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Updated: Aug 7, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
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.
Abstract:
A sudden cardiac event in patients with heart disease can lead to a heart attack in extreme cases. Therefore, prompt interventions for the particular heart situation and periodic monitoring are critical. This study focuses on a heart sound analysis method that can be monitored daily using multimodal signals acquired with wearable devices. The dual deterministic model-based heart sound analysis is designed in a parallel structure that uses two bio-signals (PCG and PPG signals) related to the heartbeat, enabling more accurate heart sound identification. The experimental results show promising performance of the proposed Model III (DDM-HSA with window and envelope filter), which had the highest performance, and S1 and S2 showed average accuracy (unit: %) of 95.39 (±2.14) and 92.55 (±3.74), respectively. The findings of this study are anticipated to provide improved technology to detect heart sounds and analyze cardiac activities using only bio-signals that can be measured using wearable devices in a mobile environment.
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