Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021

Atrial fibrillation (AF) is the most frequent arrhythmia, but paroxysmal atrial fibrillation (PAF) often remains unrecognized. Early detection of PAF is of great value for AF surgery options, drug intervention and diagnosis and treatment of various clinical complications. Although accurate detection of paroxysmal AF is very important, there is currently no algorithm that can efficiently measure the onset and end of AF episode in dynamic or wearable ECGs. Previous AF detection algorithms usually focus on the classification of AF rhythm instead of locating the onsets and ends of AF episodes. Thus, the clinical significance for the personalized treatment and management of AF patients is limited. The identification of AF rhythm is also influenced by other abnormal rhythms in clinical applications. The 4th China Physiological Signal Challenge 2021 (CPSC 2021) aims to encourage the development of algorithms for searching the AF episodes in dynamic ECG records. A new dynamic ECG database was constructed to encourage the development of more efficient and robust algorithms for PAF detection. We also develop a new scoring metric to evaluate detection methods for PAF events.