Deep Learning in the Diagnosis and Management of Arrhythmias

Abstract

Recent advancements in analyzing methods for the identification of arrhythmia based on deep learning have revealed great promise towards improving cardiac care. Probabilistic models have been used effectively to detect a number of arrhythmic disorders from ECG signals with the help of convolutional neural networks and Long Short Term Memory neural network. These models are more precise and quicker than conventional approaches to deal with the ailment in the initial stages and with diseases such as bradycardia, ventricular tachycardia, or atrial fibrillation. However, barriers such as class distribution, data sanitization, interpretability, and generalization across different types of patients remain, which hinders their clinical utilization. Actually, deep learning is used in clinical practice, especially in wearable devices and remote patient monitoring for the unceasing and real-time continuous rheological evaluation of the cardiovascular system. The subsequent advancements in this area will focus on the proper combination of the data from multiple subject areas and the application of specific treatment approaches, including the use of artificial intelligence in a more extensive medical system. Other than the diagnosis of arrhythmias, deep learning has the chances of enhancing patient prognoses, preliminary assessment, and tailor-made treatments. It is likely that deep learning-based systems will have a possibility to evolve into powerful aid for diagnosing and setting further treatment in cases of arrhythmias, though there are issues on the way to the enhance the availability and quality of the care. This will probably be facilitated by continued research and integration between academicians, practitioners, and policy makers.