Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition

Abstract

The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.

Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM

“…Feature extraction is a process of reducing the volume of the data because the techniques used in classification should have an optimal number of features (Becerra et al, ). In our work, it is not the wise decision to process the PCG signals in any analysis algorithm in a direct manner without extracting useful features.…”

“…Heart murmurs are regarded as one of the important indicators for diagnosis of heart valve disorder due to a certain type of heart disease (Amiri, Movahedi, & Kazemi, ; Song, Liu, Liao, & Li, ; Tejman‐Yarden, Levi, & Beizerov, ). Cardiovascular diseases (in various forms) are one of the major cause of a significant number of deaths and disabilities (Becerra, Orrego, & Trejos, ). Nowadays, in most of the hospitals, electrocardiography (ECG), echocardiography and PCG are the common methods that used for diagnosis of heart diseases (Jamal et al, ; Mughal, Muhammad, Sharif, Saba, & Rehman, ).…”

“…The feature set contained Wigner bispectrum, Bispectrum, and Shannon energy is used to differentiate innocent and pathological murmurs. The approach of murmur classification proposed by Becerra et al () presents a hybrid technique that uses empirical mode decomposition and Hilbert‐Huang Transform. It is applied to different intrinsic mode functions.…”