Artificial neural network-based cardiovascular disease prediction using spectral features. (July 2022)

• Record Type: Journal Article
• Authors: Khan, Misha Urooj; Samer, Sana; Alshehri, Mohammad Dahman; Baloch, Naveed Khan; Khan, Hareem; Hussain, Fawad; Kim, Sung Won; Zikria, Yousaf Bin
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Cardiovascular disease -- Machine learning -- Heart murmur -- Phonocardiogram -- Spectral analysis -- Artificial neural network -- Classification -- Segmentation -- Minimum redundancy maximum relevance -- Feature analysis; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108094
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The major cause of the increasing world mortality rate is cardiovascular disease (CVD), killing 17.9 million people annually. Current techniques are costly, challenging to operate on, and an expert is needed to confirm the diagnosis results. Phonocardiogram (PCG) signals are heart sound recordings of heart rhythms and have many advantages over traditional auscultation methods. This work targets CVD detection through PCG signal analysis using different artificial neural networks (ANN) and fusion of spectral features. PCG signal is acquired through the subject's heart by a self-designed PCG acquisition setup. It is then pre-processed and extracted five spectral features with the highest pair-wise differences. Five different types of ANN named narrow, wide, tri-layered, bi-layered, and medium are simulated with 99.99% accuracy. This proposed architecture is non-invasive, moderate, and reliable compared to current approaches and also offers great guidance in offering new low-cost alternatives for CVD diagnosis techniques. Graphical abstract: Highlights: Developed a low-cost PCG acquisition system (PAS) to record different heart sounds from patients. Annotation of the dataset by competent clinical experts/cardiologists using standard auscultation. Data training on five different types of artificial neural networks with the best spectral features having a maximum class-wise difference. Deployment of the less extensive computational algorithm with robust detection and classification of cardiovascular disease (CVD).