Open AccessMultistage decision‐based heart sound delineation method for automated analysis of heart sounds and murmurs
Author(s) -
Nivitha Varghees V.,
Ramachandran K.I.
Publication year - 2015
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.2015.0010
Subject(s) - phonocardiogram , heart sounds , stethoscope , computer science , thresholding , speech recognition , bioacoustics , pattern recognition (psychology) , signal (programming language) , artificial intelligence , acoustics , medicine , telecommunications , image (mathematics) , programming language , physics
A robust multistage decision‐based heart sound delineation (MDHSD) method is presented for automatically determining the boundaries and peaks of heart sounds (S1, S2, S3, and S4), systolic, and diastolic murmurs (early, mid, and late) and high‐pitched sounds (HPSs) of the phonocardiogram (PCG) signal. The proposed MDHSD method consists of the Gaussian kernels based signal decomposition (GSDs) and multistage decision‐based delineation (MDBD). The GSD algorithm first removes the low‐frequency (LF) artefacts and then decomposes the filtered signal into two subsignals: the LF sound part (S1, S2, S3, and S4) and the high‐frequency sound part (murmurs and HPSs). The MDBD algorithm consists of absolute envelope extraction, adaptive thresholding, and fiducial point determination. The accuracy and robustness of the proposed method is evaluated using various types of normal and pathological PCG signals. Results show that the method achieves an average sensitivity of 98.22%, positive predictivity of 97.46%, and overall accuracy of 95.78%. The method yields maximum average delineation errors of 4.52 and 4.14 ms for determining the start‐point and end‐point of sounds. The proposed multistage delineation algorithm is capable of improving the delineation accuracy under time‐varying amplitudes of heart sounds and various types of murmurs. The proposed method has significant potential applications in heart sounds and murmurs classification systems.