A pooling convolution model for multi-classification of ECG and PCG signals

Comput Methods Biomech Biomed Engin. 2024 Jan 9:1-14. doi: 10.1080/10255842.2023.2299697. Online ahead of print.ABSTRACTElectrocardiogram (ECG) and phonocardiogram (PCG) signals are physiological signals generated throughout the cardiac cycle. The application of deep learning techniques to recognize ECG and PCG signals can greatly enhance the efficiency of cardiovascular disease detection. Therefore, we propose a series of straightforward and effective pooling convolutional models for the multi-classification of ECG and PCG signals. Initially, these signals undergo preprocessing. Subsequently, we design various structural blocks, including a stacked block (MCM) comprising convolutional layer and max-pooling layers, along with its variations, as well as a residual block (REC). By adjusting the number of structural blocks, these models can handle ECG and PCG data with different sampling rates. In the final tests, the models utilizing the MCM structural block achieved accuracies of 98.70 and 92.58% on the ECG and PCG fusion datasets, respectively. These accuracies surpass those of all networks utilizing its variations. Moreover, compared to the models employing the REC structural block, the accuracies are improved by 0.02 and 4.30%, respectively. Furthermore, this research has been validated through tests conducted on multiple ECG and PCG datasets, along with comparisons to other published literature. To further validate the generalizability of the model, an additional experimen...

Source: Computer Methods in Biomechanics and Biomedical Engineering - January 9, 2024 Category: Biomedical Engineering Authors: Juliang Wang Junbin Zang Qi An Haoxin Wang Zhidong Zhang Source Type: research