Open AccessECG Arrhythmia Time Series Classification Using 1D Convolution –LSTM Neural Networks
Publication year - 2021
Publication title -
international journal of advanced trends in computer science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3091
DOI - 10.30534/ijatcse/2021/691032021
Subject(s) - heartbeat , pattern recognition (psychology) , computer science , artificial intelligence , convolutional neural network , robustness (evolution) , feature extraction , convolution (computer science) , artificial neural network , cardiac arrhythmia , deep learning , wavelet transform , wavelet , cardiology , medicine , biochemistry , chemistry , computer security , gene , atrial fibrillation
An electrocardiogram (ECG) can be dependablyused as a measuring device to monitor cardiovascular function. The abnormal heartbeat appears in the ECG pattern and these abnormal signals are called arrhythmias. Classification and automatic arrhythmia signals can provide a faster and more accurate result. Several machine learning approaches have been applied to enhance the accuracy of results and increase the speed and robustness of models. This paper proposes a method based on Time-series Classification using deep Convolutional -LSTM neural networks and Discrete Wavelet Transform to classify 4 different types of Arrhythmia in the MIT-BIH Database. According to the results, the suggested method gives predictions with an average accuracy of 97% without needing to do feature extraction or data augmentation.