Contemplate on ECG signals and classification of arrhythmia signals using CNN-LSTM deep learning model. (December 2022)

• Record Type: Journal Article
• Authors: Sowmya, S.; Jose, Deepa
• Is Part Of: Measurement. Volume 24(2022)
• Publication Date: 2022-12
• Subjects: Arrhythmia detection -- Machine learning -- Convolutional Neural Networks (CNN) -- Long Short-Term Memory (LSTM) -- Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH); Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2022.100558
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: An electrocardiogram (ECG) is a schematic illustration of heart signals that is being used to measure the electric signals of the heart and to detect any abnormalities. Due to non-invasive qualities, the Electrocardiogram (ECG) has become a commonly employed auxiliary diagnostic index for heart problems in pre-screening and give information of heart diseases. Many methods are used to find out the abnormalities in heartbeat. In this paper survey is done to find out what are the methods used to classify the ECG recordings to predict cardiovascular diseases which affects middle aged as well as older people causing severe illness leading to death. One of the major abnormality was due to arrhythmia disease. Hence many deep learning methods were used to find early prediction of arrhythmia to save lives of people. From the survey it is found that many ECG classifications done using existing database such as MIT-BIH arrhythmia. Most of the methods work on classifications, feature extractions to find abnormalities in ECG signals and found to have higher accuracies of more than 94%.In this paper, the study is based on abnormalities in ECG signals due to arrhythmia and its identification using a network architecture based on LSTM and CNN deep learning methods. The simulation result shows the CNN- LSTM algorithm has higher accuracy compared to CNN.