Atrial Fibrillation Detection Using a Feedforward Neural Network

Purpose In this study, we aimed to develop an automatic atrial fibrillation detection technique for the early prediction of atrial fibrillation, that can be used with wearable devices. Methods An effective deep learning-based technology is proposed to automatically detect atrial fibrillation. First, novel preprocessing algorithms, wavelet transform and sliding window filtering, are introduced to reduce the noise and outliers, respectively, from ECG signals. Then, a robust R-wave detection algorithm is developed. In addition, we proposed a feedforward neural network to detect atrial fibrillation based on ECG records. Results Experiments verified using a tenfold cross-validation strategy showed that the proposed method achieves competitive detection performance, and can be applied to wearable detection devices. The proposed R-wave detection algorithm achieved a detection sensitivity of 99.22%, a positive recognition rate of 98.55%, and a deviance of 2.25% on the MIT-BIH arrhythmia database. The proposed atrial fibrillation detection model achieved an accuracy of 84.00%, a detection sensitivity of 84.26%, a specificity of 93.23%, and an area under the receiver operating curve of 89.40% on a mixed dataset composed of the Challenge2017 database and the MIT-BIH arrhythmia database. Conclusion The analysis demonstrated that the proposed atrial fibrillation detection method could automatically detect atrial fibrillation with high accuracy and efficiency, could be applied to wearable devices, and has great value in the early detection of atrial fibrillation. We believe that our work will make a valuable contribution to the area of atrial fibrillation.