Abstract

Info

Title:: Multilayer perceptron-based method for atrial fibrillation ECG detection

Author(s):: WEI Wenjing1; WANG Xun1; ZHANG Pengyuan1; YAN Yonghong1; 2; 3; 1. Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China; 2. Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China

Keywords:: Keywords: atrial fibrillation; electrocardiogram; multilayer perceptron; R-wave detection; deep belief network

Abstract:: Abstract: Objective To propose an atrial fibrillation (AF) recognition method based on multilayer perceptron (MLP). Methods Firstly, a novel R-wave detection algorithm based on adaptive threshold was designed, and then with the location and amplitude of R-wave as features, MLP was used as classifier to recognize the normal/AF electrocardiogram (ECG). The network parameters of MLP were initialized by deep belief network pre-training algorithm. Finally, the weights of MLP network were tuned by error back-propagation (BP) algorithm. Results The sensitivity, specificity and average recognition rate of the proposed method for the classification of normal and AF ECG signals on a single-channel ECG database were 96.00%, 84.18% and 90.09%, respectively. Conclusion The proposed algorithm based on MLP which has high accuracy and lower computation complexity can be a new method for the intelligent diagnosis of AF.