A Novel Algorithm for Automated Human Single-Lead ECG Pre-Annotation and Beat-to-Beat Separation for Heartbeat Classification Using Autoencoders

An electrocardiogram (ECG) is used to check the electrical activity of heart over a limited short-term or long-term period. Short-term observations are often in hospitals clinics, whereas (often called continuous stream-like ECG observations) monitor heart’s on daily basis and during different activities, such as sleeping, running, eating, etc. can reflect normal sinus rhythm well problems, which might vary from Premature Atrial Contractions (PAC) Ventricular (PVC), Sinus Arrest many other problems. In order perform monitoring basis, it very important implement automated solutions that most work analysis could alert doctors case any problem, even detect type problem for have an immediate report about patient’s health status. This paper aims provide workflow abnormal signals detection sources digitized signals, including ambulatory devices. We propose algorithm pre-annotation beat-to-beat separation heartbeat classification using Autoencoders. The includes training models types has shown promising results PVC compared solutions. solution proposed no-noise noisy well.