Electrocardiography is the process of producing an electrocardiogram (ECG or EKG), recordings of heart's electrical activity. ECG classification uses ECG data to identify and categorize various heart conditions, such as arrhythmias and other anomalities. Arrhythmias are abnormal heart rhythms that can be dangerous. It consists of analysing the electrical activity of the heart and detecting some patterns corresponding to specific diseases or conditions.

ECG heartbeat classification can be used in real-time for arrhythmia detection. By classifying heartbeats in real-time, doctors can monitor patients for arrhythmias and intervene quickly if necessary. For instance, a device worn by a patient could use ECG heartbeat classification to monitor their heart rhythm for signs of arrhythmia. If an arrhythmia is detected, the device could alert the patient or their doctor, and also for driver detection of fatigue using ecg sensor or smartwatch.

We'll use different machine learning and deep learning technics for detection and classification ECG signal. Moreover, emsemble methods will be used (Model Averaging, Model Voting).