Visual ECG Analysis in Real-world Scenarios

Cardiac arrhythmia is an alteration of the heart rhythm, for which the heartbeat is irregular. Based on the severity of this condition, an arrhythmia could represent a serious danger for a patient. An ECG is a graphic representation of an heart rhythm, which provides an overview of heart's conditions over a specific time interval. ECG signal analysis is entrusted to trained clinicians, although complex and frantic environments, such as emergency settings, can make hard to delegate continuous monitoring to the medical personnel. In such scenarios, an automatic detection methodology could provide crucial support in promptly alerting clinicians towards a potential degeneration of a patient's conditions. To this end, we propose a heartbeat classification module capable of capturing the semantics of visual information of ECG signals provided by video frames. The module relies on feature extraction techniques derived from video projected images resulting in ECG data, which are then classified by means of deep-learning models. It can be used to support the early detection of some arrhythmia in critical contexts, such as emergency rooms. We show how the proposed module can be used to support clinicians in this context, and discuss an experimental evaluation performed over ground-truth datasets.