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Journal of biomedical informatics v.79, 2018년, pp.107 - 116   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

A sensor-based wrist pulse signal processing and lung cancer recognition

Zhang, Zhichao (Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China ) ; Zhang, Yuan (Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China ) ; Yao, Lina (School of Computer Science and Engineering, University of New South Wales, Australia ) ; Song, Houbing (Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, USA ) ; Kos, Anton (Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia ) ;
  • 초록  

    Abstract Pulse diagnosis is an efficient method in traditional Chinese medicine for detecting the health status of a person in a non-invasive and convenient way. Jin’s pulse diagnosis (JPD) is a very efficient recent development that is gradually recognized and well validated by the medical community in recent years. However, no acceptable results have been achieved for lung cancer recognition in the field of biomedical signal processing using JPD. More so, there is no standard JPD pulse feature defined with respect to pulse signals. Our work is designed mainly for care giving service conveniently at home to the people having lung cancer by proposing a novel wrist pulse signal processing method, having an insight from JPD. We developed an iterative slide window (ISW) algorithm to segment the de-noised signal into single periods. We analyzed the characteristics of the segmented pulse waveform and for the first time summarized 26 features to classify the pulse waveforms of healthy individuals and lung cancer patients using a cubic support vector machine (CSVM). The result achieved by the proposed method is found to be 78.13% accurate. Highlights A novel iterative sliding window (ISW) algorithm for baseline wanders removal is proposed. Twenty-six features are extracted at the first time by having an insight on JPD theory. Distinguish the pulse waveform of lung cancer patients from that of healthy individuals using CSVM. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Lung cancer recognition .   Pulse signal processing and analysis .   Iterative sliding window (ISW) .   Feature extraction .   Jin's pulse diagnosis (JPD) .   Cubic support vector machine (CSVM).  

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