A TRANSFER LEARNING APPROACH BY USING 2-D CONVOLUTIONAL NEURAL NETWORK FEATURES TO DETECT UNSEEN ARRHYTHMIA CLASSES

Year 2021, Volume: 22 Issue: 1, 1 - 9, 26.03.2021

Emre Cimen

https://doi.org/10.18038/estubtda.755500

Cited By: 3

Abstract

Arrhythmia is an irregular heartbeat and can be diagnosed via electrocardiography (ECG). Since arrhythmia can be a fatal health problem, developing automatic detection and diagnosis systems is vital. Although there are accurate machine learning models in the literature to solve this problem, most models assume all arrhythmia types present in training. However, some arrhythmia types are not seen frequently, and there are not enough heartbeat samples from these rare arrhythmia classes to use them for training a classifier. In this study, the arrhythmia classification problem is defined as an anomaly detection problem. We use ECG signals as inputs of the model and represent them with 2-D images. Then, by using a transfer learning approach, we extract deep image features from a Convolutional Neural Network model (VGG16). In this way, it is aimed to get benefit from a pre-trained deep learning model. Then, we train a ν-Support Vector Machines model with only normal heartbeats and predict if a test sample is normal or arrhythmic. The test performance on rare arrhythmia classes is presented in comparison with binary SVM trained with normal and frequent arrhythmia classes. The proposed model outperforms the binary classification with 90.42 % accuracy.

Keywords

Convolutional neural networks, Transfer Learning, One-class classification, Arrhythmia classification, Electrocardiography

A TRANSFER LEARNING APPROACH BY USING 2-D CONVOLUTIONAL NEURAL NETWORK FEATURES TO DETECT UNSEEN ARRHYTHMIA CLASSES

Abstract

Keywords

Convolutional neural networks, Transfer learning,, One-class classification,, Arrhythmia classification,, Electrocardiography

References

• [1] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems; 3-8 Dec 2012; Lake Tahoe, NV, USA: 1097–1105.
• [2] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. CoRR, 2014.
• [3] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR); 8-10 June 2015; Boston, MA, USA: IEEE, 1-9.
• [4] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 27-30 June 2016; Las Vegas, NV, USA: IEEE, 770–778.
• [5] Cimen E, Ozturk G. Arrhythmia Classification via k-Means based Polyhedral Conic Functions Algorithm. In: 2016 International Conference on Computational Science & Computational Intelligence (CSCI); 15-17 Dec 2016; Las Vegas, NV, USA: IEEE, 798–802.
• [6] Marinho LB, de Nascimento NMM. Souza JWM, Gurgel MV, Filho PPR., de Albuquerque VHC, A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Gener Comp Sy 2019; 97: 564 – 577.
• [7] Pan G, Xin Z, Shi S, Jin D. Arrhythmia classification based on wavelet transformation and random forests. Multimed Tools Appl 2018; 77(17): 21905–21922.
• [8] Lannoy G, François D, Delbeke F, Verleysen M. Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification. In: International Joint Conference on Biomedical Engineering Systems and Technologies; 20- 23 Jan 2010; Valencia, Spain; 212 – 223.
• [9] Sharma M, Singh S, Kumar A, San Tan R, Acharya UR. Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features. Comput Biol Med. 2019; 115: 1 – 10.
• [10] Yildirim Ö, Pławiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med. 2018; 102: 411 – 420.
• [11] Singh S, Pandey SK, Pawar U, Janghel RR. Classification of ECG Arrhythmia using Recurrent Neural Networks. Procedia Comput. Sci. 2018; 132: 1290 – 1297.
• [12] Jun TJ, Nguyen HM, Kang D, Kim D, Kim D, Kim Y. ECG arrhythmia classification using a 2-d convolutional neural network. CoRR, 2018.
• [13] Salem M, Taheri S, Yuan J. Ecg arrhythmia classification using transfer learning from 2- dimensional deep cnn features. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS); 17-19 Oct 2018; Cleveland, OH, USA: IEEE, 1– 4.
• [14] Ebrahimi Z, Loni M, Daneshtalab M, Gharehbaghi A. A review on deep learning methods for ECG arrhythmia classification. Expert Syst Appl. 2020; 7: 1 – 23.
• [15] Moody GB, Mark RG. The impact of the mit-bih arrhythmia database. IEEE Eng Med Biol 2001; 20(3): 45–50.
• [16] Ecg arrhythmia classification in 2d cnn. https://github.com/chingchan1996/ECG-Arrhythmia-Classification-in-2D-CNN, accessed: 2019-11-14.
• [17] Schoölkopf B, Platt JC, Shawe Taylor JC, Smola AJ, Williamson RC. Estimating the support of a high-dimensional distribution. Neural Comput. 2001; 13(7): 1443–1471.