Classification of Pulse Waveforms Using Edit Distance with Real Penalty

Zhang, Dongyu; Zuo, Wangmeng; Zhang, David; Zhang, Hongzhi; Li, Naimin

doi:10.1155/2010/303140

Cited by 33 publications

(7 citation statements)

References 23 publications

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“…Classification models for primary pulse qualities (PQs) have recently been developed to meet increasing requests for reliable pulse tonometric devices [1, 8, 11–15]. An important achievement of such efforts was the development of two clinically verified classification models for the floating and sunken PQs, both of which are based on the pulse amplitude variation as a function of contact pressure (CP).…”

Section: Discussionmentioning

confidence: 99%

New Assessment Model of Pulse Depth Based on Sensor Displacement in Pulse Diagnostic Devices

Bae

Jeon

Kim

et al. 2013

Evidence-Based Complementary and Alternative Medicine

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An accurate assessment of the pulse depth in pulse diagnosis is vital to determine the floating and sunken pulse qualities (PQs), which are two of the four most basic PQs. In this work, we proposed a novel model of assessing the pulse depth based on sensor displacement (SD) normal to the skin surface and compared this model with two previous models which assessed the pulse depth using contact pressure (CP). In contrast to conventional stepwise CP variation tonometry, we applied a continuously evolving tonometric mechanism at a constant velocity and defined the pulse depth index as the optimal SD where the largest pulse amplitude was observed. By calculating the pulse depth index for 18 volunteers, we showed that the pulse was deepest at Cheok (significance level: P < 0.01), while no significant difference was found between Chon and Gwan. In contrast, the two CP-based models estimated that the pulse was shallowest at Gwan (P < 0.05). For the repeated measures, the new SD-based model showed a smaller coefficient of variation (CV ≈ 7.6%) than the two CP-based models (CV ≈ 13.5% and 12.3%, resp.). The SD-based pulse depth assessment is not sensitive to the complex geometry around the palpation locations and temperature variation of contact sensors, which allows cost-effective sensor technology.

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Section: Discussionmentioning

confidence: 99%

New Assessment Model of Pulse Depth Based on Sensor Displacement in Pulse Diagnostic Devices

Bae

Jeon

Kim

et al. 2013

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…To compare the classification accuracy of different classifiers, the artificial neural network (ANN) classifier, support vector machine (SVM), and k-nearest neighbor (KNN) classifier are to class the wrist pulse signals instead of the HMM classifier. (41)(42)(43) Classification results are shown in Table 5.…”

Section: Comparison With Other Classifiersmentioning

confidence: 99%

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

2017

Sensors and Materials

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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.

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“…As for the pulse waveform type classification, Zhang et al [ 71 ] proposed two novel k nearest neighbor-based approaches using edit distance with real penalty. Then, those methods were applied to recognize five pulse patterns, including moderate, smooth, taut, hollow, and unsmooth.…”

Section: Machine Learning Approaches For Tcm Patient Classificatiomentioning

confidence: 99%

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Zhao

Wang

et al. 2015

Evidence-Based Complementary and Alternative Medicine

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As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

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Classification of Pulse Waveforms Using Edit Distance with Real Penalty

Cited by 33 publications

References 23 publications

New Assessment Model of Pulse Depth Based on Sensor Displacement in Pulse Diagnostic Devices

New Assessment Model of Pulse Depth Based on Sensor Displacement in Pulse Diagnostic Devices

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

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

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