Automated detection of atrial fibrillation and atrial flutter in ECG signals based on convolutional and improved Elman neural network

Abstract

Atrial fibrillation (AF) and atrial flutter (AFL) are two common life-threatening arrhythmias. Both are not only easily transformed into each other, but also often cause misdiagnosis due to the similar clinical symptoms. The early efficient and accurate detection of AF and AFL is helpful to reduce the pain and injury of patients suffering from these diseases, and the traditional detection is often inefficient and laborious. Therefore, we propose a new model mechanism using an 11-layers network architecture to automatically classify AF and AFL signals. It is mainly constructed with the convolutional neural network (CNN) and the improved Elman neural network (IENN). Besides, we specifically design two relative models as control subjects to validate the classification performance of the proposed model. 10-fold cross-validation is also implemented on the MIT-BIH AF database (AFDB) and the MIT-BIH arrhythmia database (MITDB), respectively. The obtained results show that the model achieved the accuracy, specificity, and sensitivity of 98.8%, 98.6%, and 98.9% on the AFDB database and 99.4%, 99.1%, and 99.6% on the MITDB database, respectively. The model mechanism has been demonstrated to have more superior performance than two relative models and some advanced algorithms, which can be considered as a reliable and efficient identification system to aid physicians and save lives.