Application research of pulse signal physiology and pathology feature mining in the field of disease diagnosis

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 25; no. 10; pp. 1111 - 1124
• Main Authors: Fan, Lin, Zhang, Jin Cheng, Wang, Zhongmin, Zhang, Xiaokang, Yao, Ruiling, Li, Yan
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 27.07.2022; Taylor & Francis Ltd
• Subjects: Algorithms; Chronic illnesses; decision tree; Decision trees; Diagnosis; Disease detection; Feature extraction; Herbal medicine; Kernel functions; kernel method; Photoelectricity; Physiology; Pulse diagnosis; pulse wave analysis; signal processing; Support vector machines; SVM; Traditional Chinese medicine; kernel method; Pulse diagnosis; decision tree; SVM; signal processing; pulse wave analysis
• ISSN: 1025-5842; 1476-8259; 1476-8259
• DOI: 10.1080/10255842.2021.2002306

This experiment is based on the principle of traditional Chinese medicine (TCM) pulse diagnosis, the human pulse signal collected by the sensor is organized into a dataset, and the algorithms are designed to apply feature extraction. After denoising, smoothing and eliminating baseline drift of the photoelectric sensors pulse data of several groups of subjects, we designed three algorithms to describe the difference between the two-dimensional images of the pulse data of normal people and patients with chronic diseases. Convert the calculated feature values into multi-dimensional arrays, enter the decision tree (DT) to balance the differences in human physiological conditions, then train in the support vector machine kernel method (SVM-KM) classifier. Experimental results show that the application of these feature mining algorithms to disease detection greatly improves the reliability of TCM diagnosis.