Abstract:
Coronary Artery Diseases (CADs) are a dominant cause of worldwide fatalities. The
development of accurate and timely diagnosis routines is imperative to reduce these risks and
mortalities. Coronary angiography, an invasive and expensive technique, is currently used as a
diagnostic tool for the detection of CAD but it has some procedural hazards, i.e., it requires
arterial puncture, and the subject gets exposed to iodinated radiation. Phonocardiography (PCG),
a non-invasive and inexpensive technique, is a modality employing heart sounds to diagnose
heart diseases but it requires only trained medical personnel to apprehend cardiac murmurs in
clinical environments. Furthermore, there is a strong compulsion to characterize CAD into its
types, such as Single vessel coronary artery disease (SVCAD), Double vessel coronary artery
disease (DVCAD), and Triple vessel coronary artery disease (TVCAD) to assist the cardiologist
in decision making about the treatment procedure followed. This paper presents a computeraided diagnosis system for the categorization of CAD and its types based on Phonocardiogram
(PCG) signal analysis.
The raw PCG signals were denoised via empirical mode decomposition (EMD) to remove
redundant information and noise. Next, we extract MFCC and proposed 1D-Adaptive Local
Ternary Patterns (1D-ALTP) and fused them serially to get a strong feature representation of
multiple PCG signal classes. Features were further reduced through Multidimensional Scaling
(MDS) and subjected to several classification methods such as support vector machines (SVM),
Decision Tree (DT), and K-nearest neighbors (KNN) in a comparative fashion. The best
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classification performances of 98.3% and 97.2% mean accuracies were obtained through SVM
with the cubic kernel for binary and multiclass experiments, respectively. The performance of
the proposed system is comprehensively tested through 10-fold cross validation and hold-out
train-test techniques to avoid model overfitting. Comparative analysis with existing approaches
advocates the superiority of the proposed approach
