A novel embedded system design for the detection and classification of cardiac disorders. (4th June 2021)

• Record Type: Journal Article
• Authors: Riaz, Umair; Aziz, Sumair; Umar Khan, Muhammad; Zaidi, Syed Azhar Ali; Ukasha, Muhammad; Rashid, Aamir
• Other Names: Ventura Sebastian guestEditor.; Soda Paolo guestEditor.; González Alejandro Rodríguez guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 4(2021)
• Page Start: 1844
• Page End: 1864
• Publication Date: 2021-06-04
• Subjects: cardiovascular disorders -- empirical mode decomposition -- heart murmurs -- intelligent embedded system -- K‐nearest neighbors -- phonocardiogram; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12469
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Phonocardiogram (PCG) signals hold significant prognostic and diagnostic information about cardiac health. Numerous PCG or heart sound based automated detection algorithms were previously proposed to assist the disease diagnosis process. Most of the previous studies only focused on algorithmic development. This study presents an intelligent, portable, and low‐cost embedded system for the classification of cardiac disorders associated with heart murmurs. Different stages corresponding to the developed embedded system implementation are summarized as follows: The first stage consists of the acquisition of PCG signals of both normal and patients from various hospitals with a customized and low‐cost stethoscope. The second stage describes the preprocessing, localization of S1 and S2 heart sounds, and the extraction of systole and diastole from a heart signal with an empirical mode decomposition integrated with the self‐developed algorithm. In the third stage, discriminant features are extracted to represent various cardiac classes of PCG signals in a compact manner. In the final stage of the algorithm, the k‐nearest neighbors classifier is trained and tested to distinguish between normal and four cardiac disorders. The proposed algorithm achieved 94% mean accuracy through comprehensive experimentation. The cardiac disorders classification algorithm is implemented on a RP‐based embedded system. Software application with an interactive graphical interface is also designed to assist users. The developed intelligent system is portable, low‐cost, and it enables regular patient‐monitoring. The proposed system has the potential to be employed at remote locations where the availability of doctors remains challenging.