December 1, 2021BMC Medical Informatics and Decision Making146 citationsOpen Access

ECG signal classification based on deep CNN and BiLSTM

JCJinyong ChengQilu University of Technology QZQingxu ZouChifeng Municipal Hospital YZYunxiang ZhaoSouth China Agricultural University

Structured PICO

Does a deep CNN and BiLSTM model improve the accuracy of automatic ECG classification?

Population

ECG data from the 2017 PhysioNet/CINC challenge dataset

Intervention

Dense heart rhythm network combining a 24-layer Deep Convolutional Neural Network (DCNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a new loss function

Comparator

Existing methods

Outcome

Accuracy rate and F1 score of automatic ECG classificationsurrogate

A novel deep learning model combining DCNN and BiLSTM achieved 89.3% accuracy and an F1 score of 0.891 for automatic ECG classification, potentially aiding in clinical diagnosis and self-monitoring of atrial fibrillation.

Abstract

BACKGROUND: Currently, cardiovascular disease has become a major disease endangering human health, and the number of such patients is growing. Electrocardiogram (ECG) is an important basis for {medical doctors to diagnose the cardiovascular disease, which can truly reflect the health of the heart. In this context, the contradiction between the lack of medical resources and the surge in the number of patients has become increasingly prominent. The use of computer-aided diagnosis of cardiovascular disease has become particularly important, so the study of ECG automatic classification method has a strong practical significance. METHODS: This article proposes a new method for automatic identification and classification of ECG.We have developed a dense heart rhythm network that combines a 24-layer Deep Convolutional Neural Network (DCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to deeply mine the hierarchical and time-sensitive features of ECG data. Three different sizes of convolution kernels (32, 64 and 128) are used to mine the detailed features of the ECG signal, and the original ECG is filtered using a combination of wavelet transform and median filtering to eliminate the influence of noise on the signal. A new loss function is proposed to control the fluctuation of loss during the training process, and convergence mapping of the tan function in the range of 0-1 is employed to better reflect the model training loss and correct the optimization direction in time. RESULTS: We applied the dataset provided by the 2017 PhysioNet/CINC challenge for verification. The experiment adopted ten-fold cross validation,and obtained an accuracy rate of 89.3Formula: see text and an F1 score of 0.891. CONCLUSIONS: This article proposes its own method in the aspects of ECG data preprocessing, feature extraction and loss function design. Compared with the existing methods, this method improves the accuracy of automatic ECG classification and is helpful for clinical diagnosis and self-monitoring of atrial fibrillation.

问 AI

Bookmark

View Full Paper