A two-stage classification model integrating feature fusion for coronary artery disease detection and classification

Abstract

According to the World Health Organization, Coronary Artery Disease (CAD) is a leading cause of death globally. CAD is categorized into three types, namely Single Vessel Coronary Artery Disease (SVCAD), Double Vessel Coronary Artery Disease (DVCAD), and Triple Vessel Coronary Artery Disease (TVCAD). At present, angiography is the most popular technique to detect CAD that is quite expensive and invasive. Phonocardiogram (PCG), being economical and non-invasive, is a crucial modality towards the detection of cardiac disorders, but only trained medical professionals can interpret heart auscultations in clinical environments. This research aims to detect CAD and its types from PCG signatures through feature fusion and a two-stage classification strategy. The self-developed low-cost stethoscope was used to collect PCG data from a local hospital. The PCG signals were preprocessed through an iterative signal decomposition method known as Empirical Mode Decomposition (EMD). EMD decomposes the raw PCG signal into its constituent components called Intrinsic Mode Functions (IMFs). Preprocessed PCG signal was generated exclusively through combining those signal components that contain high discriminative characteristics and less redundancy. Next, Mel Frequency Cepstral Coefficients (MFCCs), spectral and statistical features were extracted. A two-stage classification framework was devised to identify healthy and CAD types. The first stage framework relies on the fusion of MFCC and statistical features with the K-nearest neighbor classifier to predict normal and CAD cases. The second stage is activated only when the first stage detects CAD. The fusion of spectral, statistical, and MFCC features was employed with Support Vector Machines classifier to categorize PCG signatures into DVCAD, SVCAD, and TVCAD classes in the second stage. The proposed method yields mean accuracy values of 88.0%, 89.2%, 91.1%, and 85.3% for normal, DVCAD, SVCAD, and TVCAD, respectively, through 10-fold cross-validation. Comparative analysis with existing approaches confirmed the reliability of the proposed method for categorizing CAD in general clinical environments. The proposed model enhances the diagnosis performance by providing a second opinion during the medical examination.