Automatic identification of atrial fibrillation based on the modified Elman neural network with exponential moving average algorithm. (October 2021)

• Record Type: Journal Article
• Authors: Song, Zhanjie; Wang, Jibin
• Is Part Of: Measurement. Volume 183(2021)
• Publication Date: 2021-10
• Subjects: 68Uxx -- 62P10; Atrial fibrillation -- Deep learning -- Exponential moving average algorithm -- Modified Elman neural network; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109806
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Atrial fibrillation is a most common arrhythmia. An early and accurate detection for the cure and even spread of this disease is considerably critical. The visual examination of electrocardiogram signals is the most extensively used diagnosis approach, but this method is cumbersome and low-efficient. In this work, we propose an intelligent network model based on the modified Elman neural network for signals discrimination. Motivated from the exponential moving average strategy, the proposed model is capable of fully modeling the information feedback and also effectively and efficiently striking a balance between current information representation and historical information representation in original Elman neural network. To evaluate its practicability, the model is also plugged into a convolutional neural network framework and two control subjects are established for a fair comparison. Experiments on the MIT-BIH atrial fibrillation and arrhythmia databases show that the proposed model can enjoy a consistent improvement in classification performance with the accuracy of 98.2% and 97.2% respectively and exhibit lower convergence rate than existing Elman network. Thanks to its high model performance, we are planning to develop the model into a computer-aided diagnosis system to assist physicians. Highlights: We design modified Elman network (MENN) for atrial fibrillation (AF) detection. Patient-independent validation ensures the model robustness. The feature extraction and classification are not required. To our knowledge, this is the first time to redesign ENN for AF detection.