Batyrkhan Omarov^1,2,*, Nurbek Saparkhojayev², Shyrynkyz Shekerbekova³, Oxana Akhmetova¹, Meruert Sakypbekova¹, Guldina Kamalova³, Zhanna Alimzhanova¹, Lyailya Tukenova³, Zhadyra Akanova⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2815-2833, 2022, DOI:10.32604/cmc.2022.019246

Abstract Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of… More >

Vinay Arora¹, Karun Verma¹, Rohan Singh Leekha², Kyungroul Lee³, Chang Choi^4,*, Takshi Gupta⁵, Kashish Bhatia⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4151-4168, 2021, DOI:10.32604/cmc.2021.019178

Abstract The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual… More >