Accurate cirrhosis identification with wrist-pulse data for mobile healthcare

Gong, Shujie; Xu, Bin; Sun, Guodong; Chen, Mingrui; Wang, Nanyue; Dong, Chiyu; Wang, Peng; Cui, Jian

doi:10.1145/2396276.2396283

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…Experimental results manifested that the introduced method is superior to other time series matching approaches. A mobile healthcare system was developed in [ 81 ] for cirrhosis diagnosis including signal denoising and baseline wander removal for wrist-pulse preprocessing; the proposed feature extraction algorithm is called binning method and k nearest neighbor classifier. Sun et al [ 82 ] focused on the feature extraction for pulse analysis using kernel PCA and five diseases classification using k -NN.…”

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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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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“…1) Time domain feature extraction, 2) Time-frequency feature extraction, 3) The curve fitting method [8] , 4) The dimension reduction technique (DRT), 5) Frequency domain feature extraction [4] [5] .…”

Section: Feature Extractionmentioning

confidence: 99%

“…The curve fitting may over-fit the data. The key idea of Dimension Reduction Technique (DRT) is to represent the high-dimensional raw data on an intrinsic low dimensional space, but it works with more complexity [4] .…”

Section: Feature Extractionmentioning

confidence: 99%

Wrist Pulse Classification System for Healthy and Unhealthy Subjects

Parikh¹,

Thakker²

2015

IJCA

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According to Ayurveda, human health status can be measured by using three fingers as 'VATA', 'PITTA' and 'KAPHA' called Tridosha. This is very effective method to know the status of human body. But for this type of diagnosis practitioner requires long term training. Also the diagnoses from practitioner often deviate greatly due to their subjective experience. Thus, it is highly required to design such a system which diagnoses human health status objectively not subjectively.In this paper, design of an embedded system, which classifies the health status, is discussed. This system first convert time domain signal into frequency domain using Fast Fourier Transform (FFT). Using Band Energy Ratio (BER) feature vector is retrieved from frequency domain signal. This feature vector is applied to the linear as well as quadratic classifier. This will classify the human health in a group of two as healthy or unhealthy.

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“…e analysis of the radial pulse waves is regarded as an important approach to evaluate patient status in traditional Chinese medicine (TCM) [1][2][3]. GUAN is a segment of the radial artery above the radial styloid process and a critical position for collecting pulse wave correctly in TCM diagnosis [4][5][6][7][8]. However, current methods for locating GUAN, including tactile sense and pressure sensor array, could not achieve precise positioning [5,6,[9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Noncontact Method for Locating Radial Artery above Radial Styloid Process in Thermal Image

Geng

Liu

Zhang

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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A radial artery above the radial styloid process is called GUAN and is a critical position for collecting pulse wave in traditional Chinese medicine theory. Locating GUAN is a precondition for collecting radial pulse wave. However, existing methods for locating GUAN lead to large deviations. This paper proposes a novel nontouch method for locating GUAN based on thermal imaging and image processing. This method consists of three parts: the infrared thermal imaging location imaging platform, the wrist edge contour extraction algorithm based on arbitrary angle edge recognition, and radial protrusion recognition algorithm (x coordinate identification algorithm of GUAN) and radial artery fitting algorithm (y coordinate identification algorithm of GUAN). The infrared thermal imaging positioning imaging platform is used to ensure that the wrist of the subject enters the fixed imaging area in a fixed position during each measurement and transmits the thermal imaging images carrying the image information of radial processes and radial arteries to the upper computer. Arbitrary angle edge recognition algorithm is used to extract wrist contour and radial artery edge information. The x-axis coordinates of the radial artery were provided by the identification algorithm, and the y-axis coordinates of the radial artery were provided by the fitting algorithm. Finally, the x and y coordinates determine the GUAN position. The algorithm for locating GUAN could provide repeatable and reliable x and y coordinates. The proposed method shows that relative standard deviation (RSD) of x distance of GUAN is less than 9.0% and RSD of y distance of GUAN is less than 5.0%. The proposed method could provide valid GUAN coordinates and reduce deviations of locating GUAN.

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Accurate cirrhosis identification with wrist-pulse data for mobile healthcare

Cited by 9 publications

References 20 publications

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

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

Wrist Pulse Classification System for Healthy and Unhealthy Subjects

A Noncontact Method for Locating Radial Artery above Radial Styloid Process in Thermal Image

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