1. Wavelet-based fundamental heart sound recognition method using morphological and interval features
- Author
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V. Nivitha Varghees, K.I. Ramachandran, and K.P. Soman
- Subjects
medical signal processing ,phonocardiography ,wavelet transforms ,feature extraction ,eGeneralMedical databases ,PhysioNet/CinC HS Challenge ,PASCAL HSs Challenge ,decision-rule algorithm ,amplitude-dependent thresholding rule ,Shannnon energy envelope ,HS delineation ,high-frequency noises ,low-frequency noises ,murmurs ,synchrosqueezing wavelet transform ,PCG signal ,phonocardiogram ,WFHSR method ,HS patterns ,interval features ,morphological features ,wavelet-based fundamental heart sound recognition method ,Medical technology ,R855-855.5 - Abstract
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.
- Published
- 2018
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