Key result
The MLCNN-BiLSTM neural network achieved an accuracy rate of 87.81%, sensitivity of 86.00%, and specificity of 87.76% for detecting multiple cardiovascular diseases from multilead ECGs.
Why the study?
ECG signals contain rhythmic and morphological features that vary across diseases, requiring models tailored to the special structure of multilead ECG.
Does a multichannel parallel neural network (MLCNN-BiLSTM) improve the diagnostic accuracy of multilead ECG for cardiovascular diseases compared to traditional CNN?
Does a multichannel parallel neural network (MLCNN-BiLSTM) improve the diagnostic accuracy of multilead ECG for cardiovascular diseases compared to traditional CNN?
The proposed MLCNN-BiLSTM neural network demonstrates high accuracy and could serve as a first-round screening tool for clinical ECG diagnosis.
Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.
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Lu et al. (2020) studied Cardiovascular diseases. MLCNN-BiLSTM neural network vs. Traditional convolutional neural network (CNN) was evaluated on Accuracy rate for multiple cardiovascular diseases. The MLCNN-BiLSTM neural network achieved an accuracy rate of 87.81%, sensitivity of 86.00%, and specificity of 87.76% for detecting multiple cardiovascular diseases from multilead ECGs.
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