Open AccessWavelet‐based fundamental heart sound recognition method using morphological and interval features
Author(s) -
Nivitha Varghees V.,
Ramachandran K.I.,
Soman K.P.
Publication year - 2018
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2016.0109
Subject(s) - phonocardiogram , thresholding , pattern recognition (psychology) , computer science , wavelet , artificial intelligence , heart sounds , speech recognition , noise (video) , envelope (radar) , signal (programming language) , interval (graph theory) , wavelet transform , pascal (unit) , mathematics , telecommunications , medicine , radar , combinatorics , image (mathematics) , programming language
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.