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Atrial Fibrillation is a common arrhythmia. RR interval irregularity and P wave disappeared and replaced by continuous f-wave is the two important ECG manifestations of atrial fibrillation. RR interval irregularity can also be reflected in other types of arrhythmia, P Wave or f wave is a weak signal, its feature point detection is difficult and shape features are difficult to grasp. Therefore, this paper proposes a method combining manual extraction features and neural network extraction features for atrial fibrillation detection. Experiments were performed using the MIT-BIH atrial fibrillation database. The ECG signals were first processed into equal-length data, and then the 180 dimensional time-frequency domain features were manually extracted and combined with the improved 128 dimensional features extracted by the neural network. The extracted feature input integration model, the sub-model includes Decision Tree, Random forest, GBDT, XGBoost, LightGBM.In the final experiment, the stacking model was used. The accuracy, accuracy, recall rate, F1 and AUC were 99.1%, 98.9%, 98.9%, 98.9%, and 99.4%. This method is better than the single model, which provided a very good measure for detecting atrial fibrillation.

Atrial Fibrillation Detection Algorithm Based on Manual Extraction Features and Automatic Extraction Features