The document describes a portable device developed for real-time ECG signal analysis and detection of cardiac diseases like atrial fibrillation and myocardial ischemia. The device uses an ARM processor and simplified analog front-end to process ECG signals in real-time. Features are extracted from preprocessed ECG data and a support vector machine classifier detects cardiac diseases with 95.1% sensitivity and 95.5% specificity. The portable device allows for continuous monitoring and early detection of cardiac issues.
Portable ECG Monitoring System using Lilypad And Mobile Platform-PandaBoardIJSRD
New wireless system for biomedical purposes gives new possibilities for monitoring of essential function in human being. Wearable biomedical sensors will give the patient the freedom to be capable of moving readily and still be under continuously monitoring regularity of heartbeats identify any damage to the heart and devices used to regulate the heart and thereby to better quality of patient care. This paper describes a new concept for wireless and portable electrocardiogram (ECG) sensor transmitting signals to a monitoring station at the remote location within specific range, and this concept is intended for monitoring people with impairments in their cardiac activity. The proposed work helps to overcome this problem. With the advancement in Arduino and mobile technology, it is possible to design a portable ECG device which capture ECG of patient and monitor it on mobile platform. This report goes over low power Arduino, mobile platform Panda board and Zigbee technology to couple ECG over mobile board.
Portable ECG Monitoring System using Lilypad And Mobile Platform-PandaBoardIJSRD
New wireless system for biomedical purposes gives new possibilities for monitoring of essential function in human being. Wearable biomedical sensors will give the patient the freedom to be capable of moving readily and still be under continuously monitoring regularity of heartbeats identify any damage to the heart and devices used to regulate the heart and thereby to better quality of patient care. This paper describes a new concept for wireless and portable electrocardiogram (ECG) sensor transmitting signals to a monitoring station at the remote location within specific range, and this concept is intended for monitoring people with impairments in their cardiac activity. The proposed work helps to overcome this problem. With the advancement in Arduino and mobile technology, it is possible to design a portable ECG device which capture ECG of patient and monitor it on mobile platform. This report goes over low power Arduino, mobile platform Panda board and Zigbee technology to couple ECG over mobile board.
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.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Acquiring Ecg Signals And Analysing For Different Heart AilmentsIJERA Editor
This paper describes and focuses on acquiring and identification of cardiac diseases using ECG waveform in LabVIEW software, which would bridge the gap between engineers and medical physicians. This model work collects the waveform of an affected person. The waveform is analyzed for diseases and then a report is sent to the doctor through mail. Initially the waveforms are collected from the person using EKG sensor with the help of surface electrodes and the hardware controlled by MCU C8051, acquires ECG and also Phonocardiogram (PCG) synchronously and the waveform is sent to the PC installed with LabVIEW software through DAQ-6211. The waveform in digital format is saved and sent to the loops containing conditions for different diseases. If the waveform parameters coincide with any of the looping statements, particular disease is indicated. Simultaneously the patient PCG report is also collected in a separate database containing all information, which will be sent to the doctor through mail.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Instant elelectrocardiogram monitoring in android smart phonesIjrdt Journal
ECG (electrocardiogram) is very essential component for the doctors to diagnose the state of patient’s cardiovascular system. In critical situations doctors may need to examine ECG of patient instantly to take a firm and better decision in their absence near patient. In this paper a better way of instant ECG datatransfer, processing and display is demonstrated. Here ECG is acquired using simple 3 electrode single lead configuration then it is digitized and transmitted to Android smart phone in SMS message format. This SMS data is a bundle of values representing digital ECG. Acquired SMS data is fetched from inbox of the phone and processed for calculation of heart rate and detection of arrhythmia by Android application software. Then ECG is displayed on phone screen along with conclusion of heart rate and arrhythmia (if any).
Real Time Signal Quality Aware Internet of Things IOT Framework for FPGA Base...ijtsrd
Day by day the scope and use of the electronics concepts in bio-medical field is increasing gradually. A novel approach to the design of real time ECG signal acquisition system for patient monitoring in medical application, FPGA Field Programmable Gate Array is the core heart of proposed system which is configured and programmed to acquire using ECG Electrocardiogram sensor. In this paper a new concept of ECG telemetry system is discussed along with signal quality aware IoT framework for energy efficient ECG monitoring system. Tele monitoring is a medical practice that involves monitoring patients who are not at the same location as the healthcare provider. The purpose of the present study is use to identify heart condition and give the information to the doctor. The objective of the study is to improve the doctor-patient ratio and evaluation of cardiac diseases in the rural population. The proposed system for the electrocardiogram ECG monitoring controlled by FPGA and implemented in the form of android application. Dhanashri P. Yamagekar | Dr. P. C. Bhaskar "Real Time Signal Quality Aware Internet of Things (IOT) Framework for FPGA Based ECG Telemetry System and Development of Android Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18938.pdf
http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18938/real-time-signal-quality-aware-internet-of-things-iot-framework-for-fpga-based-ecg-telemetry-system-and-development-of-android-application/dhanashri-p-yamagekar
ECG SIGNAL ACQUISITION, FEATURE EXTRACTION AND HRV ANALYSIS USING BIOMEDICAL ...IAEME Publication
This Paper contains the complete process of ECG/EKG signal Acquisition from
hardware to its analysis using LabVIEW and Biomedical Workbench. Hardware of ECG
has the amplification, filtering and conversion of analog ECG data to digital by using
Arduino Uno. The acquisition part deal with acquiring the hardware data to analyzable
file format into pc. Here 6-channel ADC in Arduino Uno with LabVIEW interface is used
for conversion. Now the acquired ECG data is processed and analyzed with biomedical
workbench that provides the various features of ECG signal processing. This system is
very easy to implement and cost effective
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition,and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes.
A Real Time Electrocardiogram (ECG) Device for Cardiac PatientsIJERD Editor
Now-a-days due to rising stress levels, change in lifestyles and a variety of different issues, the number of people suffering from heart related diseases is increasing. This number would significantly rise in the next few years. As the technology enhanced, a significant paradigm shift has been observed in the biomedical industry. To tackle the heart related issues, technology can be introduced in one’s life. This paper proposes a wireless, wearable ECG device capable of processing the patient’s ECG in a real time environment. It is capable of comparing the ECG with threshold parameters, and if ECG of the patient is not in the range of the threshold values, the device notifies the cardiac patient’s mobile phone by sending a Multimedia Messaging Service (MMS) of the changed ECG and, in turn the patient’s mobile phone sends this changed ECG image to the mobile phone present at the hospital.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
Electrocardiogram signal processing algorithm on microcontroller using wavele...IJECEIAES
The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
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.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Acquiring Ecg Signals And Analysing For Different Heart AilmentsIJERA Editor
This paper describes and focuses on acquiring and identification of cardiac diseases using ECG waveform in LabVIEW software, which would bridge the gap between engineers and medical physicians. This model work collects the waveform of an affected person. The waveform is analyzed for diseases and then a report is sent to the doctor through mail. Initially the waveforms are collected from the person using EKG sensor with the help of surface electrodes and the hardware controlled by MCU C8051, acquires ECG and also Phonocardiogram (PCG) synchronously and the waveform is sent to the PC installed with LabVIEW software through DAQ-6211. The waveform in digital format is saved and sent to the loops containing conditions for different diseases. If the waveform parameters coincide with any of the looping statements, particular disease is indicated. Simultaneously the patient PCG report is also collected in a separate database containing all information, which will be sent to the doctor through mail.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Instant elelectrocardiogram monitoring in android smart phonesIjrdt Journal
ECG (electrocardiogram) is very essential component for the doctors to diagnose the state of patient’s cardiovascular system. In critical situations doctors may need to examine ECG of patient instantly to take a firm and better decision in their absence near patient. In this paper a better way of instant ECG datatransfer, processing and display is demonstrated. Here ECG is acquired using simple 3 electrode single lead configuration then it is digitized and transmitted to Android smart phone in SMS message format. This SMS data is a bundle of values representing digital ECG. Acquired SMS data is fetched from inbox of the phone and processed for calculation of heart rate and detection of arrhythmia by Android application software. Then ECG is displayed on phone screen along with conclusion of heart rate and arrhythmia (if any).
Real Time Signal Quality Aware Internet of Things IOT Framework for FPGA Base...ijtsrd
Day by day the scope and use of the electronics concepts in bio-medical field is increasing gradually. A novel approach to the design of real time ECG signal acquisition system for patient monitoring in medical application, FPGA Field Programmable Gate Array is the core heart of proposed system which is configured and programmed to acquire using ECG Electrocardiogram sensor. In this paper a new concept of ECG telemetry system is discussed along with signal quality aware IoT framework for energy efficient ECG monitoring system. Tele monitoring is a medical practice that involves monitoring patients who are not at the same location as the healthcare provider. The purpose of the present study is use to identify heart condition and give the information to the doctor. The objective of the study is to improve the doctor-patient ratio and evaluation of cardiac diseases in the rural population. The proposed system for the electrocardiogram ECG monitoring controlled by FPGA and implemented in the form of android application. Dhanashri P. Yamagekar | Dr. P. C. Bhaskar "Real Time Signal Quality Aware Internet of Things (IOT) Framework for FPGA Based ECG Telemetry System and Development of Android Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18938.pdf
http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18938/real-time-signal-quality-aware-internet-of-things-iot-framework-for-fpga-based-ecg-telemetry-system-and-development-of-android-application/dhanashri-p-yamagekar
ECG SIGNAL ACQUISITION, FEATURE EXTRACTION AND HRV ANALYSIS USING BIOMEDICAL ...IAEME Publication
This Paper contains the complete process of ECG/EKG signal Acquisition from
hardware to its analysis using LabVIEW and Biomedical Workbench. Hardware of ECG
has the amplification, filtering and conversion of analog ECG data to digital by using
Arduino Uno. The acquisition part deal with acquiring the hardware data to analyzable
file format into pc. Here 6-channel ADC in Arduino Uno with LabVIEW interface is used
for conversion. Now the acquired ECG data is processed and analyzed with biomedical
workbench that provides the various features of ECG signal processing. This system is
very easy to implement and cost effective
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition,and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes.
A Real Time Electrocardiogram (ECG) Device for Cardiac PatientsIJERD Editor
Now-a-days due to rising stress levels, change in lifestyles and a variety of different issues, the number of people suffering from heart related diseases is increasing. This number would significantly rise in the next few years. As the technology enhanced, a significant paradigm shift has been observed in the biomedical industry. To tackle the heart related issues, technology can be introduced in one’s life. This paper proposes a wireless, wearable ECG device capable of processing the patient’s ECG in a real time environment. It is capable of comparing the ECG with threshold parameters, and if ECG of the patient is not in the range of the threshold values, the device notifies the cardiac patient’s mobile phone by sending a Multimedia Messaging Service (MMS) of the changed ECG and, in turn the patient’s mobile phone sends this changed ECG image to the mobile phone present at the hospital.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
Electrocardiogram signal processing algorithm on microcontroller using wavele...IJECEIAES
The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for
cardiovascular diseases. A cleaned ECG signal provides necessary information about the
electrophysiology of the heart diseases and ischemic changes that may occur. It provides
valuable information about the functional aspects of the heart and cardiovascular system. The
objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in
this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the
ECG signal consists of following stages: detection of QRS complex in ECG signal; feature
extraction from detected QRS complexes; classification of beats using extracted feature set
from QRS complexes. In turn automatic classification of heartbeats represents the automatic
detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the
automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG
signal.QRS complex detection is the first step towards automatic detection of cardiac
arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG
signal peak classification approach is used in ECG signal for determining various diseases . As
known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of
heart of human. Therefore duration and amplitude of all peaks are found. R-R and P-R
intervals are calculated. Finally, we have obtained the necessary information for disease
detection .For detection of cardiac arrhythmias; the extracted features in the ECG signal will
be input to the classifier. The extracted features contain morphological l features of each
heartbeat in the ECG signal. This project is implemented by using MATLAB software. An
interface was created to easily select and process the signal. “.dat” format is used the for ECG
signal data. We have detected bradycardia and tachycardia. Massachusetts Institute of
Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for
performance analysis.
A Wireless Methodology of Heart Attack Detectionijsrd.com
The wrist watch with Heart Attack Detection is equipment that is used daily to indicate heart condition, to detect heart attack and to call for emergency help. It was designed specially to help patients with heart disease.This includes three common sub units. They are Circuit, Analysis Algorithm, and Bluetooth Communication. The first one is to wear on the wrist of the patient to captures the abnormal heart beat waves from the victim and the alternate methods are installed under the stick. This project is based on the previous project “Wireless Heart Attack Detector with GPS†of Fall 2004 [1]. we consider a big improvement in reducing the complexity of the project greatly, in saving power consumption of the project to run much fewer codes and in making the project to run at a faster time. No wire is attached to the wrists. In our project, the ECG waveform is transmitted wirelessly from the wrists to the watch. This gives the user great flexibility while the program is switched on and running. User can drive safely, can use restroom easily and can function normally like without the project. Previous project had the wire connection. All the hardware on the walking watch would have been strapped to the wrists. This will not make the project functional and marketable. Our project is completely portable. Heart condition is displayed in our project. The previous project did not inform the user about his heart condition. We display the heart condition through two LEDs as low-risk (alert level between 4 and 6) and high risk (alert level between 7 and 9). The user can know their heart condition and take proper action to avoid the fatal moment. Proper action could be slowing down and taking a rest.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LE...sipij
In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best
previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100%.sensitivity while best previous work got 84.6%.
Day by day the scope & use of the electronics concepts in bio-medical field is increasing step by step. In this paper the review of newly developed concepts is done for the monitoring of the ECG signal. This paper also reviews a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node. Further the study of IoT frame work for ECG monitoring has been carried out. Ms. Dhanashri Yamagekar | Dr. Pradip Bhaskar"Real time ECG Monitoring: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7065.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7065/real-time-ecg-monitoring-a-review/ms-dhanashri-yamagekar
Evaluating ECG Capturing Using Sound-Card of PC/Laptopijics
The purpose of the Evaluating ECG capturing using sound-card of PC/Laptop is provided portable and low
cost ECG monitoring system using laptop and mobile phones. There is no need to interface microcontroller
or any other device to transmit ECG data. This research is based on hardware design,
implementation, signal capturing and Evaluation of an ECG processing and analyzing system which attend
the physicians in heart disease diagnosis. Some important modification is given in design part to avoid all
definitive ECG instrument problems faced in previous designs. Moreover, attenuate power frequency noise
and noise that produces from patient's body have required additional developments. The hardware design
has basically three units: transduction and conditioning Unit, interfacing unit and data processing unit.
The most focusing factor is the ECG signal/data transmits in laptop/PC via microphone pin. The live
simulation is possible using SOUNDSCOPE software in PC/Laptop. The software program that is written
in MATLAB and LAB-View performs data acquisition (record, stored, filtration) and several tasks such as
QRS detection, calculate heart rate.
Health Care Monitoring for the CVD Detection using Soft Computing Techniquesijfcstjournal
Now-a-days, many diseases are reducing the life time of the human. One of the major diseases is cardiovascular disease (CVD). It has become very common perhaps because of increasingly busy lifestyles.The rapid development of mobile communication technologies offers innumerable opportunities for the development of software and hardware applications for remote monitoring of cardiac disease. Compressed ECG is used for fast and efficient. Before performing the diagnosis, the compressed ECG must be decompressed for conventional ECG diagnosis algorithm. This decompression introduces unnecessary delay. In this paper, we introduce advanced data mining technique to detect cardiac abnormalities from the
compressed ECG using real time classification of CVD.When the patient affect cardiac disease, at the time hospital server can automatically inform to patient via email/SMS based on the real time CVD classification. Our proposed system initially uses the data mining technique, such as Genetic algorithm for attribute selection and Expectation Maximization based clustering. In this technique are used to identify the
disease from compressed ECG with the help of telecardiology diagnosis system
Design and implementation of portable electrocardiogram recorder with field ...IJECEIAES
The electrical activities of the heart are used to monitor cardiovascular diseases. It can be measured using electrocardiogram (ECG), a simple, painless test that can be recorded graphically. The physician, to predict the patient’s heart conditions and recommend suitable treatments, uses electrodes placed on the patient’s skin surface, to record these signals. The P, Q, R, S, T waves in the ECG signal can be used to determine the normality and abnormality of the heart's condition. The time interval differs for each cardiovascular condition of the heart. In this work, the ECG signal is acquired real-time using an intelligent sensor module, and the recorded value is processed to find the peak values. The data is sent to the web server using internet of things technology at a minimal time, where the physician can view it and proper decision can be taken. The real-time ECG data acquisition is also made using the field programmable gate array kit as it is a low cost, high-speed device and the output is viewed in the computer. The developed model is validated through MATLAB software and implemented for real-time applications.
The term Arrhythmia refers to any change from the normal sequence in the electrical impulses. It is also treated as abnormal heart rhythms or irregular heartbeats. The rate of growth of Cardiac Arrhythmia disease is very high & its effects can be observed in any age group in society. Arrhythmia detection can be done in many ways but effective & simple method for detection & diagnosis of Cardiac Arrhythmia is by doing analysis of Electrocardiogram signals from ECG sensors. ECG signal can give us the detail information of heart activities, so we can use ECG signals to detect the rhythm & behaviour of heart beats resulting into detection & diagnosis of Cardiac Arrhythmia. In this paper new & improved methodology for early Detection & Classification of Cardiac Arrhythmia has been proposed. In this paper ECG signals are captured using ECG sensors & this ECG signals are used & processed to get the required data regarding heart beats of the human being & then proposed methodology applies for Detection & Classification of Cardiac Arrhythmia. Detection of Cardiac Arrhythmia using ECG signals allows us for easy & reliable way with low cost solution to diagnose Arrhythmia in its prior early stage.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stockrebeccabio
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stock
Telegram: bmksupplier
signal: +85264872720
threema: TUD4A6YC
You can contact me on Telegram or Threema
Communicate promptly and reply
Free of customs clearance, Double Clearance 100% pass delivery to USA, Canada, Spain, Germany, Netherland, Poland, Italy, Sweden, UK, Czech Republic, Australia, Mexico, Russia, Ukraine, Kazakhstan.Door to door service
Hot Selling Organic intermediates
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
2. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 2 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
Background
Heart disease is one of the major causes of death, especially for the elderly population in
many countries. A total of 42 million out of 84 million people in North America who have
one or more cardiovascular diseases are estimated to be older than 60 years old [1]. The
existing ambulatory ECG monitoring systems take a considerable amount of time and
effort, record ECG signals in patients through long-term hospitalization, and the ECG
data have to be sent to professionals for diagnostic analysis. However, a portable ECG
device, which provides real time monitoring of heart disease, can help medical decision
making by detecting sporadic events of heart disease as early as possible. If the patient
with chronic diseases worn a ECG device without any real time monitoring function, the
primary defect of such solution is arise from lack of help when a major incident occurs
during the monitoring. The device without real time analysis recorded ECG waveform
but no immediate response is taken to help the patient. The device with real time analysis
can support medical decision with captured ECG waveform during doubtful sections of
incident as a black box. Therefore, a portable ECG device is required for monitoring and
identification of sporadic and chronic events of heart diseases.
Representative ECG signals of a normal ECG, in atrial fibrillation (AFib), and in myocar-
dial ischemia, are shown in Figure 1. AFib, which is caused by a rapid and irregular heart
beat at a rate of 400 to 600 beats per minute, is a type of arrhythmia [2-5]. AFib can be
detected by monitoring the heart beat and absence of the P wave. Myocardial ischemia,
caused by blockage of coronary arteries, reduces oxygen supply from the heart [6-9], and
can be detected by monitoring abnormal divergence in the PR and ST segments. Even
though various detection methods have been proposed for AFib and myocardial ischemia
[10-17], they can only detect a single disease. To simultaneously detect AFib and ischemia,
a compact and efficient architecture for detecting heart disease is required.
Developing a portable ECG monitoring device has been an active focus of research
(Table 1). Most of the portable ECG device have simple metal contacts that the user can
place their thumbs or other fingers on or place against bareskin, such as on the chest
[18-26]. The metal contacts are much more convenient and faster to use than adhesive
skin electrodes. In general, there are more artifact noise and artifacts called baseline wan-
der in the typical thumb contact. On the other hand, recordings using adhesive electrodes
are much cleaner, consistent and more accurate [27-31]. While most of these devices
acquire and record ECG signals, they do not provide real-time identification for analysis
of heart disease. Signal analysis of two devices is below the level that recognize existence
0 200 400 600 800 1000
0
0.5
1
1.5
2
2.5
Time (ms)
Voltage
(mV)
0 200 400 600 800 1000
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time (ms)
Voltage
(mV)
0 200 400 600 800 1000
-4.5
-4
-3.5
-3
Time (ms)
Voltage
(mV)
(a) (b) (c)
Figure 1 ECG signals. Examples of ECG signals in various cases. (a) Normal ECG, (b) irregular ECG containing
atrial fibrillation, and (c) ST segment elevation containing myocardial ischemia.
3. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 3 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
Table 1 Reviewed existing ECG devices and their properties for ECG signal analysis
Product Contact Software for ECG Support for medical analysis
iPhoneECG [18] Dry metal for fingers Measurement Professional report
Smartheart [27] Adhesive electrodes Measurement Interpretation center
CardioDefender [19] Dry metal for fingers Signal analysis Irregularity detection
ME 80 [20] Dry metal for fingers Measurement Heart rate calculation
ELI 10 mobile [28] Adhesive electrodes Measurement ECG interpretation
EPI Life [21] Dry metal for fingers Signal analysis Interpretation center
ReadMyHeart [22] Dry metal for fingers Measurement Professional report
ECG Check [23] Dry metal for fingers Measurement Heart rate calculation
Dicare-m1CP [24] Dry metal for fingers Measurement Irregularity detection
HeartCheck PEN [25] Dry metal for fingers Measurement Professional report
MD100E [29] Adhesive electrodes Measurement Professional report
PC-80 [30] Adhesive electrodes Measurement Professional report
REKA E100 [26] Dry metal for fingers Measurement ECG interpretation
EKG/ECG-80A [31] Adhesive electrodes Measurement Built-in ECG printer
and nonexistence of irregular rhythm trends [19,21]. From a supporting medical analysis
perspective, professional reports from the interpretation center are provided as medi-
cal analysis service with extra charge [18,21,22,25,27,29,30]. Simple information of heart
rate and irregularity is provided [19,20,23,24,26,28]. Thus, it is important to develop new
healthcare device to achieve meaningful monitoring and real-time alert system.
Also, several classification methods are implemented for cardiac disease detection. We
already validated that SVM has outperformed against kernel density estimation and arti-
ficial neural networks as classifier in previous work [16,17]. Principal component analysis
(PCA), Genetic algorithm (GA), rule-based methods are also adapted to detect cardiac
diseases. However, the platforms of these classifiers are limited to desktop and laptop.
Thus, these classifiers are insufficient to work in real-time on mobile and portable plat-
form [32]. In order to overcome all these weakness, this study aimed to implement a
portable real-time ECG processing device with an algorithm for detecting heart disease
based on the feature extractors reported in previous studies [16,17].
Methods
Overall framework
As shown in Figure 2, the proposed portable ECG device was designed using the following
blocks: a simplified analog front-end, an ARM processor to realize signal monitoring and
Analog Front-end Unit ARM processor Display
Power Supply
Instrumentation amplifier
Right leg driver
Bandpass filters
Li-Po battery
Protection circuit
Preprocessing
Monitoring
Diagnosis
Patient
Electrodes
Figure 2 Overall framework. Overall framework configuration of the proposed portable ECG device.
4. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 4 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
analysis, an interactive display unit, and a power source. The features of the individual
blocks are as follows below.
• Reduced hardware complexity: We aimed to develop a powerful software working
platform using an ARM processor to simplify the hardware requirements.
Consequently, portability of the device can be accomplished. The minimization
analog front-end was realized by implementing most of the analog functions
(highpass, lowpass, and notch filters) using digital filters. In addition to device
compatibility, this also helped to reduce power consumption of the device and
extended battery life.
• Real-time processing: Computational complexity is one of the major obstacles while
implementing hardware in real-time. However, to alleviate this drawback, the
proposed software was modified and implemented in an ARM processor [16,17].
Furthermore, the digital filter removes various types of noises and baseline wander
from the preprocessed ECG data and then it tends to extract and classify the features
from the filtered ECG data for analysis. The processed ECG data and results of the
analysis can be displayed using an interactive LCD display. To summarize the analysis
results, the device reports averaged feature values and detected diseases every minute.
• Simultaneous feature extraction for AFib and ischemia: We considered two distinct
diseases corresponding to atrial and ventricular activity. To simultaneously describe
heart activity, we implemented features for irregularity, shape, area, slope, and
distribution of ECG data [16,17]. Feature extraction methods to represent irregularity
were simplified without compromising detection performance. Furthermore, the
extracted features from the ECG signal were classified into AFib and myocardial
ischemia. Therefore, if target diseases are changed or added, we can easily adjust the
feature extractors and train the classifiers accordingly.
Analog front-end
ECG signals that are generated by electrical activity in the heart are a small pulse train of
which the amplitude is less than 2 mV and bandwidth ranges from 0.05 to 150 Hz. Because
the ECG signal is often corrupted by various noises originating from the body and analog
signal processing hardware, efforts have been made to capture a clean ECG signal with
error-prone analog circuits, such as amplifiers and filters [33-35]. In the current study,
analog signal processing was minimized by realizing most of the signal processing in the
software using a cheap and general purpose ARM processor to enable a small system for
portability and reconfigurable for use in various conditions. The analog front-end has
two major functions (1) amplifying the ECG signal to be sampled and quantized properly
by an analog-to-digital converter (ADC), and (2) attenuating high-frequency noises that
can corrupt the sampled ECG signal because of an aliasing effect. For the sake of cost
reduction and simplicity of hardware, an ADC embedded in the ARM processor was used
instead of using an additional high-performance ADC. An ADC running at a sampling
frequency (FS) of 1 kHz has a 12-bit resolution with a reference voltage (VREF) of 1.8 V.
Figure 3 shows a block diagram of the analog front-end. The analog front-end contains
an amplifier with a first-order high-pass filter for DC or offset rejection, two gain stages
with a level shifter, and a second-order low-pass filter (LPF) as an anti-aliasing filter.
Because the peak ECG signal has an amplitude of approximately 2 mV, the required gain
5. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 5 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
Instrumentation
Amplifier
1st order
high-pass filter
2nd order
low-pass filter
(Gain stage #1)
Level Shifter
(Gain stage #2)
ADC
VREF=1.8 V
ECG
2 mV
Gain: 15 V/V Gain: 1 V/V
Corner freq.: 0.048 Hz
Gain: 10 V/V
Corner freq.: 151 Hz
Gain: 6 V/V
Figure 3 H/W block diagram. Hardware block diagram of the proposed minimized analog front-end.
is 900 (V/V), and this is implemented by spreading it over the gain stages. The amplifier
has a gain of 15 V/V and the two gain stages are 10 and 6 V/V.
ECG signal processing and learning on the ARM processor
The proposed software has two different operating modes of the training phase and test
phase. A schematic setup of the proposed software is shown in Figure 4. The software
can be divided into three functional blocks, including preprocessing, feature extraction,
and classification. At the preprocessing stage, the noise and baseline wander of the mea-
sured ECG data were removed. We simultaneously labeled the locations of the QRS
complex, P wave, and T wave by using the QRS complex detector. Later, using the labeled
QRS complex, we calculated interbeat intervals and created Poincaré plots. In the feature
extraction process, we extracted feature values for irregularity and morphological shape
from a sliding window. Eventually, at the classification block, we built a trained support
vector machine (SVM) model that could detect heart disease from the test data. Further-
more, the trained SVM model was moved to the ARM processor and operated to classify
heart disease based on the test phase. In this study, we trained our proposed system to
detect AFib based on the MIT-BIH AF, Arrhythmia, CinC 2001, and CinC 2004 databases,
and to detect myocardial ischemia using the European ST-T databases [36]. Primarily,
the training phase was conducted to train our proposed SVM model, by exploiting these
databases. Conversely, the test phase provided the analytical results of the measured ECG
signals that were acquired using the proposed electrodes.
Remove baseline wandering QRS complexes detection
Preprocessing with digital filters
Noise removal
Training set / ECG data
Feature Extraction
Training and test with classifier
Irregularity features from Poincar plot Morphological features from ECG waveform
Build a SVM model using training dataset
&
Classify the test dataset using trained SVM model
Figure 4 S/W block diagram. Block diagram of the proposed software for training and test phases.
6. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 6 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
Preprocessing with digital filters
As mentioned above, in addition to filtering provided at the analog-front end, ECG data
are processed by digital filters to further reduce the noise and interference, which are
caused by the following artifacts: contraction of skeletal muscle, fluctuation of the power
supply, and mechanical force on electrodes. The digital filters have the advantage of
being able to easily change the filter characteristics (i.e., modification of filter coeffi-
cients). Therefore, to remove accumulated noise, we combined 0.05-Hz highpass, 60-Hz
notch, and 40-Hz lowpass filters using Butterworth filters, which are a type of infinite
impulse response. Advantages are that the implemented filters require a small number of
operations per time step.
Similarly, to remove baseline wandering effects, we decomposed the signal vectors
using a wavelet operator, which has a similar shape as QRS complexes. In this step, we
used Daubechies8 as mother wavelet since Daubechies wavelet family have similar shapes
with QRS complex. Measured ECG data is decomposed into eight levels with detail
and approximation coefficients. The sequences of detail coefficients represent prominent
points and segments, and the sequences of approximation coefficients represent an unex-
pected wandering baseline. Therefore, important sequences of detail coefficients can be
retained from the input ECG data. Subsequently, to detect QRS complexes, we assigned
proper locations of QRS complexes using the wavelet scale selection method. From the
decomposed coefficients, we substituted the zero-vector to all sequences, except for one
detail coefficient sequence. By repeating this step to all acquired sequences of detail coef-
ficients, we measured the score to find the protruding segment. The QRS complex was
then assigned as QRSi by maximum protruding values for each target segment. And the
interbeat interval Ii is RR interval which is calculated from the difference of two con-
secutive QRS complexes as follows: Ii = QRSi − QRSi−1. More detailed descriptions for
detecting QRS complexes have been previously reported [17].
Feature extraction and classifications
We used sliding windows of size 10 seconds and included about 10 consecutive inter-
beat intervals. The sliding window continuously moves to the next interbeat interval,
overlapping the half of the interval. Therefore, the features for irregularity and morpho-
logical shape can be extracted from the sliding window. To represent irregularity on ECG
data, we created a Poincaré plot, which showed self-similarity in periodic functions and
sequences. A point in the plot can be defined as Pk = (Ik, Ik+1), where Ik is k-th inter-
beat interval. If the measured points converge near to a central point, this phenomenon
implies the interbeat intervals are almost the same in the observed sliding window. In
contrast, a pattern with diffused points represents irregular interbeat intervals.
In real-time processing, to represent irregularity to detect AFib, we modified three
features based on our previous study [16]: (1) a simplified mean stepping increment,
(2) the sum of the distance from the major interbeat interval point, and (3) the num-
ber of clusters in a Poincaré plot. These features are extracted from the current sliding
window which contains n interbeat intervals. The first feature is a simplified mean
stepping increment. The distance between two consecutive points Pk and Pk+1 in the
plot using the Euclidean distance can be formulated as follows: distance(Pk, Pk+1) =
(Ik − Ik+1)2
+ (Ik+1 − Ik+2)2
. If the two consecutive points are regular beats, the
7. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 7 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
distance converges to zero. However, irregularity of ECG signals is accumulated with
increasing distances. Furthermore, by removing the common points, the simplified value
of the summation can be implemented in the portable device. The simplified version of
mean stepping increment was modified as follows:
simplified mean stepping =
n
k=1
distance(Pk, Pk+1)
Ik
. (1)
The second feature is the sum of the distance from a diagonal line in a Poincaré plot. If
a point is located around a diagonal line in the plot, then it denotes that x and y positions
have similar values. This characteristic also means that interbeat intervals are regularly
generated. This dispersion feature illustrates how to distribute the points in a plot from
regular interbeat intervals as follows:
dispersion =
1
n
n
k=1
distance((Ik, Ik), Pk). (2)
The third feature is the number of clusters in a Poincaré plot, and this is decided from
the spectral clustering method [37]. In normal cases, the interbeat intervals are regular.
Therefore, the corresponding points in the Poincaré plot are grouped as a small clus-
ter, i.e., closely located points. After the clustering process, a plot of normal ECG signals
shows a consistent group of points. However, a plot of AFib shows scattered points.
To capture characteristics related to myocardial ischemia, we focused on the shape of
the ST segment and QRS complexes. We can extract significant morphological infor-
mation through QRS complexes and the T wave peak. The first feature of ischemia is
cumulative voltage values, which measure how the T wave is elevated from a normal QRS
onset point. If the ST segment deviates from normal levels, this feature value is highly
increased. The mean value of the ST segment is usually located at around the QRS onset
point. The second and third features are a voltage deviation in the ST segment and a slope
from the QRS onset to the offset point, respectively.
As explained above, at the training phase, feature values are agglomerated together to
a feature space. At the test phase, extracted features from preprocessed ECG signals are
classified using support vector machines every minute. We classified the extracted test
feature values using the trained SVM model.
Implementation and programming environment
The proposed device captures ECG signals from the human body using a four-pole clip
electrode through the analog front-end. Furthermore, we used three-lead ECG signals
from the left arm, right arm, and right leg. The size of the analog front-end module is
(H, W, D) = (0.1 cm, 3.5 cm, 7.7 cm), which can easily be embedded into a wearable ECG
acquisition device. The instrumentation amplifier (TI INA216) and the OP-Amp (LM358)
are used to obtain regulated ECG signals from the human body. These circuits are suitable
for a portable device with the data acquisition system.
To provide portability, as well as an interactive service, we attached a compact (4.3”
TFT) LCD display device on top of the ARM processor. The LCD4 display provides a
simple and compact display solution with touch screen capability. This display offers a
good resolution of 480 × 272 and a four-wire resistive touch screen provides the oppor-
tunity to design various types of graphic user interfaces. This system is equipped with a
8. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 8 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
compact lithium polymer single cell battery with 1300 mAh and the battery should last
for approximately 3 hours with a full charge. The hardware implementation is shown in
Figure 5.
For scalability, we developed our software using C++ language and QT for embedded
Linux. Preprocessing, feature extraction, classification, and graphic user interface for the
software were implemented and tested in the ARM processor. The classifier was used
based on libSVM, which provides an integrated library for support vector machines [38].
The software consists of three screen activities, including the main signal view, a Poincaré
plot, and data description view.
Figure 6 shows the ECG monitoring and real-time test results on human subjects. The
proposed device simultaneously records ECG signals from a user, then displays it on the
real-time screen. By collecting ECG signals continuously for one minute, we can then
calculate inter-beat intervals for each QRS complex. Furthermore, the Poincaré plot is
drawn from extracted inter-beat intervals and is updated after every minute. The plot
shows heart activity by regular drawing of points. The activity of points converging on
one centroid represents that heart activity is regular and normal. On the other hand, when
the points are irregularly distributed, this plot represents one of the typical AFib cases
[39,40].
Additionally, the data description view is displayed after the measured ECG data are
analyzed. The description view provides essential information with the average extracted
feature values and classification results. The numbers with AF and ischemia represent
the existence of corresponding diseases as (0 for nonexistence and 1 for existence). The
numbers with “feat (1,2,3)” represent a set of three features for detecting AFib as follows:
(1) simplified mean stepping, (2) the sum of the distance from the major interbeat interval
point, and (3) the number of clusters in a Poincaré plot. Similarly, the numbers with “feat
(4,5,6)” represent a set of three features for detecting myocardial ischemia as follows: (4)
cumulative voltage value, (5) voltage deviation in the ST segment, and (6) slope from
the QRS onset to the offset. Since the range of feature values varies widely, all ranges of
features are normalized. The summarized information and corresponding ECG records
would help medical decision for physicians.
Results and discussion
The proposed device is characterized as follows: (1) it reduces hardware complexity,
(2) it has real-time processing, and (3) it has simultaneous feature extraction for AFib
Figure 5 Implemented hardware. Photographs of the implemented hardware. (a) Prototype consisting of
electrodes, display, analog front-end, and ARM processor. (b) Detail view of implemented device.
9. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 9 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
(a) (b)
Figure 6 Implemented software. Real-time ECG monitoring on human subject. (a) Input ECG signals are
visualized in the main view. (b) A Poincaré plot and extracted features are shown on the screen.
and ischemia. To validate our device, we examined the quality of acquired signals,
computational complexity, and the accuracy of the embedded algorithm.
Performance evaluation of the analog front-end
We conducted an experiment to determine the quality of the measured ECG signals
by comparing public ECG databases, synthesis signals, and collected ECG signals The
purpose of this experiment was to determine whether our ECG measurement is simi-
lar to well-organized cardiac databases. We prepared three ECG signals as follows. First,
existing records from the PhysioNet databases were chosen as public ECG signals [36].
Second, synthesized ECG signals with simulated random noise and wandering baseline
were generated as follows:
noise = rand() · σECG ·
sin
rand() · π
SampRate
+
1
2
cos
rand() · 2π
SampRate
. (3)
The noise was added to the public ECG signals as Eq. 3. The waveform of our result had
a similar amplitude to the public ECG signals and stable shapes. We then transformed
signals from the time domain to the frequency domain. The measured ECG signals were
similar to the quality of the public cardiac database. This finding indicates that various
types of noises were well controlled and maintained for the purpose of analysis. We then
estimated the power spectral density using a periodogram in the frequency domain. We
calculated the similarity by using the root-mean-square-error (RMSE) between the sig-
nals as follows: RMSE(ECGexisting, ECGsimulated) = 0.0308, RMSE(ECGexisting, ECGour) =
0.0545, RMSE(ECGsimulated, ECGour) = 0.0853. The similarity results showed that our
device captured ECG signals that were as clean as the public cardiac database. Addition-
ally, ECG signals from our device were distinguished from simulated ECG signals. This
result indicates the quality of our analog front-end and digital filters.
In addition, We have conducted additional tests to compare the SNR before and after
filtering using MIT-BIH Noise Stress Test Database [36]. This database provides three
types of noises (e.g. baseline wander (BW), muscle artifact (MA), and electrode motion
artifact (EM)). Various noisy signals have been generated and tested by combining nor-
mal ECG signal from MIT-BIH Arrhythmia database with the noises [36]. The following
Table 2 shows the experiment results of comparing the SNR before and after the filtering.
Average SNR improvements of BW, EM, and MA are (7.3632 dB, 5.2544 dB, 6.5382 dB),
resulting in overall SNR improvement of 6.3853 dB.
10. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 10 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
Table 2 Test results to compare the SNR before and after filtering
Clean signal from Type of SNR before SNR after SNR
MIT-BIH Arrhythmia DB noise filtering (dB) filtering (dB) improvement (dB)
Record 118 BW 6 15.1689 9.1689
Record 118 EM 6 13.7682 7.7682
Record 118 MA 6 14.4875 8.4875
Record 118 BW 10 18.5482 8.5482
Record 118 EM 10 15.7853 5.7853
Record 118 MA 10 17.5477 7.5477
Record 118 BW 14 19.4872 5.4872
Record 118 EM 14 17.5643 3.5643
Record 118 MA 14 18.6872 4.6872
Record 119 BW 6 14.2548 8.2548
Record 119 EM 6 12.2346 6.2346
Record 119 MA 6 13.7458 7.7458
Record 119 BW 10 17.9658 7.9658
Record 119 EM 10 16.4867 6.4867
Record 119 MA 10 17.1054 7.1054
Record 119 BW 14 18.7548 4.7548
Record 119 EM 14 15.6875 1.6875
Record 119 MA 14 17.6561 3.6561
Average 10 16.3853 6.3853
Performance evaluation of computational complexity
Real-time monitoring of the device was tested by measuring the computational time of
the primary components. Computational complexity is based on processes, such as signal
acquisition, digital filters, feature extraction, and classifications. We measured average
values for the central processing unit (CPU) and memory use per minute. The results of
the performance evaluation are as follows: (1) average CPU use: 33%, (2) minimum CPU
use: 11%, (3) maximum CPU use: 56%, and (4) average memory consumption: 55%. Our
results showed the feasibility of our device in real situations.
Evaluation of sensitivity and specificity for AFib and myocardial ischemia
We also compared our results with recent detection algorithms such as Artificial Neural
Networks, Principal Component Analysis, Genetic Algorithms, Rule-based method, and
morphological analysis [10-17]. For each heart disease, we already found the set of param-
eters with the best classification results in our previous work [16,17]. For the purpose of
overall comparison, we evaluated our method with the following three sets of MIT-BIH
databases: (1) MIT-BIH AF and arrhythmia, (2) CinC challenge 2001 and 2004 databases,
and (3) European ST-T database from PhysioNet. The number of waveforms are (1) 48
and 27, (2) 300 and 110, and (3) 90, respectively. These waveforms randomly partitioned
into 10 equal size sub-samples for 10-fold cross validation. Of the 10 sub-samples, a sin-
gle sub-sample is retained as the validation data for testing the model, and the remaining
9 samples are used as training data. The cross-validation process is then repeated 10
times, with each of the 10 samples used exactly once as the validation data. Then, the 10
11. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 11 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
results from the folds are averaged to produce a single estimation result. To compare the
classification results, we measured the sensitivity and specificity as follows:
sensitivity =
TP
(TP + FN)
× 100, specificity =
TN
(TN + FP)
× 100 (4)
Where true positive (TP) implies normal beats, which are correctly detected as anno-
tated. False positive (FP) represents abnormal beats that are classified into normal labels.
True negative (TN) denotes abnormal beats that are detected as annotated with heart dis-
ease. False negative (FN) indicates normal beats, which are considered as abnormal cases.
Table 3 shows the results of the classification with sensitivity and specificity. The aver-
age sensitivity and specificity of our method were 95.1% and 95.9%, respectively. These
results indicate that our method effectively detects AFib and ischemia cases with a higher
performance of detection for distinct cardiac diseases, while existing methods only focus
on one target disease.
Conclusion
In this study, we proposed and implemented a portable ECG device for real-time and per-
sonal purposes. We reduced the hardware complexity by using the digital filter-driven
hardware architecture. By using this device, patients can keep tracking the condition
of their heart on a daily basis, at low cost. According to the experimental results with
MIT-BIH databases, our algorithm has a higher sensitivity and specificity of 95.1% and
95.9%, respectively. In addition, the proposed device has lower computational complex-
ity than other existing detection algorithms that capture abnormal heart activities from
atrial and ventricular chambers on portable and mobile platform. In summary, our device
contributes to excellent monitoring and acceptable analysis results for helping medi-
cal decision making. Our results provide empirical evidence to substantiate real-time as
well as show that our portable personal health care device has high quality signals, low
computational complexity, and accurate detection ability.
Table 3 Offline classificationresults for AFib and ischemia using an SVM
Previous works Target Databases Sensitivity Specificity
diseases
Papaloukas et al. [10] Ischemia European ST-T 0.9 0.9
Goletsis et al. [11] Ischemia European ST-T 0.912 0.909
Exarchos et al. [13] Ischemia European ST-T 0.912 0.922
Park et al. [17] Ischemia European ST-T 0.957 0.953
Dash et al. [15] AFib MIT-BIH AF and MIT-BIH Arrhythmia 0.944 0.951
Logan and Glass [12] AFib MIT-BIH AF 0.96 0.89
Kikillus et al. [14] AFib MIT-BIH AF and MIT-BIH NSR 0.944 0.934
Park et al. [16] AFib CinC 2001 and 2004 0.914 0.929
This work
AFib MIT-BIH AF and MIT-BIH Arrhythmia 0.956 0.962
AFib CinC 2001 and 2004 0.928 0.938
Ischemia European ST-T 0.969 0.977
12. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 12 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
TJ designed the framework of the portable ECG device and wrote the manuscript. BK contributed to hardware design of
the device. BL designed the analog front-end and proofread the manuscript. MJ designed the experiment and checked
the validity of the proposed methods. All authors read and approved the final manuscript.
Acknowledgements
This work was supported in part by a Systems Biology Infrastructure Establishment Grant provided by the Gwangju
Institute of Science and Technology (GIST) in 2014, and the Basic Research in High-tech Industrial Technology Project
through a grant provided by GIST in 2014.
Author details
1School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
2School of Mechatronics, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea. 3Broadcom
Corporation, Irvine CA 92617, USA.
Received: 28 August 2014 Accepted: 19 November 2014
Published: 10 December 2014
References
1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie
C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth
LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, III ERM, Moy CS, et al.: Heart disease and
stroke statistics–2014 update: a report from the American Heart Association. Circulation 2013, 129:e28–e292.
2. Levy S, Camm AJ, Saksena S, Aliot E, Breithardt G, Crijns H, Davies W, Kay N, Prystowsky E, Sutton R, Waldo A, Wyse DG:
International consensus on nomenclature and classification of atrial fibrillation. Europace 2003, 5:119–122.
3. Rieta JJ, Castells F, Sánchez C, Zarzoso V, Millet J: Atrial activity extraction for atrial fibrillation analysis using
blind source separation. IEEE Trans Biomed Eng 2004, 51(7):1176–1186.
4. Petrutiu S, Ng J, Nijm GM, Al-Angari HM, Swiryn S, Sahakian AV: Atrial fibrillation and waveform characterization.
IEEE Eng Med Biol Mag 2006, 25(6):24–30.
5. Asl BM, Setarehdan SK, Mohebbi M: Support vector machine-based arrhythmia classification using reduced
features of heart rate variability signal. Artif Intell Med 2008, 44:51–64.
6. García J, Sörnmo L, Olmos S, Laguna P: Automatic detection of ST-T complex changes on the ECG using filtered
RMS difference series: application to ambulatory ischemia monitoring. IEEE Trans Biomed Eng 2000,
47(9):1195–1201.
7. Kusumoto FM: Cardiovascular Pathophysiology. Raleigh, North Carolina: Hayes Barton Press; 2004.
8. Pueyo E, Sörnmo L, Laguna P: QRS slopes for detection and characterization of myocardial ischemia. IEEE Trans
Biomed Eng 2008, 55(2):468–477.
9. Ruud TS, Nielsen BF, Lysaker M, Sundnes J: A computationally efficient method for determining the size and
location of myocardial ischemia. IEEE Trans Biomed Eng 2009, 56(2):263–272.
10. Papaloukas C, Fotiadis D, Likas A, Michalis L: An ischemia detectionmethod based on artificial neural networks.
Artif Intell Med 2002, 24:167–178.
11. Goletsis Y, Papaloukas C, Fotiadis D, Likas A, Michalis L: Automated ischemic beat classification using genetic
algorithms andmulticriteria decision analysis. IEEE Trans Biomed Eng 2004, 51:1717–1725.
12. Logan B, Healey J: Robust detection of atrial fibrillation for a long term telemonitoring system. In Computers
in Cardiology. IEEE 2005:619–622.
13. Exarchos T, Tsipouras M, Exarchos C, Papaloukas C, Fotiadis D, Michalis L: Amethodology for the automated
creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules
obtained by a decision tree. Artif Intell Med 2007, 40:187–200.
14. Kikillus N, Hammer G, Wieland S, Bolz A: Algorithm for identifying patients with paroxysmal atrial fibrillation
without appearance on the ECG. Conf Proc IEEE Eng Med Biol Soc 2007, 2007:275–8.
15. Dash S, Chon K, Lu S, Raeder E: Automatic real time detection of atrial fibrillation. Ann Biomed Eng 2009,
37:1701–1709.
16. Park J, Lee S, Jeon M: Atrial fibrillation detection by heart rate variability in Poincare plot. Biomed Eng Online
2009, 8(38):1–12.
17. Park J, Pedrycz W, Jeon M: Ischemia episode detection in ECG using kernel density estimation, support vector
machine and feature selection. Biomed Eng Online 2012, 11(30):1–22.
18. AliveCor Heart Monitor: iPhoneECG [http://www.alivecor.com/]
19. Everist Health: CardioDefender [http://everisthealth.com/]
20. Beurer: ME 80 [http://www.beurer.com]
21. HeartrHeart: EPI Life [http://www.heartronics.com.my]
22. DailyCare BioMedical: ReadMyHeart [http://www.dcbiomed.com/webls-en-us/ReadMyHeart:V2.0.html]
23. Cardiac Designs: ECGCheck [http://www.ecgcheck.com/]
24. Dimetek: Dicare-m1C [http://www.dimetekus.com/Micro-Ambulatory-ECG-Recorder_p238.html]
25. HeartCheck: The HeartCheck PEN [http://www.theheartcheck.com/products/pen_device.html]
26. REKA Health: E100 cardiac Monitor [https://www.rekahealth.com]
27. SHL Telemedicine: Smartheart [http://www.shl-telemedicine.com/portfolio/smartheart]
28. Mortara: ELI 10 mobile [http://www.mortara.com]
29. ChoiceMMed: MD100E [http://www.choicemmed.com/info.aspx?m=photoid=537]
13. Jeon et al. BioMedical Engineering OnLine 2014, 13:160 Page 13 of 13
http://www.biomedical-engineering-online.com/content/13/1/160
30. Creative Medical: PC-80 [http://www.creative-sz.com/Easy-ECG-Monitor/Easy-ECG-Monitor-PC-80A.html]
31. CONTEC Medical System: ECG80A [http://www.contecmed.com]
32. Baig MM, Gholamhosseini H, Connoly MJ: A comprehensive survey of wearable and wireless ECG monitoring
systems for older adults. Med Biol Eng Comput 2013, 51:485–495.
33. Dobrev D: Review of “Analysis and application of analog electronic circuits to biomedical instrumentation”
by Robert B Northrop. Biomed Eng Online 2012, 11(29):1–7.
34. Vázquez-Seisdedos CR, Neto JaE, Marañón Reyes EJ, Klautau A, Limão de Oliveira RC: New approach for T-wave
end detection on electrocardiogram: performance in noisy conditions. Biomed Eng Online 2011, 10(77):1–11.
35. Poungponsri S, Yu XH: An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction
using neural networks. Neurocomputing 2013, 117:206–213.
36. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE:
PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex
physiologic signals. Circulation 2000, 101(23):e215–e220. [Circulation Electronic Pages: http://circ.ahajournals.org/
cgi/content/full/101/23/e215 PMID:1085218;doi:10.1161/01.CIR.101.23.e215].
37. Chung FR: Spectral graph theory, Volume 92. USA: American Mathematical Soc.; 1997.
38. Chang CC, Lin CJ: LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2011, 2:27:1–27:27.
Software available at [http://www.csie.ntu.edu.tw/~cjlin/libsvm]
39. Kasmacher H, Wiese S, Lahl M: Monitoring the complexity of ventricular response in atrial fibrillation. Discrete
Dyn Nat Soc 2000, 4:63–89.
40. Zemaityte D, Varoneckas G, Ozeraitis E, Podlipskyte A, Valyte G, Zakarevicius L: Heart rate Poincare plots and their
hemodynamic correlates: discrimination between sinus and ectopic rhythms. Biomedicine 2001, 1(2):80–89.
doi:10.1186/1475-925X-13-160
Cite this article as: Jeon et al.: Implementation of a portable device for real-time ECG signal analysis. BioMedical
Engineering OnLine 2014 13:160.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit