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. 2019 Jun 5;19(11):2558.
doi: 10.3390/s19112558.

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network

Yinsheng Ji  1   2 , Sen Zhang  3   4 , Wendong Xiao  5   6
Affiliations

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network

Yinsheng Ji et al. Sensors (Basel). .

Abstract

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.

Keywords: automatic classification; convolutional neural network; deep learning; electrocardiogram; electrocardiogram preconditioning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overall procedure involved in electrocardiogram (ECG) beat classification.
Figure 2
Figure 2
Individual intrinsic mode functions (IMFs) after ECG decomposition.
Figure 3
Figure 3
ECG signal processing flow based on empirical mode decomposition (EMD).
Figure 4
Figure 4
Comparison of the original signal and the processed signal.
Figure 5
Figure 5
Find the position of the R wave based on discrete wavelet transform (DWT).
Figure 6
Figure 6
ECG beat extraction process.
Figure 7
Figure 7
Normal beat and four ECG arrhythmia beats.
Figure 8
Figure 8
Faster regions with a convolutional neural network (Faster R-CNN) architecture.
Figure 9
Figure 9
Region proposal network [36].
Figure 10
Figure 10
Intersection-over-union (IoU).
Figure 11
Figure 11
Non-maximum suppression.
Figure 12
Figure 12
The detected ECG beats.
Figure 13
Figure 13
Score threshold test results.
Figure 14
Figure 14
The detected sample graph consisting of multiple ECG beats.
Figure 15
Figure 15
Faster R-CNN and one versus rest support vector machine (OVR SVM) comparison results.
See this image and copyright information in PMC

References

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