The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.

Introduction The 4th China Physiological Signal Challenge 2021 (CPSC 2021) aims to encourage the development of algorithms for searching the paroxysmal atrial fibrillation (PAF) events from dynamic ECG recordings. ECG signal provides an important role in non-invasively monitoring and clinical diagnosis for cardiovascular disease (CVD). AF is the most frequent arrhythmia, but PAF often remains unrecognized[1, 2]. Early screening and early detection of paroxysmal AF are particularly important. It is of great value for AF surgery options, drug intervention, and the diagnosis and treatment of various clinical complications [3]. Although accurate detection of paroxysmal AF is very important, there is currently no algorithm that can efficiently measure the onsets and offsets of AF events in dynamic or wearable ECGs [4]. Previous AF detection algorithms usually focus on the classification of AF rhythm, such as entropy feature-based [5, 6] or machine learning-based methods [7, 8], without the location of onsets and offsets of AF events. Thus, the clinical significance for the personalized treatment and management of AF patients is limited. In clinical applications, other abnormal rhythms can significantly influence the accurate identification of AF rhythm. In this year’s challenge, we focus on the detection of paroxysmal AF events from dynamic ECGs. A new dynamic ECG database containing episodes with totally or partly AF rhythm, or non-AF rhythm was constructed, to encourage the development of the more efficient and robust algorithms for paroxysmal AF event detection. # Challenge Data Data are recorded from 12-lead Holter or 3-lead wearable ECG monitoring devices. Challenge data provides variable-length ECG records fragments extracted from lead I and lead II of the long-term dynamic ECGs, each sampled at 200 Hz. In order to avoid ambiguity in annotation, an AF event is limited to contain no less than 5 heart beats. The training set in the 1st stage consists of 730 records, extracted from the Holter records from 10 AF patients (5 PAF patients) and 39 non-AF patients (usually including other abnormal and normal rhythms). The training set in the 2nd stage consists of 706 records from 37 AF patients (18 PAF patients) and 14 non-AF patients. The test set comprises data from the same source as the training set as well as different data source. We ensure that at least one test subset was collected by a different ECG monitoring system compared with the training set. Same as in previous years, we are not planning to release the test set at any point. All data is provided in WFDB format and the annotations are standardized according to PhysioBank Annotations (link: https://archive.physionet.org/physiobank/annotations.shtml). The annotation includes the beat annotations (R peak location and beat type), the rhythm annotations (rhythm change flag and rhythm type) and the diagnosis of the global rhythm. Please refer to the example code entry (link: https://github.com/CPSC-Committee/cpsc2021-python-entry ) of the challenge for specific data and label load functions. Note that the flag of atrial fibrillation and atrial flutter (‘AFIB’ and ‘AFL’) in annotated information are seemed as the same type when scoring the method. Please download the training data from here ( Training Set I and Training Set II).

A large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database ## Abstract Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis. ## References Paper: www.nature.com/articles/s41597-023-02621-1