Publication Type:

Journal Article


Healthcare Technology Letters, Institution of Engineering and Technology, Volume 5, Number 3, p.81-87 (2018)



Acoustic noise, Automated analysis, Average sensitivities, Biomedical signal processing, Cardiology, Detection error rate, Evaluation results, High-frequency noise, Overall accuracies, Phonocardiography, Reliable recognition, Signal to noise ratio, Synchrosqueezing, Wavelet decomposition


Accurate and reliable recognition of fundamental heart sounds (FHSs) plays a significant role in automated analysis of heart sound (HS) patterns. This Letter presents an automated wavelet-based FHS recognition (WFHSR) method using morphological and interval features. The proposed method first performs the decomposition of phonocardiogram (PCG) signal using a synchrosqueezing wavelet transform to extract the HSs and suppresses the murmurs, low-frequency and high-frequency noises. The HS delineation (HSD) is presented using Shannnon energy envelope and amplitude-dependent thresholding rule. The FHS recognition (FHSR) is presented using interval, HS duration and envelope area features with a decision-rule algorithm. The performance of the method is evaluated on PASCAL HSs Challenge, PhysioNet/CinC HS Challenge, eGeneralMedical databases and real-time recorded PCG signals. Results show that the HSD approach achieves an average sensitivity (Se) of 98.87%, positive predictivity (Pp) of 97.50% with detection error rate of 3.67% for PCG signals with signal-to-noise ratio of 10 dB, and outperforms the existing HSD methods. The proposed FHSR method achieves a Se of 99.00%, Sp of 99.08% and overall accuracy of 99.04% on both normal and abnormal PCG signals. Evaluation results show that the proposed WFHSR method is able to accurately recognise the S1/S2 HSs in noisy real-world PCG recordings with murmurs and other abnormal sounds. © 2018 Institution of Engineering and Technology. All rights reserved.


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Cite this Research Publication

N. V. Varghees, Dr. K. I. Ramachandran, and Dr. Soman K. P., “Wavelet-based Fundamental Heart Sound Recognition Method using Morphological and Interval Features”, Healthcare Technology Letters, vol. 5, pp. 81-87, 2018.