Detecting heart ailments by investigating ECG with neural networks. (21st July 2022)

• Record Type: Journal Article
• Authors: Prabadevi, B.; Deepa, N.; Krithika, L.B.; Gulati, Ravi Raj; Sivakumar, R.
• Is Part Of: International journal of medical engineering and informatics. Volume 14:Number 5(2022)
• Page Start: 414
• Page End: 423
• Publication Date: 2022-07-21
• Subjects: cardiovascular disease -- CVD -- electrocardiogram -- networks -- arrhythmia -- classification; 610.2805
• Languages: English
• ISSNs: 1755-0653

Abstract

Heart ailments or cardiovascular diseases (CVD) are the diseases that incorporate the blood vessels or heart, which is common among various age groups. Though numerous techniques have been used to classify heart abnormalities, such as classification and regression trees (CART), they are less accurate. Therefore, a technique for early detection of heart ailments with more accuracy is mandatory. A model has been designed and proposed to detect the heart ailments using three-layered neural networks for better accuracy. Electrocardiogram (ECG or EKG) is used to identify arrhythmia (irregular heartbeat) accurately, and the UC Irvine (UCI) arrhythmia dataset of ECG reports are used to implement a classification for different types of heart abnormalities.