Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network

Abstract

Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with heartbeat activity's global sequence features can effectively improve the accuracy of heartbeat classification. Firstly, the noise is removed by the continuous wavelet transform method. Secondly, the peak of the R wave is detected by the tagged database, and then the P-QRS-T wave morphology and the RR interval are extracted. This feature set is heartbeat activity's global sequence features, which combines single heartbeat morphology and 21 consecutive RR intervals. Finally, the Bi-LSTM algorithm and the BiLSTM-Attention algorithm are used to identify heartbeat category respectively, and the MIT-BIH arrhythmia database is used to verify the algorithm. The results show that the BiLSTM-Attention model combined with heartbeat activity's global sequence features has higher interpretability than other methods discussed in this paper.

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

“…A pediatric heart disease screening application was also solved using a CNN model for the task of automatic structural heart abnormality risk detection from digital phonocardiogram (PCG) signals [52]. Moreover, bidirectional neural network architecture has been introduced for effectively improving the accuracy of heart disease applications using the BiLSTM-Attention algorithm that reached better results (accuracy of 99.49%) than the literature's review [53]. Other applications of deep learning presented in medical imaging and achieved the state-of-the-art results [54], and many challenging tasks were solved in biomedicine considering the utility of the neural networks [55].…”

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

“…In this paper, the data is divided into a training set and a test set, and the label output of the model is compared with the real label to get the experimental results. The results of N category heartbeat classification are calculated by formulas ( 6 )–( 9 ) [ 42 ]. S, V, and F heartbeats are calculated in the same way.…”

“…Higher values of these indicators indicate better classification performance. These four indicators are calculated by the following formula [ 42 ]. …”

“…In this paper, to classify heartbeat more accurately, the noise of raw ECG signal is removed by wavelet transform. Wavelet transform is a signal time-frequency analysis method [ 42 ], which can retain the features of ECG signal. Besides, it avoids important physiological details and has a simple calculation process [ 43 , 44 ].…”

“…In formula ( 1 ), m represents the scale factor and n represents the transforming parameter. They are mainly used to stretch the basic wavelet function ψ ( t ), τ reflects the displacement, and m and τ are continuous variables, so it is also called continue wavelet transform (CWT) [ 42 ].…”