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An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data.

Mohsen Nabian1,2, Yu Yin1, Jolie Wormwood3

  • 1Augmented Cognition LabElectrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA.

IEEE Journal of Translational Engineering in Health and Medicine
|November 17, 2018
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Summary

Researchers developed an open-source biosignal processing toolbox (Bio-SP tool) for analyzing physiological signals like ECG and blood pressure. This tool aids in health tracking and disease diagnosis by extracting relevant features semi-automatically.

Keywords:
Affective computingbiosignal processingblood pressure (BP)dimensionality reductionelectrocardiogram (ECG)electrodermal activity (EDA)electromyography (EMG)feature extractionhealth informaticsimpedance cardiography (ICG)machine learningpattern recognitionquality checking

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Area of Science:

  • Physiological signal processing
  • Affective computing
  • Digital health

Background:

  • Peripheral physiological signals (biosignals) are crucial in psychological studies and healthcare.
  • Applications include health tracking, sleep assessment, disease diagnosis, and understanding emotions.

Purpose of the Study:

  • To develop a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction.
  • To assist researchers in affective computing, digital health, and telemedicine.
  • To enable semi-automatic and reliable extraction of physiological patterns.

Main Methods:

  • Developed algorithms for signal-specific quality checking, artifact/noise filtering, and segmentation.
  • Incorporated features relevant to category discrimination in healthcare.
  • Created an open-source MATLAB software with a user-friendly GUI.

Main Results:

  • The Bio-SP tool successfully implements state-of-the-art algorithms for biosignal processing.
  • Features extracted are relevant for discriminating between disease and non-disease patterns.
  • The GUI is compatible with MathWorks Classification Learner for model development.

Conclusions:

  • The Bio-SP tool provides a reliable and accessible method for physiological signal analysis.
  • It supports researchers in extracting meaningful features for various healthcare applications.
  • The open-source nature and GUI facilitate future development and application in digital health and telemedicine.