Wavelet-based fundamental heart sound recognition method using morphological and interval features

Healthcare Technology Letters

K.I. Ramachandran¹  K.P. Soman¹  V. Nivitha Varghees¹ 

[1] Amrita University;

关键词: 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;   

DOI  :  10.1049/htl.2016.0109

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown