Unsupervised segmentation of heart sounds from abrupt changes detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an unsupervised method to segment heart sounds and silences from cardiograms by detecting abrupt signal changes. The algorithm reliably identifies heart states, aiding in cardiovascular disease diagnosis.
Area Of Science
- Biomedical Engineering
- Signal Processing
- Cardiology
Background
- Early diagnosis of cardiovascular diseases relies on heart auscultation.
- Automated segmentation of cardiograms into heart states aids physician analysis.
- Unsupervised segmentation of heart sounds and silences is crucial for effective interpretation.
Purpose Of The Study
- To propose an unsupervised method for segmenting heart sounds and silences in cardiograms.
- To detect abrupt signal changes for identifying the beginning and end of heart sounds.
- To improve the physiological interpretation of heart sound recordings.
Main Methods
- Localizing abrupt signal changes corresponding to heart sound boundaries.
- Calculating signal power within intervals defined by change points to identify sounds and silences.
- Adjusting algorithm parameters based solely on estimated heart rate.
Main Results
- The method was evaluated on three independent databases (PhysioNet 2016, CirCor DigiScope, PASCAL).
- Achieved high mean F1 scores: 91.2%, 94.3%, and 96.3% across databases.
- Outperformed most competing unsupervised approaches in detection performance.
Conclusions
- The proposed algorithm is reliable, robust, and easy to implement.
- Demonstrates top-ranking detection performance across diverse heart sound databases.
- Offers a simple and effective alternative for heart sound segmentation.
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