Abstract
Mostly all works dealing with ECG signal and Convolutional Network approach use 1D CNNs and must train them from scratch, usually applying a signal preprocessing, such as noise reduction, R-peak detection or heartbeat detection. Instead, our approach was focused on demonstrating that effective transfer learning from 2D CNNs can be done using a well-known CNN called AlexNet, that was trained using real images from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. From any temporal signal, it is possible to generate spectral images (spectrograms) than can be analysed by 2D CNN to do the task of extracting automatic features for the classification stage. In this work, the power spectrogram is generated from a randomly ECG segment, so no conditions of signal extraction are applied. After processing the spectrogram with the CNN, its outputs are used as relevant features to be discriminated by a Multi Layer Perceptron (MLP) which classifies them into arrhythmic or normal rhythm segments. The results obtained are in the 90% accuracy range, as good as the state of the art published with 1D CNNs, confirming that transfer learning is a good strategy to develop decision models in signal and image medical tasks.
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References
Ho, K.K.L., Moody, G.B., Peng, C.-K.: Predicting survival in heart failure cases and controls using fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96, 842–848 (1997)
Thakor, N.V., Zhu, Y.-S.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)
Antunes, E., Brugada, J., Steurer, G., Andries, E., Brugada, P.: The Differential Diagnosis of a Regular Tachycardia with a Wide QRS Complex on the 12-Lead ECG: Ventricular Tachycardia, Supraventricular Tachycardia with Aberrant Intraventricular Conduction, and Supraventricular Tachycardia with Anterograde Conduction over an Accessory Pathway (1994)
Tsipouras, M.G., Fotiadis, D.I., Sideris, D.: An arrhythmia classification system based on the RR-interval signal. Artif. Intell. Med. 33, 237–250 (2005)
Kiranyaz, S., Ince, T., Hamila, R., Gabbouj, M.: Convolutional neural networks for patient-specific ECG classification. In: 37th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2015) (2015)
Nguyen, Q.T., Bui, T.D.: Speech classification using SIFT features on spectrogram images. Vietnam J. Comput. Sci. 3(4), 247–257 (2016)
Acharyaa, U.R., Oha, S.L., Hagiwaraa, Y., Tana, J.H., Adama, M., Gertychd, A., Sane, T.R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)
Pyakillya, B., Kazachenko, N., Mikhailovsky, N.: Deep learning for ECG classification. IOP Conf. Series J. Phys. Conf. Series 913 (2017). 012004
Xiang, Y., Lin, Z., Meng, J.: Automatic QRS complex detection using two-level convolutional neural network. BioMed. Eng. OnLine (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
Hoo-Chang, S., Roth, H.R., Gao, M., Le, L., Ziyue, X., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
MIT-BIH Arrhythmia Database [Internet]. Harvard-MIT Division of Health Sciences and Technology (1980). https://www.physionet.org/physiobank/database/mitdb/. Accessed Feb 2018
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
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We would like to thank the company Sallén Tech of the Gunnevo group for financing the publication of this work.
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Ruiz, J.T., Pérez, J.D.B., Blázquez, J.R.B. (2019). Arrhythmia Detection Using Convolutional Neural Models. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_15
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