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Related Experiment Video

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

Zhichao Zhang1, Yuan Zhang1, Lina Yao2

  • 1Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China.

Journal of Biomedical Informatics
|February 12, 2018
PubMed
Summary

This study introduces a novel wrist pulse signal processing method inspired by Jin's pulse diagnosis (JPD) for lung cancer detection. The method achieved 78.13% accuracy in distinguishing lung cancer patients from healthy individuals.

Keywords:
Cubic support vector machine (CSVM)Feature extractionIterative sliding window (ISW)Jin’s pulse diagnosis (JPD)Lung cancer recognitionPulse signal processing and analysis

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Area of Science:

  • Biomedical Signal Processing
  • Traditional Chinese Medicine
  • Oncology

Background:

  • Pulse diagnosis offers a non-invasive method for health assessment.
  • Jin's pulse diagnosis (JPD) is a validated technique, but its application in lung cancer detection via signal processing remains underdeveloped.
  • A standardized set of pulse features for JPD in lung cancer recognition is currently lacking.

Purpose of the Study:

  • To propose a novel wrist pulse signal processing method for convenient at-home lung cancer care.
  • To develop a method for classifying lung cancer patients based on JPD insights.
  • To establish standard JPD pulse features for biomedical signal analysis.

Main Methods:

  • An iterative slide window (ISW) algorithm was developed to segment de-noised wrist pulse signals into individual periods.
  • Characteristics of segmented pulse waveforms were analyzed.
  • Twenty-six novel features were identified and used to classify pulse waveforms from healthy individuals and lung cancer patients.

Main Results:

  • A classification accuracy of 78.13% was achieved using a cubic support vector machine (CSVM).
  • The proposed method successfully differentiated pulse waveforms between healthy and lung cancer groups.
  • Novel JPD pulse features were defined and utilized for classification.

Conclusions:

  • The developed JPD-inspired wrist pulse signal processing method shows promise for lung cancer detection.
  • This approach could facilitate convenient remote health monitoring and care for lung cancer patients.
  • Further research is warranted to refine the method and improve diagnostic accuracy.