The influence of atrial flutter in automated detection of atrial arrhythmias - are we ready to go into clinical practice?".

Objective: To investigate the impact of atrial flutter (A) in the atrial arrhythmias classification task. We additionally advocate the use of a subject-based split for future studies in the field in order to avoid within-subject correlation which may lead to over-optimistic inferences. Finally, we demonstrate the effectiveness of the classifiers outside of the initially studied circumstances, by performing an inter-dataset model evaluation of the classifiers in data from different sources.

ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients.

The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients' ECGs are made available in multiple formats.