Novel Clustering Method towards Identification of Activation Points for Atrial Fibrillation

In this paper, a novel approach to locate the activation points of atrial fibrillation (AF) is proposed. This new method is built upon machine learning, where common parameters, such as dominant frequency, first harmonic frequency, etc., are adopted. Features are extracted from the original electrocardiography (ECG) and then clustering is performed to classify the ECG signals into two groups, namely activation and nonactivation points. The experimental results are compared with those from the state-of-the-art system, Topera, used in East Jefferson General Hospital nowadays.