Feature Extraction and Recognition of Human Physiological Signals Based on the Convolutional Neural Network

Abstract

Human physiological signal processing is one of the research fields widely used in recent years. Research on human physiological signals plays a vital role in predicting human health and detecting and classifying certain disease outbreaks. The network of human physiological signals is difficult to determine because it contains a lot of information about human activities. To this end, a variety of feature extraction, feature selection, and classification algorithms have been implemented in the anomaly prediction process. However, it has the main disadvantage of classification results, using a large number of features and increasing complexity. In order to solve these problems, this paper proposes a convolutional neural network-based extraction technique for human physiological signal features and uses an MPL classifier to detect whether the ECG signal is normal or not, taking the ECG signal as an example. In this paper, the signal preprocessing method based on wavelet transform and morphological filtering is adopted, and the high-frequency signal is removed by wavelet transform, and the low-frequency signal is removed by morphological filtering. A wide range of tests on ECG signals obtained from the MIT-BIH-AR databank and INCART database showed that the method has good detection performance with sensitivity Sen = 99.54%, positive prediction rate PPR = 99.65%, detecting mistake ratio DER = 0.35% and precision Acc = 99.55%, which is an improved performance compared to other techniques, proving the superiority of the present technique.