Table 1.

Summary table of the major databases used for classification of ECG signals.

database	type of recordings	number of recordings	annotations
MIT-BIH Arrhythmiaa	— 30-min excerpts — 2-channel ambulatory ECG — 360 Hz	48	beat-by-beat annotations for each beat in each recording (approx. 110 000 annotations)
QT databasea	— 15-min. excerpts — 2-channel ECG — 250 Hz	105	— reference beat annotations — segmentation of waveforms (for 30 to 100 normal beats per recording)
American Heart Association ventricular arrhythmiaa	— 2-channel excerpts — analogue ambulatory ECG — 250 Hz	80 for training—75 for testing	— 8 classes of recordings (level of ventricular ectopy) — final 30 min annotated beat-by-beat
INCARTa	— 30-min ECG — 12 leads — 275 Hz	75	— 175 000 beat annotations — 10 classes pathological diagnosis
UCI Machine Learning: Arrhythmia dataset	— 279 attributes (age, sex, height, waveforms description over 12 leads such as duration, amplitudes, areas)	452	16 arrhythmia classes labelled
Long-Term-STa	— between 21 and 24 h — 2 or 3 ECG signals — 250 Hz	86	— annotated ST episode — QRS annotations — ST level measures

^aPhysioBank datasets [17] available at https://physionet.org/:

— gathers 60 databases (4TB) of physiological signals: cardiopulmonary, neural, other biomedical signals

— freely available

— healthy subjects and patients (sudden cardiac death, congestive heart failure, epilepsy, gait disorders, sleep apnoea, ageing)