Paper Info
Title
Deep Learning Based Patient-Specific Classification Of Arrhythmia On Ecg Signal
Abstract
The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat. After removing the noise from ECG signals by a low-pass filter, the two-lead heartbeat segments with 2-s length were generated on the filtered signals, and classified by an adaptive ResNet model. The proposed method was evaluated on the MIT-BIH Arrhythmia Database with the patient-specific pattern. The overall accuracy was 98.6% and sensitivity of N, S, V, F were 99.4%, 85.4%, 96.6%, 90.6% respectively. Experimental results show that the proposed method achieved a good performance, and would be useful in the clinic practice.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856650
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Heartbeat,Pattern recognition,Heart rate variability,Computer science,Feature extraction,Artificial intelligence,Beat (music),Deep learning,Electrocardiography,Artificial neural network,Ectopic beat
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
1
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Wei Zhao121.70
Jing Hu221.36
Dongya Jia344.80
Hongmei Wang43113.44
Zhenqi Li543.11
Cong Yan632.09
Tianyuan You721.36