Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

Abstract

Abstract-Numerous studies are being conducted in recent years focusing on phonocardiographic (PCG) signals due to their capability to characterize heart sounds. These characteristics can be exploited in developing computer-aided auscultation system as a complementary tool for clinicians in diagnosis of cardiovascular disorders. This study proposes a new type of features to distinguish five categories of heart sounds, including normal, mitral stenosis, mitral regurgitation, aortic stenosis, and aortic regurgitation. PCG signals were collected from online resources and training CDs. Wavelet packet transform was utilized for heart sound analysis as opposed to discrete wavelet transform that has been extensively used in the previous studies. Then, trapezoidal function was calculated for deriving feature vectors. A hybrid classifier was designed composing of three types of classifiers, multilayer perceptron (MLP) artificial neural network, k-nearest neighbor (KNN), and support vector machine (SVM), to classify feature vectors. The promising results demonstrate the effectiveness of the proposed trapezoidal features and hybrid classifier for heart sound classification.

Classifying the heart sound signals using textural‐based features for an efficient decision support system

“…In the literature, both supervised (Das et al, 2019; Moulana et al, 2022) and unsupervised (Das et al, 2020; Torre‐Cruz et al, 2022) approaches of heart health status detection have been reported. The unsupervised methods, despite their potential, have some limitations about their usability and accuracy (Safara et al, 2012). Since a labelled dataset is at hand (PhysioNet 2016), the proposed work focuses on a supervised method of depicting cardiac health status.…”

A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

“…Systems are devised in (Ahmad, 2011;Grzegorczyk et al, 2016;Hofmann et al, 2016) to interpret the condition of heart valves as normal or abnormal without further classifying the abnormal ones, while in (Emre & Uguz, 2011;Uğuz, 2012), the valvular heart condition is interpreted as one of the three cases (normal, mitral stenosis, pulmonary stenosis). Furthermore (Noman et al, 2018) presents a novel system to diagnose four valvular heart cases (normal, aortic regurgitation, mitral stenosis, mitral regurgitation), whereas in (Safara et al, 2013;Suboh et al, 2008;Suhas et al, 2017), five valvular heart cases (normal, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation) are diagnosed. In (Kumar et al, 2018), a system is devised to diagnose five heart valve diseases (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, pulmonary stenosis).…”

Survey of Different Methodologies Used in Phonocardiogram Signal Analysis

“…These are called murmurs. They are generally high-frequency, noise like sounds that are produced as a result of turbulent blood flow [2]. Intensity, frequency content, split information, time relations, location of murmurs etc.…”