Multi-Morphological Pulse Signal Feature Point Recognition Based on One-Dimensional Deep Convolutional Neural Network

• Published in: Information (Basel) Vol. 14; no. 2; p. 70
• Main Authors: Wang, Guotai, Geng, Xingguang, Huang, Lin, Kang, Xiaoxiao, Zhang, Jun, Zhang, Yitao, Zhang, Haiying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: 1D-DCNN; Accuracy; Algorithms; Artificial neural networks; Cardiovascular disease; Coronary vessels; Curvature; curvature method; Datasets; Diagnosis; Feature recognition; Flow velocity; Heart; Hemodynamics; Laboratories; Machine learning; Mathematical models; Methods; multi-morphological; Neural networks; Parameter identification; Physiological aspects; Physiology; pulse signal; Signal processing; Support vector machines; Time domain analysis; Traditional Chinese medicine; Type 2 diabetes; Veins & arteries; China
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14020070

Radial pulse signals are produced by the periodic ejection of blood from the heart, and physiological and pathological information of the human body can be analyzed by extracting the time-domain characteristics of pulse waves. However, since pulse signals are weak physiological signals on the body surface and complex, the acquisition of pulse characteristics using the traditional curvature method will produce a large error, which cannot meet the needs of pulse wave analysis in current clinical practice. To solve this problem, a multi-morphological pulse signal feature recognition algorithm based on the one-dimensional deep convolutional neural network (1D-DCNN) model is proposed. We used the multi-channel pulse diagnosis instrument independently developed by the team to collect radial pulse signals under continuous pressure of the test subjects and collected 115 subjects and extracted a total of 1300 single-cycle pulse signals and then divided these pulse signals into 6 different forms. Five types of pulse signal time-domain feature points were labeled, and five independent feature point datasets were labeled and formed five customized neural network models that were generated to train and identify the pulse feature point datasets independently. The results show that the correction coefficient (Radjusted2) of the multi-class pulse signal processing algorithm proposed in this paper for each type of feature point recognition reaches more than 0.92. The performance is significantly better than that of the traditional curvature method, which shows the accuracy and superiority of the proposed method. Therefore, the multi-class pulse signal characteristic parameter recognition model based on the 1D-DCNN model proposed in this paper can efficiently and accurately identify pulse time-domain characteristic parameters, which can be applied to discriminate time-domain pulse information in clinical practice and assist doctors in diagnosis.