Machine Learning Integration in Cardiac Electrophysiology

Atrial fibrillation is a disorder in which there is a chaotic fire of electrical signals from the upper chambers of the heart. The identification of the location of the myocardium responsible for firing these signals and ablation of the area may potentially cure the problem. The electrophysiologists may have to insert the probes or catheters and do the cardiac mapping to identify and analyze the complex heart signals patterns and to identify the location of AF responsible electrical foci. Nowadays, machine learning has become crucial in every technology field. Automation with software using machine-learning algorithms may aid electrophysiologists to do cardiac mapping without struggle and detecting electrical foci by computers. ML algorithms may identify arrhythmia compared to a board-certified cardiologist and can be developed as a very fast and reliable diagnostic tool. (c) 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved

Radial basis function neural network-based adaptive critic control of induction motors

Abstract Abnormal intra-QRS potentials (AIQP) in signalaveraged electrocardiograms have been proposed to be a potential noninvasive index for the diagnosis of the risk of ventricular arrhythmias. This study tries to develop a nonlinear neural network using radial basis functions Introduction Signal-averaged electrocardiograms (SAECG) have became an important noninvasive tool for diagnosing the risk of ventricular arrhythmias Methods Data acquisition The high-resolution electrocardiograms were recorded at rest with patients in a supine position using a commercially available Simens-Elema Megacart ® machine and a bipolar, orthogonal X, Y and Z lead system [1-2]. The input electrocardiograms were further digitized by an analog to digital converter with a sampling rate of 2 kHz and a resolution of 12 bits, and a 10 min digitized ECG signal was stored on a computer hard disk for subsequent off-line analysis. According to the standards of SAECG analysis recommended by the 1991 ESC, AHA and ACC Task Force and the 1996 ACC committee, the signal averaging technique was applied to reduce the effects of random noise, and a bidirectional Butterworth filter with 40 to 250 Hz frequency band was used to extract high frequency components of SAECG. The final noise level of SAECG was lower than 0.7 µV. The starting point (onset) and end point (offset) were obtained by using the analysis of vector magnitude Two study groups consisting of the normal and AIQP groups were recruited to test the performance of the proposed RBF neural network for the detection of AIQP. The normal group consisted of the X-lead SAECG of 42 normal Taiwanese. Because all the normal subjects had a normal clinical history, physical examination, and 12-lea