phase shift. Most advanced research digital filters are FIR and IIR.FIR filters are stable as they have non-recursive structure. They give the exact linear phase and efficiently realizable in hardware. The filter response is finite duration. Thus noise removal using FIR digital filter is better option in comparison with IIR digital filter. But it is very difficult to find the cut-off frequency parameter for dynamic multi-channel ECG waveforms using existing traditional methods. So, in this research, newly introduced Multi-Swarm Optimization (MSO) methodology for automatically identifying the cut-off frequency parameter of multichannel ECG waveforms for low-pass filtering is inspecting. Generally, the spectrums of the ECG waveforms are extracted from four classes: normal sinus rhythm, atria fibrillation, arrhythmia and supraventricular. Baseline wander is removed using the Moving Median Filter. A dataset of the extracted features of the ECG spectrums is used to train the MSO. The performance of the MSO with various parameters is investigated. Finally, the MSO-identified cut-off frequency parameter, it’s applied to a Finite Impulse Response (FIR) filter. The resulting signal is evaluated against the original clean and conventional filtered ECG signal. Keywords: 12 Channel ECG Waveforms, Multi Swarm Optimization Neural Network, Low-pass filtering, Finite Impulse Response (FIR).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Computer Based Model to Filter Real Time Acquired Human Carotid PulseCSCJournals
Acquisition and reliable parameter extraction of human bio-signals is extremely sensitive to interferences. Analogue filters have been used in the past to remove artefacts but they only help to suppress these contaminations and are difficult to realize. Their simulated computer based digital equivalents are however a viable and effective solution to this. But these filters are mostly tested on stored database and so there is a need to develop a system where real time acquired human signal is filtered and analyzed online on a computer. Present paper deals with design and simulation of digital techniques like FIR filter, IIR notch filter, spectrum analysis and convolution implemented on real time, non-invasively acquired carotid pulse wave on a computer based environment named Simulink using FDA tool. Results obtained show the accuracy of the designed techniques and gives an easy model to online acquire and filter bio-signals sitting at home.
Recognition of Epilepsy from Non Seizure Electroencephalogram using combinati...Atrija Singh
IC3: International IEEE Conference on Contemporary Computing
Noida India
Presented on 10th August 2017.
Topic : Recognition of Epilepsy from Non Seizure Electroencephalogram using combination of Linear SVM and Time Domain Attributes.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.
Novel method to find the parameter for noise removal from multi channel ecg w...eSAT Journals
Abstract In general, electrocardiogram (ECG) waveforms are affected by noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist. It is very difficult to remove the noise from 12 channel ECG waveforms using standard noise removal methodologies. Removal of the noise from ECG waveforms is majorly classified into two types in signal processing namely Digital filters and Analog filters. Digital filters are more accurate than analog filters because analog filters introduce nonlinear phase shift. Most advanced research digital filters are FIR and IIR.FIR filters are stable as they have non-recursive structure. They give the exact linear phase and efficiently realizable in hardware. The filter response is finite duration. Thus noise removal using FIR digital filter is better option in comparison with IIR digital filter. But it is very difficult to find the cut-off frequency parameter for dynamic multi-channel ECG waveforms using existing traditional methods. So, in this research, newly introduced Multi-Swarm Optimization (MSO) methodology for automatically identifying the cut-off frequency parameter of multichannel ECG waveforms for low-pass filtering is inspecting. Generally, the spectrums of the ECG waveforms are extracted from four classes: normal sinus rhythm, atria fibrillation, arrhythmia and supraventricular. Baseline wander is removed using the Moving Median Filter. A dataset of the extracted features of the ECG spectrums is used to train the MSO. The performance of the MSO with various parameters is investigated. Finally, the MSO-identified cut-off frequency parameter, it’s applied to a Finite Impulse Response (FIR) filter. The resulting signal is evaluated against the original clean and conventional filtered ECG signal. Keywords: 12 Channel ECG Waveforms, Multi Swarm Optimization Neural Network, Low-pass filtering, Finite Impulse Response (FIR).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Computer Based Model to Filter Real Time Acquired Human Carotid PulseCSCJournals
Acquisition and reliable parameter extraction of human bio-signals is extremely sensitive to interferences. Analogue filters have been used in the past to remove artefacts but they only help to suppress these contaminations and are difficult to realize. Their simulated computer based digital equivalents are however a viable and effective solution to this. But these filters are mostly tested on stored database and so there is a need to develop a system where real time acquired human signal is filtered and analyzed online on a computer. Present paper deals with design and simulation of digital techniques like FIR filter, IIR notch filter, spectrum analysis and convolution implemented on real time, non-invasively acquired carotid pulse wave on a computer based environment named Simulink using FDA tool. Results obtained show the accuracy of the designed techniques and gives an easy model to online acquire and filter bio-signals sitting at home.
Recognition of Epilepsy from Non Seizure Electroencephalogram using combinati...Atrija Singh
IC3: International IEEE Conference on Contemporary Computing
Noida India
Presented on 10th August 2017.
Topic : Recognition of Epilepsy from Non Seizure Electroencephalogram using combination of Linear SVM and Time Domain Attributes.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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.
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.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...Subhajit Sahu
With biomedical signal processing algorithms, such as the Pan-Tompkins QRS peak detection algorithm, FIR filters are utilized. Raw ECG signal can be fed to a Moving window filter, which helps filter out noise and get the signal of interest. These FIR filters involve the use of multipliers and adders, which take in several input sample and output a single sample.
This paper replaces accurate adders in such filters with 10 16-bit signed approximate adders (power ve error parameters) from the EvoApprox library. Functional validation is done in MATLAB with Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of Moving Window Integration; and
Mean Square Error (MSE) of thresholds as error metrics. MIT-BIH Arrythmia database is used as the raw ECG input.
In hardware evaluation, a 100-point FIR filter is implemented with a single Multiply-accumulate unit where the exact adder is replaced with selected approximate adder. RTL model is synthesized with 45nm NandGate Open Cell library in Synopsys design compiler. Area, Average power, and Worst-case delay are measured.
On average the presented methodology provides an area-saving of 19.71% and power-saving of 19.27%.
⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BC...Victor Asanza
Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería...Victor Asanza
EEG SIGNALS
Data Set
Methodology and Results
Analysis of Results
Trabajos en cursor
Resultados Obtenidos
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ #BCI System using a Novel Processing Technique Based on Electrodes Sele...Victor Asanza
This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Performance Study of Various Adaptive filter algorithms for Noise Cancellatio...CSCJournals
Removal of noises from respiratory signal is a classical problem. In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the respiratory and other biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. This paper focuses on (i) Model Respiratory signal with second order Auto Regressive process. Then randomly generated noises have been mixed with respiratory signal and nullify these noises using various adaptive filter algorithms (ii) to remove motion artifacts and 50Hz Power line interference from sinusoidal 0.18Hz respiratory signal using various adaptive filter algorithms. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Noise Cancellation in ECG Signals using ComputationallyCSCJournals
Several signed LMS based adaptive filters, which are computationally superior having multiplier free weight update loops are proposed for noise cancellation in the ECG signal. The adaptive filters essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the signed regressor LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign-sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of signal to noise ratio and computational complexity.
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
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.
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.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...Subhajit Sahu
With biomedical signal processing algorithms, such as the Pan-Tompkins QRS peak detection algorithm, FIR filters are utilized. Raw ECG signal can be fed to a Moving window filter, which helps filter out noise and get the signal of interest. These FIR filters involve the use of multipliers and adders, which take in several input sample and output a single sample.
This paper replaces accurate adders in such filters with 10 16-bit signed approximate adders (power ve error parameters) from the EvoApprox library. Functional validation is done in MATLAB with Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of Moving Window Integration; and
Mean Square Error (MSE) of thresholds as error metrics. MIT-BIH Arrythmia database is used as the raw ECG input.
In hardware evaluation, a 100-point FIR filter is implemented with a single Multiply-accumulate unit where the exact adder is replaced with selected approximate adder. RTL model is synthesized with 45nm NandGate Open Cell library in Synopsys design compiler. Area, Average power, and Worst-case delay are measured.
On average the presented methodology provides an area-saving of 19.71% and power-saving of 19.27%.
⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BC...Victor Asanza
Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería...Victor Asanza
EEG SIGNALS
Data Set
Methodology and Results
Analysis of Results
Trabajos en cursor
Resultados Obtenidos
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ #BCI System using a Novel Processing Technique Based on Electrodes Sele...Victor Asanza
This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Performance Study of Various Adaptive filter algorithms for Noise Cancellatio...CSCJournals
Removal of noises from respiratory signal is a classical problem. In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the respiratory and other biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. This paper focuses on (i) Model Respiratory signal with second order Auto Regressive process. Then randomly generated noises have been mixed with respiratory signal and nullify these noises using various adaptive filter algorithms (ii) to remove motion artifacts and 50Hz Power line interference from sinusoidal 0.18Hz respiratory signal using various adaptive filter algorithms. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Noise Cancellation in ECG Signals using ComputationallyCSCJournals
Several signed LMS based adaptive filters, which are computationally superior having multiplier free weight update loops are proposed for noise cancellation in the ECG signal. The adaptive filters essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the signed regressor LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign-sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of signal to noise ratio and computational complexity.
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
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.
Differential equation fault location algorithm with harmonic effects in power...TELKOMNIKA JOURNAL
About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
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.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Day by day the scope & use of the electronics concepts in bio-medical field is going to increase step by step. Electrocardiogram (ECG) is basically a non-invasive way of measuring the electrical activity of the heart by registering the extracellular potentials generated by it. The ECG signal consists of low amplitude voltage in the presence of high amplitude offset. A power-efficient ECG acquisition system uses a fully digital architecture helps to reduce the power consumption and delay time. Instead of analog block, they convert the input voltage into a digital code by delay lines and are mainly built on digital blocks This digital architecture is capable of operating with a low supply voltage of 0.5 V. The circuit implemented in 90nm CMOS technology. The simulation results show that the DCC circuit of digital architecture consumes 0.42nW of power.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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1. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
Noise Analysis & QRS Detection in ECG Signals
International Proceedings of Computer Science and Information Technology.
Abstract:
With the latest advancements in electronics, several techniques are used for removal of unwanted
entities from signals especially that are implied in the most sophisticated applications. The removal of
power line interference from most sensitive medical monitoring equipment can also be removed by
implementing various useful techniques. The power line interference (50/60 Hz) is the main source of
noise in most of bio-electric signals. The thesis report presents the removal of power line interference
and other single frequency tones from ECG signal using the advanced adaptive filtering technique with
LMS (least mean square) algorithm. The thesis is based on digital signal processing (DSP) techniques with
MATLAB package, with the emphases on design of adaptive LMS algorithm. The adaptive interference
removal technique can be used for removal of power line interference in various potential applications
such as recording Electrocardiograms (ECG), Electroencephalogram (EEG) and Electromyogram (EMG).
MATLAB package will be used in the thesis work which is a powerful tool for the interactive design in
most of the scientific applications and complex engineering calculations.
As an additional in order to achieve the goal of thesis it will also be investigated and implemented for
the removal of harmonics (hum) and high frequency noise from ECG signal by using general notch
rejection filters, which are partial milestone for the thesis.
2. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
A: - Removal of Power Line Interference (50 Hz) from ECG Signal by LMS Algorithm
FilterTap = 16
mu = 0.005
3. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
FilterTap = 16
mu = 0.009
4. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
FilterTap = 32
mu = 0.005
5. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
FilterTap = 32
mu = 0.009
6. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
B: - Removing of Harmonics and High Frequency Noise from Original ECG Signal
Conclusions:
This report is devoted to the problems and solutions on removal of Power Line Interference and other
Single Frequency Tones from Signals. The understanding of noise cancellation from ECG signal was
explained clearly to the readers, the methods and techniques applicable to be used discussed
throughout the report. It has been proposed a solution for the power line interference its respective
harmonics and high frequency noise interferences from original ECG signal. The results have been
obtained which were required in purpose statement of the report.
In general FIR filters are used because these types of filters have simple architecture and are logically
stable, so the FIR filter was chosen for the development of the system. The research has been made for
the selection of filters and algorithm, two kind of adaptive filters i.e. FIR (Finite impulse response) & IIR
(Infinite impulse response) and two kinds of algorithms, the LMS (least mean square) & RLS (recursive
least squares) algorithms were studied and examined. The LMS algorithm is the most widely used
adaptive filtering algorithm in biomedical monitoring equipment, so it was decided to be employed for
the thesis to get the required solution for the main purpose. The applications of the LMS algorithm can
be implemented due to its simplicity and robustness.
An adaptive filter is used in applications that require different filter characteristics in response to
variable signal conditions. The speed of adaptation and accuracy of the noise cancellation after
adaptation are important measures of performance for noise cancellation algorithm. The goal of the
adaptive filter is to match the filter coefficients to the noise so that the adaptive filter can subtract the
noise out from signal. So adaptive filtering technique was selected to achieve the goal of thesis.
The test for the simulation of ECG signal has been taken. The signal is corrupted by power line
interference of 50 Hz. It is observed that the frequency of the power line interference is 50 Hz which is
then mixed with original ECG signal, it is also examined that the mixed signal is displayed on the plot.
After passing through LMS algorithm the filtered output is nearly same as the input signal with some
acceptable distortion range. The value of step size μ play an important role in determining the
convergence speed, stability and residual error after convergence. The convergence rate was controlled
7. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
by Removal of Power Line Interference and other Single Frequency Tones from Signals MSc Thesis
Report, Mälardalen University, Sweden
56
LMS step size μ. The ECG signal graphs described in the simulation results verify the adaptation of the
LMS adaptive algorithm by changing various parameters like step size, convergence value (μ) and filter
taps have various effects on the output graphs. The result shows that LMS is an effective algorithm used
for the adaptive filter in the noise cancellation implementation.
By increasing the filter order it shows a convergence rate but makes the results more precise and by
decreasing the step size value it creates the slower convergence but improves the stability and accuracy.
The recovered signal closely resembles to the original simulated signal minus the noise. It can be seen
that the implementation of the algorithm functions as correctly and efficiently. By comparing the graphs
of the input signal of ECG and output signal, it is noticed that the simulation program performs
satisfactorily and that noise cancellation from original ECG signal is acquired. The overall performance of
LMS algorithm for power line interference is achieved.
Furthermore the general notch rejection filters method also performs the correct operation while
filtering the noise from original ECG signal. This technique for the investigation, implemented and
analysis of removal of harmonics and high frequency noise from original ECG signal performed
satisfactory. It is concluded that the low frequency noise (hum) and high frequency noise can be
removed from original ECG signal by the implementation of general notch rejection filters method and
the desired result can be achieved accurately.
Future Enhancements:
The depth knowledge achieved is in a number of aspects by using digital signal processing techniques
with MATLAB package for medical monitoring equipment (ECG). It provides the real concepts along with
the theoretical backgrounds of removal of power line interference, single frequency tones and high
frequency noise from original ECG signal. This enhances the understanding and self-confidence in the
field of electronics and biomedical engineering.
In the thesis, the adaptive signal processing filtering technique based on LMS algorithm could be
implemented for more signals and also improvement of the thesis can be further implemented with
different algorithms such as NLMS and RLS to achieve the desired results. It could also be investigate
and implement the removal of multiple of harmonics from ECG signal. In further research it would be of
interest to make a broader study and look at some companies for related project for the
implementation.
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8. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
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10. Base paper: - http://www.ipcsit.com/vol47/027-ICCTS2012-T30009.pdf
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