Application research of pulse signal physiology and pathology feature mining in the field of disease diagnosis. Issue 10 (27th July 2022)

• Record Type: Journal Article
• Authors: Fan, Lin; Zhang, Jin Cheng; Wang, Zhongmin; Zhang, Xiaokang; Yao, Ruiling; Li, Yan
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 10(2022)
• Page Start: 1111
• Page End: 1124
• Publication Date: 2022-07-27
• Subjects: Pulse diagnosis -- signal processing -- decision tree -- SVM -- kernel method -- pulse wave analysis; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.2002306
• Languages: English
• ISSNs: 1025-5842

Abstract

ABSTRACT: 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.