Detection of Cardiac Arrhythmias in Electroc Ardiograms Using Deep Learning

Medical diagnostic support systems based on automatic learning algorithms, called machine learning, represent a form of artificial intelligence (AI) that improves the performance, quality, and speed of health care. Improvements in embedded machine learning applications have led to the design of many medical monitoring devices. The latter are equipped with biological signal sensors to measure the activity of the target organ of a subject. The essential objective of these devices is to record, save and analyze the acquired signals to establish an appropriate diagnosis and/or identify the signs of pathological symptoms. The work of this paper falls within this context and has the main objective of implementing new strategies for the analysis and diagnosis of Electrocardiogram (ECG) signals, focused on the detection of episodes of cardiac arrhythmia.