2020 Computing in Cardiology Conference (CinC) 2020
DOI: 10.22489/cinc.2020.098
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Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms

Abstract: Activity 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 … Show more

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“…The continuous electrical activity that is a footprint of RAc ( 22 ) was quantified in each CartoFinder acquisition. The electrical burden of the bipolar EGMs was measured using an automatic activity detection algorithm based on the fully unsupervised Hidden Markov Models (HMMs) ( 23 ). The method automatically detects periods of electrical activity in AF bipolar EGMs and provides a score between 0 (no activity) and 1 (continuous activity), as shown in the Supplementary Material .…”
Section: Methodsmentioning
confidence: 99%
“…The continuous electrical activity that is a footprint of RAc ( 22 ) was quantified in each CartoFinder acquisition. The electrical burden of the bipolar EGMs was measured using an automatic activity detection algorithm based on the fully unsupervised Hidden Markov Models (HMMs) ( 23 ). The method automatically detects periods of electrical activity in AF bipolar EGMs and provides a score between 0 (no activity) and 1 (continuous activity), as shown in the Supplementary Material .…”
Section: Methodsmentioning
confidence: 99%