Classification of cardiac abnormality based on BCG signal using 1-D convolutional neural network

This paper presents a ballistocardiogram (BCG) classification method based on 1-D convolution neural networks (CNNs), which provides an auxiliary basis for clinical diagnosis, especially in the monitoring of cardiac function in the elderly. Four categories of BCG signals are used as input of 1-D CNN. By moving the convolution kernel on the time axis, the temporal variability of BCG signals can be better satisfied while retaining the frequency band correlation. Then they were sent to the multi-layer sensor, after that the features processed by softmax classifier were classified. We obtained an accuracy of 93.39%, of which the H class is 95.36%, the C class is 86.19%, the D class is 95.31%, and the Y class is 96.57%. Compared with the existing research results, the proposed method achieves a superior classification performance. This method is simple, fast and highly generic, which can serve as a reliable adjunct tool for the daily diagnosis of heart disease in the elderly.