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Journal of Healthcare Engineering
Volume 2017, Article ID 4108720, 13 pages
https://doi.org/10.1155/2017/4108720
Research Article

Patient-Specific Deep Architectural Model for ECG Classification

1School of Information Science and Engineering, FuJian University of Technology, Xueyuan Road 3, Fuzhou 350118, China
2School of Instrument Science and Engineering, Southeast University, Sipailou 2, Nanjing 210096, China
3Institute for Medical Science and Technology, University of Dundee, Dundee DD2 1FD, UK
4School of Basic Medical Sciences, Nanjing Medical University, Longmian Avenue 101, Nanjing 211166, China

Correspondence should be addressed to Jianqing Li; nc.ude.umjn@ilqj

Received 23 October 2016; Revised 2 February 2017; Accepted 16 February 2017; Published 7 May 2017

Academic Editor: Ishwar K. Sethi

Copyright © 2017 Kan Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.