Wavelet Decomposition and Feature Extraction from Pulse Signals of the Radial Artery

Sareen, M.; Abhinav, .; Prakash, P.; Anand, Sneh

doi:10.1109/icacte.2008.66

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…They are time-domain features [13], frequencydomain features [14] and Hemodynamic features [1]. In fact, these time-domain features which are related with superior wave, predicrotic wave and dicrotic wave are the most important.…”

Section: Related Workmentioning

confidence: 98%

PDhms: Pulse Diagnosis via Wearable Healthcare Sensor Network

Gong¹,

Lu²,

Wang³

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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Pulse Diagnosis Theory (PDT) has the advantages of non-invasive treatment and disease prevention. Combining these merits with Wireless Sensor Network(WSN), we propose a novel networked low-cost and wearable healthcare monitoring system, namely PDhms, for pulse data collection, pulse analysis and pulse diagnosis. Some practical challenges still exist in PDhms such as exerting appropriate pressure on a human radial artery, overcoming seriously limited resources, improving low Signal to Noise Ratio(SNR) and conducting resistance of interference. To address these challenges, we present a robust external pressure control algorithm for pulse data collection, and propose FEA, a novel light-weight and adaptive feature extraction algorithm for sensed pulse data. We conduct the large-scale pulse data collection experiments of 1356 pulse samples, the comparison experiment between the FEA and the typical derivative-based algorithm, as well as pulse diagnosis experiments based on Support Vector Machine(SVM). Experimental results show that PDhms is a valuable solution for low-cost wearable healthcare monitoring system.

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Section: Related Workmentioning

confidence: 98%

PDhms: Pulse Diagnosis via Wearable Healthcare Sensor Network

Gong¹,

Lu²,

Wang³

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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“…The wavelet energy and wavelet entropy based on the wavelet coefficients can quantify characteristics of cardiovascular system [41]. Sareen et al extracted the different frequency components of the radial pulse wave through wavelet transform and showed that the feature parameters extracted by wavelet transform were helpful to analyze the variability of the radial pulse wave [42].…”

Section: ) Time-frequency Joint Characteristicsmentioning

confidence: 99%

“…The purpose of feature extraction is to analyze the information represented by radial pulse waves. The methods include time-domain analysis [5,6,31,39], frequency-domain analysis [11,40], time-frequency joint analysis [13,15,[41][42][43][44], nonlinear dynamics analysis [29,[45][46][47][48], and feature fusion analysis [22,49]. The features extracted by these methods can assist in improving methods for diagnosis or treatment of CVD.…”

mentioning

confidence: 99%

Analysis of the Radial Pulse Wave and its Clinical Applications: A Survey

Yang

Liu

et al. 2021

IEEE Access

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“…This kind of method is able to be implemented on sensor nodes, but it is only suitable for regular pulse waveforms with high SNR. Wrist-pulse feature extraction methods [16,28,37,41] for irregular pulse waveforms with low SNR are performed on computers, but these methods are not Figure 1. Architecture of EasiCPRS system.…”

Section: Related Workmentioning

confidence: 99%

EasiCPRS

Zhang

Wang

et al. 2011

Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems

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Traditional Chinese Pulse Diagnosis is a convenient and noninvasive method for disease diagnosis and healthcare. We have designed and implemented a Chinese wrist-pulse retrieval system based on the principle of Traditional Chinese Pulse Diagnosis (TCPD), called EasiCPRS. It is designed to be small in size, low in cost, with flexibility in deployment, and simplicity in operation. The contributions of this work are: 1. The wrist-pulse at "cun, guan and chi"points over the radial artery are obtained by applying a moderate and adjustable taking pressure during wrist-pulse retrieval. 2. A wrist-pulse signal conditioning circuit and a robust external taking pressure control algorithm are designed to overcome low signal-to-noise ratio (SNR). 3. A lightweight algorithm for wrist-pulse feature extraction is achieved on a resourceconstrained platform to economize energy and bandwidth.We developed EasiCPRS prototype, trained and verified the performance of the system by collecting and analyzing thousands of wrist-pulse samples from volunteers in a number of different health conditions such as hypertension, pregnancy and so on which were diagnosed by doctors in hospital. The experimental results showed potential usefulness of the system in disease diagnosis and healthcare.

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Wavelet Decomposition and Feature Extraction from Pulse Signals of the Radial Artery

Cited by 19 publications

References 9 publications

PDhms: Pulse Diagnosis via Wearable Healthcare Sensor Network

PDhms: Pulse Diagnosis via Wearable Healthcare Sensor Network

Analysis of the Radial Pulse Wave and its Clinical Applications: A Survey

EasiCPRS

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