Single-Cycle Pulse Signal Recognition Based on One-Dimensional Deep Convolutional Neural Network

Abstract

Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement.