PMID- 34656868 OWN - NLM STAT- MEDLINE DCOM- 20211104 LR - 20211104 IS - 1879-0534 (Electronic) IS - 0010-4825 (Linking) VI - 138 DP - 2021 Nov TI - Phonocardiogram signal analysis for classification of Coronary Artery Diseases using MFCC and 1D adaptive local ternary patterns. PG - 104926 LID - S0010-4825(21)00720-4 [pii] LID - 10.1016/j.compbiomed.2021.104926 [doi] AB - 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 computer-aided 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 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. CI - Copyright (c) 2021 Elsevier Ltd. All rights reserved. FAU - Iqtidar, Khushbakht AU - Iqtidar K AD - Knowledge and Data Science Research Centre, Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan. Electronic address: khushbakht.iqtidar18@ce.ceme.edu.pk. FAU - Qamar, Usman AU - Qamar U AD - Knowledge and Data Science Research Centre, Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan. FAU - Aziz, Sumair AU - Aziz S AD - Department of Electronics Engineering, University of Engineering and Technology, Taxila, Pakistan. FAU - Khan, Muhammad Umar AU - Khan MU AD - Department of Electronics Engineering, University of Engineering and Technology, Taxila, Pakistan. LA - eng PT - Journal Article DEP - 20211008 PL - United States TA - Comput Biol Med JT - Computers in biology and medicine JID - 1250250 SB - IM MH - Algorithms MH - *Coronary Artery Disease/diagnostic imaging MH - Heart Murmurs MH - *Heart Sounds MH - Humans MH - Phonocardiography MH - Signal Processing, Computer-Assisted OTO - NOTNLM OT - 1D-adaptive local ternary patterns OT - Classification OT - Computer-aided diagnosis OT - Coronary artery disease OT - Feature extraction OT - Phonocardiogram (PCG) EDAT- 2021/10/18 06:00 MHDA- 2021/11/05 06:00 CRDT- 2021/10/17 20:45 PHST- 2021/03/17 00:00 [received] PHST- 2021/09/15 00:00 [revised] PHST- 2021/10/01 00:00 [accepted] PHST- 2021/10/18 06:00 [pubmed] PHST- 2021/11/05 06:00 [medline] PHST- 2021/10/17 20:45 [entrez] AID - S0010-4825(21)00720-4 [pii] AID - 10.1016/j.compbiomed.2021.104926 [doi] PST - ppublish SO - Comput Biol Med. 2021 Nov;138:104926. doi: 10.1016/j.compbiomed.2021.104926. Epub 2021 Oct 8.