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.