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|Title:||Automatic Identification of S1 and S2 Heart Sounds Using Simultaneous PCG and PPG Recordings|
|Keywords:||multi-channel PCG segmentation|
S1 and S2 identification
variational mode decomposition
|Abstract:||Accurate and reliable identification of the first (S1) and second (S2) sounds of the phonocardiogram (PCG) is still a challenging task due to the presence of the S3 and S4, murmurs, high-pitched sounds, physiological interference, and other environmental noises. This paper presents an automated method for identification of the S1 and S2 sounds by simultaneously recording, processing, and fusing the extracted fiducial points of the PCG and photoplethysmogram (PPG) signals. The method consists of four main stages: the PCG and PPG signal sensing with Arduino interfacing, the heart sound delineation (HSD) and pulse waveform delineation (PWD) using variational mode decomposition (VMD), and the S1/S2 identification based on the estimated timings of the pulse onset, peak, upward slope zerocrossing, and downward slope zerocrossing of the pulsatile waveform. The accuracy and robustness of the HSD and PWD algorithms are evaluated using both the benchmark databases and simultaneous PCG and PPG recordings. Then, the proposed S1/S2 identification method is evaluated using the simultaneous PCG and PPG recordings. The VMD-based HSD algorithm achieves an average sensitivity (SE) = 99.80%, positive predictivity (PP) = 99.0%, and accuracy (ACC) = 98.81% whereas the Gaussian derivative filter (GDF)-based HSD and bandpass filter (BPF)-based HSD algorithms achieve the average SE = 99.75%, PP = 93.59%, and ACC = 93.37% and the average SE = 99.67%, PP = 84.97%, and ACC = 84.73%, respectively, for the recorded PCG signals with SNR value of 5 dB. The proposed S1/S2 identification method achieves a correct identification rate of 99.75% which outperforms the GDF+systole interval(SI)/diastole interval(DI) and the BPF+SI/DI feature-based methods having 66% and 59.25%, respectively, in the case of noisy recordings with a SNR value of 5 dB. The proposed method has great potential in improving the identification accuracy and robustness in the presence of other sounds and murmurs, and different kinds of environmental noises. � 2018 IEEE.|
|Appears in Collections:||Research Publications|
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