Micro-piezoelectric pulse diagnoser and frequency domain analysis of human pulse signals

• Main Authors: Hung Chang, Jiaxu Chen, Yueyun Liu
• Format: Article
• Language: English
• Published: Elsevier 2018-01-01
• Series: Journal of Traditional Chinese Medical Sciences
• Subjects: Pulse diagnosis; Electronic pulse diagnoser; Standardization of pulse diagnosis in traditional Chinese medicine; Frequency spectrum; Fast Fourier transform
• Online Access: http://www.sciencedirect.com/science/article/pii/S2095754816302046

Background: The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse. However, pulses can vary markedly from person to person. Further, pulse diagnosis is difficult to learn and requires one-on-one teaching. Methods: To address this problem, we built a home-made pulse diagnoser and measured human pulses for studying the standardization of pulse diagnosis. Our pulse diagnoser was composed of a piezoelectric transducer, differential amplifier, data acquisition instrument, and a Matlab analysis program. The measured pulses were converted into electronic signals by a piezoelectric transducer and saved on a computer. The digitalized data were then refined and analyzed by fast Fourier transform for frequency analysis. Simulations were performed to assess the factors that affected the pulse, including phase shifts of reflected pulse waves (generate sub-peaks in pulses), inconsistent heart rates (deform pulse waves), the vessel stiffness (alter harmonic frequencies of the pulses), and the wave amplitudes (determined by the pulse strength). Results: By comparing a published report and our simulation findings, we characterized the pulse types and the effects of various factors, and then applied the findings to study actual pulses in patients. Three types of pulses were determined from the frequency spectrum—choppy pulse (Se mai) without apparent harmonic peaks, the harmonic frequencies of wiry pulse (Xian mai) that are non-integer multiples of the fundamental frequency, and surging pulse (Hong mai) that exhibit strong amplitudes in the spectrum of frequency. A normal pulse and a slippery pulse were differentiated by a phase shift, but not by assessing the frequency spectrum. Conclusion: These findings confirm that frequency domain analysis can avoid ambiguity arising in assessing the three types of pulses in the time domain. Further studies of other pulses in the frequency domain are required to develop a precise electronic pulse diagnoser.