A Practical Approach to Wrist Pulse Segmentation and Single-period Average Waveform Estimation

Xia, Chunming; Li, Yan; Yan, Jianjun; Wang, Yiqin; Yan, Hong‐Bin; Guo, Rui; Li, Fufeng

doi:10.1109/bmei.2008.140

Cited by 34 publications

(13 citation statements)

References 1 publication

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“…2 that the wrist pulse signal is not a random process but a cyclic wave with regularly occurring systolic and diastolic waves, which is confirmed in [21]. In this study, a sampled pulse signal is segmented into single-period waveforms for further analysis.…”

Section: Wrist Pulse Signal Collection and Pre-processingmentioning

confidence: 62%

Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification

Chen

Zhang

et al. 2009

Medical Engineering & Physics

110

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Section: Wrist Pulse Signal Collection and Pre-processingmentioning

confidence: 62%

Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification

Chen

Zhang

et al. 2009

Medical Engineering & Physics

110

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“…The aim is to segment the complete wrist pulse time series into single period waves [15]. An amplitude threshold is chosen manually by inspection.…”

Section: Preprocessing Of Wrist Pulse Signalsmentioning

confidence: 99%

Study of wrist pulse signals using time domain spatial features

Rangaprakash

Dutt

2015

Computers & Electrical Engineering

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“…Due to the page limitation, these would not be described here. Detailed information on segmentation and single-period average waveform estimation can be found in [11].…”

Section: ) Segmentation and Average Waveform Estimationmentioning

confidence: 99%

An improved approach to the classification of seven common TCM pulse conditions

Xia

Wang

et al. 2011

2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)

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Pulse diagnosis with finger pulse-taking is popular in Chinese culture. Wrist pulse waveform analysis has becoming common in Traditional Chinese Medicine (TCM) engineering and diagnosis modernization. An improved two-step classification method is proposed in this paper to differentiate seven common TCM pulse conditions, include four mono and three concurrent pulses. For both time-domain and frequencydomain feature-based patterns, a total of ten effective discrimination functions (five for each domain ) are trained and tested for majority-rule based voting analysis. Case studies based on both basic one-step and improved two-step methods are given. Results show that the overall classification performance has been improved from 52% to 57% by introducing two-step method, and four out of seven individual classification accuracies are also improved.

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A Practical Approach to Wrist Pulse Segmentation and Single-period Average Waveform Estimation

Cited by 34 publications

References 1 publication

Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification

Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification

Study of wrist pulse signals using time domain spatial features

An improved approach to the classification of seven common TCM pulse conditions

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