Key result
An automated PCG signal delineation method achieved an average sensitivity of 99.56% and positive predictivity of 96.97% for heart sound analysis.
The proposed automated PCG signal delineation method demonstrates high sensitivity and positive predictivity for identifying heart sound components, enabling potential real-time cardiac health monitoring.
This paper presents an automated PCG signal delineation method for real-time wireless cardiac health monitoring and audio-visual stethoscope applications. The proposed method comprises the steps of: preprocessing, total variation filtering, smooth energy envelope computation, peak-amplitude determination and endpoint determination. The total variation filter is used to smooth out the background noises and preserves the heart sound components. The endpoints of heart sounds (S1, S2, S3, S4 and murmurs) are determined by using smooth energy envelope and hard-thresholding rule. The peaks of the heart sounds are determined by using candidate waveform obtained from Hilbert transformation of smooth envelope. The proposed delineation method is validated by using different clean and noisy pathological and non-pathological PCG signals. Experiments on a large PCG database show that the proposed method achieves an average sensitivity (Se) of 99.56% and positive predictivity (+P) of 96.97%, with the maximum average delineation error of 9.08-msec, 0.483-msec and 7.834-msec for endpoints of heart sounds, peak-locations and durations, respectively.
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Varghees et al. (2014) studied Cardiac health monitoring. Automated PCG signal delineation method was evaluated on Average sensitivity (Se) and positive predictivity (+P). An automated PCG signal delineation method achieved an average sensitivity of 99.56% and positive predictivity of 96.97% for heart sound analysis.
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