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Appl. Sci. 2018, 8(9), 1590; https://doi.org/10.3390/app8091590

A Spatial Pyramid Pooling-Based Deep Convolutional Neural Network for the Classification of Electrocardiogram Beats

1,2
,
1,2,* , 2
,
2
and
3
1
College of Instrument Science and Electrical Engineering, Jilin University, Changchun 130061, China
2
Department of Electronic Information Engineering, Zhuhai College of Jilin University, Zhuhai 519041, China
3
Department of Biomechanical Engineering, City University of Hong Kong, Hong Kong SAR 999077, China
*
Author to whom correspondence should be addressed.
Received: 16 July 2018 / Revised: 30 August 2018 / Accepted: 31 August 2018 / Published: 8 September 2018
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Abstract

An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%). View Full-Text
Keywords: ECG beats; classification; feature extraction; convolutional neural networks; spatial pyramid pooling ECG beats; classification; feature extraction; convolutional neural networks; spatial pyramid pooling
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Li, J.; Si, Y.; Lang, L.; Liu, L.; Xu, T. A Spatial Pyramid Pooling-Based Deep Convolutional Neural Network for the Classification of Electrocardiogram Beats. Appl. Sci. 2018, 8, 1590.

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