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A Spectral-Based Approach for BCG Signal Content Classification.

Mohamed Chiheb Ben Nasr1, Sofia Ben Jebara1, Samuel Otis2

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This study introduces a novel method to identify useful Ballistocardiogram (BCG) signal frames for accurate cardiac and respiratory rate measurements. The approach effectively segments BCG signals based on human body activities, achieving high accuracy.

Keywords:
Ballistocardiogram signalconnected mattresshuman activities classificationsignal segmentationspectral features

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Physiological Monitoring

Background:

  • Ballistocardiogram (BCG) signals are crucial for non-invasive vital sign measurement.
  • Human body activities frequently interfere with BCG signal quality, complicating heart rate (HR) and respiratory rate (RR) extraction.
  • Accurate segmentation of BCG signals based on activity is needed to improve vital sign measurement reliability.

Purpose of the Study:

  • To develop binary flags identifying BCG signal frames suitable for HR and RR measurement.
  • To achieve refined BCG signal segmentation by classifying various human body activities.
  • To enhance the accuracy and reliability of vital sign extraction from BCG signals.

Main Methods:

  • Utilized unsupervised Gaussian Mixture Model (GMM) classification for BCG signal exploration and hyper-parameter definition.
  • Employed supervised K-Nearest Neighbors (KNN) classification for frame-level and temporal series analysis of spectral features (SFM, SC).
  • Classified human body activities (e.g., coughing, breath holding, movement) from BCG signals using two-level supervised classification.

Main Results:

  • Successfully generated binary flags to indicate useful BCG signal frames for vital sign measurement.
  • Achieved high accuracy (94.6%) in segmenting BCG signals according to distinct human body activities.
  • Demonstrated the effectiveness of spectral features (SFM, SC) and temporal series analysis for activity classification.

Conclusions:

  • The proposed framework offers a novel and powerful method for BCG signal segmentation based on human body activities.
  • This approach significantly improves the potential for reliable cardiac and respiratory rate measurement from BCG signals.
  • The developed technique addresses a key challenge in BCG signal processing, paving the way for more robust vital sign monitoring.