Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 79, Issue -, Pages 107-116Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2018.01.009
Keywords
Lung cancer recognition; Pulse signal processing and analysis; Iterative sliding window (ISW); Feature extraction; Jin's pulse diagnosis (JPD); Cubic support vector machine (CSVM)
Funding
- National Natural Science Foundation of China [61572231]
- Shandong Provincial Key Research & Development Project [2017GGX10141]
- Slovenian Research Agency within the research program Algorithms and Optimization Methods in Telecommunications
- Embry-Riddle Aeronautical University
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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.
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