Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term Memory (LSTM) layer, is utilized to recognize arrhythmia. Indeed, the segments centered around R-peaks acquired from the previous step are fed into this network to distinguish various arrhythmias. The LSTM part of the proposed network enables modeling the relation among different heartbeats in a sequence. Experiments on the MIT-BIH databases and Creighton university ventricular tachyarrhythmia show the superiority of our proposed method on arrhythmia detection in comparison with the recent methods proposed for this problem. The performance of the proposed model on test samples is 98.93%, 99.78%, and 99.58% respectively in terms of overall accuracy, sensitivity, and specificity for tackling the problem of 4-class arrhythmia classification. Thus, it outperforms other recent methods with a large margin in terms of accuracy and specificity.

A Hybrid Deep Model for Automatic Arrhythmia Classification based on LSTM Recurrent Networks

High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.

http://www.diagnostyka.net.pl/pdf-126398-54267?filename=Methods of extracting.pdf

Methods of extracting electrocardiograms from electronic signals and images in the Python environment

Health monitoring in humans is very important. This monitoring can be done in different people from embryonic period to adulthood. A healthy fetal will lead to a healthy baby. For this purpose, health assessment methods are used from the fetal to adulthood. One of the most common methods of assessing health at different times is to use clinical signs and data. Measuring heart rate, blood pressure, temperature, and other symptoms can help monitor health. However, there are usually errors in human predictions. Data mining is a technique for identifying and diagnosing diseases, categorizing patients in disease management, and finding patterns to diagnose patients more quickly and prevent complications. It could be a great help. Increasing the accuracy of diagnosis, reducing costs, and reducing human resources in the medical sector have been proven by researchers as the benefits of introducing data mining in medical analysis. In this paper, data mining methods will be introduced to diagnose and monitor health in individuals with various heart diseases. Heart disease will be evaluated to make the study more comprehensive, including fetal health diagnosis, arrhythmias, and machine learning data mining angiography. Attempts are made to introduce the relevant database in each disease and to evaluate the desired methods in health monitoring. 

Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional Review

A lot of development is going on in the area of automation in the health care sector for few years. It helps clinicians to accurately diagnose diseases. However, since mortality rate has been increased due to heart diseases, it leads to concentrate on the heart-related diseases and early, accurate diagnosis might help reduce the risk. ECG being the most commonly used technique, ECG signal processing becomes crucial. This survey covers different techniques, databases used in past years for detection of arrhythmia using ECG signal.

Bhagyashri Bhirud

Papers

Arrhythmia Detection Using ECG Signal: A Survey