Abstract

Info

Author(s):: WANG Ziqiang1; LIU Hongyun2; SHI Jinlong2; WANG Weidong2; 1. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; 2. Department of Medical Engineering Support Center, Chinese PLA General Hospital, Beijing 100853, China

Keywords:: Keywords: arrhythmia; heartbeat recognition; convolution neural network

Abstract:: Abstract: Electrocardiogram (ECG) analysis is the main method for diagnosing arrhythmia. The type of heartbeat plays a key role in the diagnosis of arrhythmia, and the automatic recognition of heartbeat is an important step in the automatic diagnosis of arrhythmia. Herein the convolution neural network is used to automatically identify the type of heartbeat. The ECG data used in this study are derived from MIT-BIH arrhythmia database. The morphological features of ECG are taken as inputs and an end-to-end learning network structure is adopted. The results of 10-fold cross-validation test show that the average accuracy and specificity of the proposed network for the recognition of 13 types of heartbeats are 99.24% and 99.59%, respectively, and the recognition accuracy of the proposed network is 99.07% when adding random noise which is less than 0.4 mV. In addition, taking the clinical data of one of the patients from the database as the measured data, a positive predictive value of 99.19% can be obtained. The results prove that the proposed network can automatically learn the input features, accurately identify a variety of heartbeats, and has a good robustness to noise, which provides a reliable basis for the subsequent automatic diagnosis of arrhythmia, and may also provide decision-making support for other diagnoses based on ECG analysis.