IEEE transactions on bio-medical engineering · 2026
A Dual Classifier-Regressor Architecture for Heart Sound Onset/Offset Detection.
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Somarathne Pamuditha, Herath Sandun, Gargiulo Gaetano, Breen Paul, Anderson Neil, Yao Yu, Liu Tongliang, Withana Anusha
Аннотация
OBJECTIVE: Identifying the first and second heart sounds (S1 and S2) from phonocardiogram (PCG) signals is an essential step in automating the diagnosis of cardiac conditions such as irregular heartbeat, valve misfunctions, and heart failure. Recent research inspired by image segmentation has shown promise in utilising deep neural networks for point-wise PCG segmentation with the support of synchronised electrocardiogram (ECG). This paper shifts the focus from point-wise segmentation to identifying the onset/offset of S1 and S2 heart sounds in the PCG signal.
METHODS: We incorporate the ECG signal and its keypoints to improve the detection of the heart sounds. Our proposed method employs a joint classifier-regressor architecture for predicting the probability and the location of transitions in the PCG signal.
RESULTS: When evaluated on the largest publicly available PhysioNet/CinC 2016 dataset, the proposed approach outperforms the existing state-of-the-art methods, achieving a sensitivity of 0.98 and a positive predictive value of 0.98 in identifying midpoints of S1 and S2 segments. It also identifies the onset/offset locations with an 11.11 ms error.
CONCLUSION: It is evident that identifying the transitions simplifies, leading to better training and inference.
SIGNIFICANCE: In addition to achieving state-of-the-art results, this proposed approach could also be adapted to other physiological signals, such as respiration, blood pressure, or muscle activity, for locating regions of activity/interest.
METHODS: We incorporate the ECG signal and its keypoints to improve the detection of the heart sounds. Our proposed method employs a joint classifier-regressor architecture for predicting the probability and the location of transitions in the PCG signal.
RESULTS: When evaluated on the largest publicly available PhysioNet/CinC 2016 dataset, the proposed approach outperforms the existing state-of-the-art methods, achieving a sensitivity of 0.98 and a positive predictive value of 0.98 in identifying midpoints of S1 and S2 segments. It also identifies the onset/offset locations with an 11.11 ms error.
CONCLUSION: It is evident that identifying the transitions simplifies, leading to better training and inference.
SIGNIFICANCE: In addition to achieving state-of-the-art results, this proposed approach could also be adapted to other physiological signals, such as respiration, blood pressure, or muscle activity, for locating regions of activity/interest.