Pulse Wave Measuring Sensor System via the Average Magnitude Difference Function Entropy for Classifying Cardiac Conditions

The formation and changes of the pulse wave are affected by the functional status of the heart, blood, and arterial vessels. The pulse signal covers pathological information in the cardiovascular system, and the doctor of traditional Chinese medicine (TCM) can diagnose diseases by feeling the pulse. The diagnosis of diseases through feeling the pulse of TCM primarily relies on the doctor's feelings and subjective experience, and it lacks objectified data. In this work, pulse signals were collected digitally by designing pulse sensors and pulse acquisition systems in accordance with the feel pulse habits of TCM. The pulse rate variability (PRV) was used to characterize the functional state of the heart. The average magnitude difference function (AMDF) fuzzy entropy method was proposed to investigate the change in pulse rate. A total of 77 volunteers with different heart health conditions were recruited for the collection of the pulse signal. Results showed that the AMDF fuzzy entropy can distinguish clearly among knotted pulse, regular intermittent pulse, and normal pulse. Therefore, this method has potential application value to realize cardiac-function state analysis and abnormal recognition.