Publication: Classification of arrhythmia signals by using recurrent neural network
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Date
2022-08-01
Authors
Lip, Chan Qin
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Abstract
Arrhythmia is a problem with the rate of the heartbeat that either beats too fast, or too slow, or with an irregular rhythm. Atrial fibrillation (AF) is the most common arrhythmia that can be diagnosed by using an electrocardiogram (ECG) pattern. Classification of arrhythmia through ECG can be very challenging if we fully depend on experts because it is very time-consuming due to neural networks use neurons to transmit data in the form of input values and output values through connections, deep learning is associated with the transformation and extraction of features. The experimental data is obtained from PhysioNet CinC Challenge 2017 Database. The ECG signals undergone pre-processing including filtering, QRS detection, segmentation, and median wave selection. In this project, recurrent neural network (RNN) model, RNN-Long Short Term Memory (RNN-LSTM) model, and RNN-Bidirectional LSTM (RNN-BiLSTM) model are developed to classify ECG signals into four classes, which are the normal rhythm, AF rhythm, other rhythms, and noisy signal. The proposed RNN model achieved an accuracy of 71.00%, the RNN-LSTM model achieved 86.00% of accuracy, while the RNN-BiLSTM model achieved 89.00% of accuracy