Why the study?
Does a novel machine learning clustering method accurately identify activation points for atrial fibrillation compared to the Topera system?
Does a novel machine learning clustering method accurately identify activation points for atrial fibrillation compared to the Topera system?
A novel machine learning clustering method using ECG features may offer an alternative approach to identifying atrial fibrillation activation points compared to existing systems like Topera.
May offer an ECG-based alternative for AF activation mapping; leaves open prospective clinical validation.
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.
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Pu et al. (2016) studied this question.
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