A Dual Classifier-Regressor Architecture for Heart Sound Onset/Offset Detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new method to precisely identify the start and end of first (S1) and second (S2) heart sounds in phonocardiogram (PCG) signals using electrocardiogram (ECG) data. The approach significantly improves the accuracy of cardiac condition diagnosis.
Area Of Science
- Cardiology
- Biomedical Engineering
- Signal Processing
Background
- Accurate identification of heart sounds (S1 and S2) from phonocardiogram (PCG) signals is crucial for diagnosing various cardiac conditions.
- Deep learning models, particularly those inspired by image segmentation and aided by synchronized electrocardiograms (ECG), have shown potential for PCG analysis.
- Existing methods often focus on point-wise segmentation, but identifying the precise onset and offset of heart sounds remains a challenge.
Purpose Of The Study
- To develop and evaluate a novel method for identifying the onset and offset of the first (S1) and second (S2) heart sounds in PCG signals.
- To leverage synchronized ECG signals and their keypoints to enhance the accuracy of heart sound detection.
- To shift the focus from point-wise segmentation to the precise localization of heart sound transitions.
Main Methods
- A joint classifier-regressor architecture was proposed to predict both the probability and precise location of S1 and S2 sound onsets and offsets.
- The method incorporates synchronized ECG signals and their derived keypoints to improve the detection of heart sounds within the PCG signal.
- The model was trained and evaluated on the PhysioNet/CinC 2016 dataset, the largest publicly available dataset for this task.
Main Results
- The proposed approach achieved state-of-the-art performance on the PhysioNet/CinC 2016 dataset.
- Sensitivity of 0.97 and a positive predictive value of 0.98 were obtained for identifying the midpoints of S1 and S2 segments.
- The method accurately identified the onset/offset locations of heart sounds with a mean error of 11.11 ms.
Conclusions
- Identifying the transitions (onset/offset) of heart sounds simplifies the analysis, leading to improved model training and inference.
- The developed method offers a significant advancement in the automated analysis of PCG signals for cardiac diagnostics.
- The approach has potential for adaptation to locate regions of interest in other physiological signals like respiration or blood pressure.
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