Analyzing Wrist Pulse Signals Measured with Polyvinylidene Fluoride Film for Hypertension Identification

Abstract

The human wrist pulse signal, which often exhibits nonstationarity, can reflect changes in mechanisms and pathophysiology in the blood and viscera. In this paper, we present an integrated approach for identifying hypertension using the wavelet packet transform (WPT) and continuous hidden Markov models (HMM) to analyze wrist pulse signals. The approach starts with decomposing the wrist pulse signals into a number of frequency sub-bands through the WPT. Then the local discriminant bases (LDB) algorithm is used to obtain the best representation of the wrist pulse signal in the optimal frequency sub-bands. After that, energy features are extracted from those sub-bands and the optimal features associated with corresponding sub-bands are selected using the Fisher linear discriminant criterion. The optimal features are subsequently used as input to a continuous HMM classifier for hypertension identification. Experimental results indicate that the presented approach can differentiate the hypertensive wrist pulses from healthy wrist pulses effectively. In addition, when compared with other classifiers, the results demonstrate that the continuous HMM classifier yields a better classification result.

The Association of Radial Artery Pulse Wave Variables with the Pulse Wave Velocity and Echocardiographic Parameters in Hypertension

“…Pulse condition is a state of pulse throbbing which described the feeling of a finger's palpation. For the blood vessels of human run through the whole body to connect both the internal organs and the external skins, it often provides comprehensive information on the visceral functions, qi and blood, Yin and Yang, which can be used for diagnostic purposes [23]. Pulse diagnosis, as one of the four classical methods (inspection, auscultation and olfaction, inquiry and palpation) in traditional Chinese medicine, plays an important role in the diagnosis of diseases.…”

“…Besides, pulse wave variables were associated with different Sasang constitution [21] and quality of life [22] in hypertension patients. The prediction models of hypertension based on the radial artery pulse images have been studied and they achieved an accuracy of about 80% by related machine learning methods [4, 23].…”