DDM-HSA: Dual Deterministic Model-Based Heart Sound Analysis for Daily Life Monitoring

Miran Lee1, Qun Wei2,3, Soomin Lee4

  • 1Department of Computer and Information Engineering, Daegu University, Kyeongsan 38453, Republic of Korea.

Insights

This study introduces a new heart sound analysis method using wearable devices. It accurately identifies heart sounds using dual bio-signals for improved cardiac monitoring.

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Sudden cardiac events in heart disease patients necessitate prompt intervention and continuous monitoring.
  • Wearable devices offer a platform for daily, non-invasive patient monitoring.

Purpose of the Study:

  • To develop and validate a heart sound analysis method using multimodal signals from wearable devices.
  • To enhance the accuracy of heart sound identification for early cardiac event detection.

Main Methods:

  • A dual deterministic model-based heart sound analysis (DDM-HSA) was employed.
  • The method utilized parallel processing of phonocardiogram (PCG) and photoplethysmogram (PPG) bio-signals.
  • Model III incorporated window and envelope filtering for signal refinement.

Main Results:

  • The proposed Model III (DDM-HSA with window and envelope filter) demonstrated high performance.
  • Average accuracy for S1 heart sound identification was 95.39% (±2.14).
  • Average accuracy for S2 heart sound identification was 92.55% (±3.74).

Conclusions:

  • The DDM-HSA method shows significant promise for accurate heart sound analysis.
  • This technology enables improved cardiac activity monitoring using readily available bio-signals from wearable devices.
  • The findings support the development of advanced mobile health solutions for cardiovascular care.

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