Atrial Fibrillation Detection Based on the Combination of Depth and Statistical Features of ECG

Atrial fibrillation is a kind of common chronic arrhythmia. The incidence of atrial fibrillation increases with aging. Therefore, especially for the elderly, accurate detection of atrial fibrillation can effectively prevent stroke. In this paper, we propose a strategy that combines the heartbeat model based on deep learning with statistical heart rate features, using a classifier such as a multi-layer perceptron to identify atrial fibrillation rhythm. It is worth noticing that the heartbeat model that we used to extract features for the classification of heartbeat. Through this transfer learning method, the features of each heartbeat in the heart rhythm are extracted one by one for the identification task of atrial fibrillation. We evaluated the proposed method on the MIT-BIH AF dataset. The experimental result shows that under the attention mechanism, the accuracy of the proposed method is 98.91%, the sensitivity is 99.41% and the specificity is 98.50%, which outperforms most of the current algorithms.