Detection of Fibrillatory Episodes in Atrial Fibrillation Rhythms via Topology-informed Machine Learning

Effective and efficient methods for diagnosing cardiac conditions remain of significant importance and relevance in clinical cardiology. As such, advances in machine- and deep-learning technologies pave the way to high throughput approaches to automated classification of cardiac abnormalities. While there is rich literature on ECG-based classification of cardiac conditions, particularly on diagnosing Atrial Fibrillation, there is a dearth on algorithms that can effectively measure the onset and offset of atrial fibrillation events within an ECG. In this work, we show that an off-the-shelf machine learning algorithm can be trained on mathematically-computable shape signatures embedded within the local topology of ECGs to identify fibrillatory episodes in ECGs of AF patients. More precisely, we show that a topology-informed machine learning algorithm can accurately classify segments within an ECG as either resembling an atrial fibrillation event or not. Furthermore, we show that based on the model-provided classification of segments, a simple criterion may be used to determine whether the AF rhythm is paroxysmal or persistent.