Multistage decision-based heart sound delineation method for automated analysis of heart sounds and murmurs

• Published in: Healthcare technology letters Vol. 2; no. 6; pp. 156 - 163
• Main Authors: Nivitha Varghees, V, Ramachandran, K.I
• Format: Journal Article
• Language: English
• Published: England The Institution of Engineering and Technology 01-12-2015
• Subjects: adaptive thresholding; diastolic murmurs; envelope extraction; fiducial point determination; Gaussian kernels based signal decomposition; Gaussian processes; heart murmurs; high‐pitched sounds; medical signal processing; multistage decision‐based delineation; phonocardiogram signal; phonocardiography; robust multistage decision‐based heart sound delineation; systolic murmurs; high-pitched sounds; heart murmurs; multistage decision-based delineation; Gaussian kernels based signal decomposition; fiducial point determination; envelope extraction; medical signal processing; systolic murmurs; robust multistage decision-based heart sound delineation; adaptive thresholding; Gaussian processes; phonocardiography; phonocardiogram signal; diastolic murmurs
• ISSN: 2053-3713; 2053-3713
• DOI: 10.1049/htl.2015.0010

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.