Using an Active Learning Semi-supervision Algorithm for Classifying of ECG Signals and Diagnosing Heart Diseases

2018 ◽  
pp. 69-82
Author(s):  
Javad Kebriaee ◽  
Hadi Chahkandi Nejad ◽  
Sadegh Seynali
Keyword(s):  
Active Learning ◽  
Heart Diseases ◽  
Ecg Signals

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

scholarly journals Washable embroidered textile electrodes for long-term electrocardiography monitoring

2019 ◽  
Vol 2 (3) ◽  
pp. 126-135 ◽  
Author(s):  
Amale Ankhili ◽  
Shahood uz Zaman ◽  
Xuyuan Tao ◽  
Cedric Cochrane ◽  
Vladan Končar ◽  
...  
Keyword(s):  
Heart Diseases ◽  
Thermoplastic Polyurethane ◽  
Health Condition ◽  
Ecg Signals ◽  
Medical Quality ◽  
Textile Electrodes ◽  
Washing Process ◽  
Encapsulation Process ◽  
Domestic Machine

The improvement of human health condition is an important objective that remains relevant since the origin of human being. Currently, cardiovascular diseases are the first cause of death worldwide. For this reason, permanent real-time monitoring of heart activity (Electrocardiogram: ECG), its analysis and alerting of concerned person is a solution to decrease the death toll provoked by heart diseases. ECG signal of medical quality is necessary for permanent monitoring and accurate heart examining. It can be obtained from instrumented underwear only if it is equipped with high quality, flexible textile based electrodes guaranteeing low contact resistance between the skin and them. This work is therefore devoted to the design and test of wearable textile embroidered bands following defined protocol for ECG long-term monitoring. These bands were investigated in three configurations: band without any adding layer to protect lines between electrodes and the connector, band with lines protected by simple yarn, band with lines protected with thermoplastic polyurethane (TPU). Bands were worn around chest by healthy subjects in a sitting position and ECG signals were acquired by an Arduino-based device and assessed. Washability tests of connected underwear were carried out over 50 washing cycles in a domestic machine and by using a commercial detergent. Influence of encapsulation process on the electrical properties of textile electrodes during repetitive washing process has also been investigated and analyzed. All the ECG signals acquired and recorded have been reviewed by a cardiologist in order to validate their quality required for accurate diagnosis.

Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi

2018 ◽  
Vol 35 ◽  
pp. 96-108
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Mohammad Iqbal Omar
Keyword(s):  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Features Extraction ◽  
Raspberry Pi ◽  
Support Vector ◽  
Ecg Signal ◽  
Statistical Features ◽  
Classification Approach ◽  
Ecg Signals ◽  
Heart Monitoring

Heart monitoring kits are only available for bedridden patients and the traditional heart monitoring kits have many wires that are obstacle patients’ mobility. Most of the existing heart monitoring kits can not detect heart diseases. Thus, the current study proposed a wireless heart monitoring kit to monitor patients with a heart abnormality. The proposed kit can detect and classify four arrhythmia types as well as normal ECG with high accuracy. The design and development of the wireless heart abnormality monitoring kit (WHAMK) in this research were divided into three stages. These stages are the development of an arrhythmias detection and classification method using artificial intelligence approach, design and implementation of the kit hardware, and design and coding of the kit software. Arrhythmias classification approach is divided into four stages, namely obtaining the electrocardiograph (ECG) signals, preprocessing, features extraction and classification. The features extraction method are based on statistical features. The library support vector machine (LIBSVM) was used to classify the ECG signals. The hardware of the kit is divided into two parts, namely ECG body sensor (EBS), and processing and displaying unit (PDU). EBS working on acquiring the ECG signal from patient's body. PDU working on processing the collected ECG signal, plotting it and detecting the arrhythmias. Arrhythmias classification approach was developed by using statistical features and LIBSVM. They were implemented in the software of the kit to enable it to detect the arrhythmias in the real-time and fully automatically. The kit can detect and classify four arrhythmia types as well as normal sinus rhythm (NSR). These types of arrhythmia are premature atrial contraction (PAC), premature ventricles contraction (PVC), Bradycardia and Tachycardia. The proposed kit gave a good accuracy for detecting and classifying Arrhythmia with the overall accuracy of 96.2%.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

Detection of Heart Defects using Electrocardiogram (ECG)

2018 ◽  
Vol 4 (11) ◽  
Author(s):  
Kenil Shah ◽  
Mayur Rane ◽  
Dr. Vahid Emamian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Heart Diseases ◽  
Heart Defects ◽  
Healthy Person ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Processing Techniques ◽  
Electrocardiogram Ecg ◽  
Ecg Data

Electrocardiogram (ECG) signals are vital to identifying cardiovascular disease. The numerous availability of signal processing and neural networks techniques for processing of ECG signals has inspired us to do research on extracting features of ECG signals to identify different cardiovascular diseases. We distinguish between a healthy person ECG data and person having disease ECG data using signal processing and neural network toolbox in Matlab. The data was downloaded from physiobank. To distinguish normal and abnormal ECG, Neural network is used. Feature extraction method is used to identify heart diseases. The diseases that are identified include Tachycardia, Bradycardia, first- degree Atrioventricular (AV) and a healthy person. Subsequently, ECG signals are very noisy; signal processing techniques are used to remove the noise impurity. The heart rate can be calculated by detecting the distance between R-R intervals of the signal. The algorithm successfully distinguished between normal and abnormal ECG data.

scholarly journals Identification of Cardiac Diseases from(ECG) Signals based on Fractal Analysis

2014 ◽  
Vol 13 (6) ◽  
pp. 4556-4565
Author(s):  
Rashiq Marie ◽  
Maram H. Al Alfi
Keyword(s):  
Heart Diseases ◽  
Healthy Person ◽  
Spectral Density Function ◽  
Cardiac Diseases ◽  
Power Spectral Density Function ◽  
Ecg Signals ◽  
Power Spectral ◽  
Fractal Signal ◽  
Power Spectrum Method ◽  
Proper Analysis

This paper investigates the use of fractal geometry for analyzing ECG time series signals. A technique of identifying cardiac diseases is proposed which is based on estimation of Fractal Dimension (FD) of ECG recordings. Using this approach, variations in texture across an ECG signal can be characterized in terms of variations in the FD values. An overview of methods for computing the FD is presented focusing on the Power Spectrum Method (PSM) that makes use of the characteristic of Power Spectral Density Function (PSDF) of a Random Scaling Fractal Signal. A 20 dataset of ECG signals taken from MIT-BIH arrhythmia database has been utilized to estimate the FD, which established ranges of FD for healthy person and persons with various heart diseases. The obtained ranges of FD are presented in tabular fashion with proper analysis. Moreover, the experimental results showing comparison of Normal and Abnormal (arrhythmia) ECG signals and demonstrated that the PSM shows a better distinguish between the ECG signals for healthy and non-healthy persons versus the other methods.

scholarly journals Feature Extraction and Classification of Segmented ECG Signals Based on Radial Basis Function and Random Forest Methodology

2021 ◽  
Vol 10 (02) ◽  
pp. 35-45
Author(s):  
Rexy J ◽  
Velmani P ◽  
Rajakumar T.C
Keyword(s):  
Heart Disease ◽  
Random Forest ◽  
Radial Basis Function ◽  
Basis Function ◽  
Performance Metrics ◽  
Heart Diseases ◽  
Ecg Signals ◽  
Specificity And Sensitivity ◽  
Radial Basis ◽  
The Given

Heart disease is the major cause of death ratio increase in this decade. Nowadays various people of different age sector undergo the high risk of heart problems and miss their precious life all of a sudden. Early detection of heart disease will save many people’s life well in advance. Heart Diseases are predictable and they can be identified in earlier stage. First basic method to identify heart disease is ElectroCardioGram (ECG) which is the basic recording method of electrical activities of a functioning heart. ECG is the cheapest and painless method to detect the basic heart problems. This paper is an attempt to detect and classify heart beat signals which will serve as the basic step to predict basic and serious issues which may affect the functioning of the heart. The raw ECG signals are extracted and preprocessed to remove unwanted noises which will produce effective results. The preprocessed ECG signals are then are utilized to identify the heart beats which comprise of signals such as P,Q,R,S,T and U. After detecting the heart beats, they are segmented to extract the ECG Features. The temporal and spectral features are extracted from the segmented ECG signals for classification purpose. The extracted feature vectors are utilized to classify the signals. Radial Basis function and Random Forest method are commonly used classification methodologies; hence these two methodologies are applied to classify the ECG Signals into five basic classes. Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database and Noise Stress database are used for this implementation and the classes are identified based on the given dataset parameters. Performance metrics such as accuracy, specificity and sensitivity are computed to find out the best classification methodology among the applied two methodologies. This performance analysis provides a clear comparative view of both the existing methodologies and specifies that Radial Basis function well suits for the given segmented ECG signals and the extracted features. Hence this performance evaluation paves way for best classification algorithm selection or extension of the best methodology and it can be further optimized for better classification result. The implementation process has been carried out using Matlab software environment.

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