Key result
A two-channel heart sound segmentation framework using PCG and pulse signals achieved 100% identification accuracy in distinguishing S1 and S2 under clean and noisy conditions.
A novel two-channel framework using PCG and pulse signals accurately segments heart sounds and distinguishes S1 and S2 under both clean and noisy conditions.
Enables reliable automated S1/S2 segmentation in noisy environments; leaves open prospective clinical validation before diagnostic adoption.
Phonocardiogram (PCG) segmentation is the crucial first step in automated heart sound analysis and diagnostic systems. Recently, the cardiac signals (including, electrocardiogram, phonocardiogram and photoplethysmogram) are simultaneously recorded for most cardiac signal processing applications such as cardiovascular diagnostic system, biometric authentication, and emotion/stress recognition. In this paper, we present an effective two-channel heart sound segmentation framework using PCG and pulse signals. The proposed framework comprises the steps of: heart sound signal decomposition using stationary wavelet transform, Shannon entropy envelope extraction, heart sound endpoint determination, systolic peak detection, and heart sound discrimination. The proposed framework is tested and validated using the simultaneously recorded heart sound and pulse signals. Performance evaluation results demonstrate that the proposed heart sound endpoint and systolic peak detection methods can achieves an average Se of 98.98%, +P of 96.80% and Se of 99.57%, +P of 99.37%, respectively. The proposed framework achieves an identification accuracy of 100% in distinguishing the first heart sound (S1) and second heart sound (S2) under clean and noisy signal conditions.
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Varghees et al. (2016) studied Heart sound segmentation. Two-channel heart sound segmentation framework using PCG and pulse signals was evaluated on Heart sound endpoint and systolic peak detection performance (Sensitivity and Positive Predictivity). A two-channel heart sound segmentation framework using PCG and pulse signals achieved 100% identification accuracy in distinguishing S1 and S2 under clean and noisy conditions.
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