Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms

Proceeding of 2020 Computing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, ItalyActivity detection in atrial fibrillation (AF) electrograms (EGMs) is a key concept to understand the mechanisms of this frequent arrhythmia and design new strategies for its treatment. We present a new method that employs Hidden Markov Models (HMMs) to identify activity presence in bipolar EGMs. The method is fully unsupervised and hence it does not require labeled training data. The HMM activity detection method was validated and compared to the non-linear energy operator (NLEO) method for a set of manually annotated EGMs. The HMM performed better than the NLEO and exhibited more robustness in the presence of low voltage fragmented EGMs.This study was supported by grants PI18/01895 from the Instituto de Salud Carlos III, and RD16/0011/0029 Red de Terapia Celular from the Instituto de Salud Carlos III, the projects RTI2018-099655-B-I00; TEC2017-92552-EXP; PID2019-108539RB-C22, Y2018/TCS-4705, and the support of NVIDIA Corporation with the donation of the Titan V GPU used during this research.Publicad