Abstract
Deep Learning (DL) has turned into a subject of study in different applications, including medical field. Finding the irregularities in Electrocardiogram (ECG) is a critical part in patients’ health monitoring. ECG is a simple, non-invasive procedure used in the prediction and diagnosis of Cardiac Arrhythmia. This paper proposes a new transfer learning-based end to end approach to automate the cardiac arrhythmia classification. The proposed approach begins with gathering ECG Dataset and extracting beats after ECG beat segmentation. Developing a Model from scratch is time-consuming, so the concept of transfer learning is used. For transferring the knowledge to our ECG classification domain, the last layers of the model are fine-tuned such that model becomes more domain-specific to our target ECG data. Three pre-trained Convolutional Neural Networks (CNNs), AlexNet, Resnet18, GoogleNet are explored, and then, our model is designed by block wise fine-tuning each layer with different model training parameters. To update the weights and offsets, Adaptive moment estimation, Root means square propagation and Stochastic gradient descent with momentum (SGDM) are three different optimizers used. Investigating the results obtained by training fine-tuned models, we select the model which gives the system's best accuracy. MIT-BIH arrhythmia database is considered in this study. Performance of each Fine-tuned Model is evaluated by calculating Precision, Recall, Specificity, F-score and Accuracy. Moreover, our proposed fine-tuned Deep-CNN Model is effective and outperformed the existing models in the literature with accuracy of 99.56%.
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Benjamin, E.J., Virani, S.S., Callaway, C.W., Chamberlain, A.M., Chang, A.R., Cheng, S., Chiuve, S.E., Cushman, M., Delling, F.N., Deo, R., et al.: Heart disease and stroke statistics—2018 update: a report from the american heart association. Circulation 137(12), e67–e492 (2018)
Kumari, L.V.R., PadmaSai, Y., et al.: FPGA based arrhythmia detection. Procedia Comput. Sci. 57, 1 (2015)
Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.C.: Ecg arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116, 38–45 (2013)
Sai, Y.P., et al.: A review on arrhythmia classification using ecg signals. In: 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6. IEEE (2020)
Mohebbanaaz, L.V., Sai, Y.P.: Classification of arrhythmia beats using optimized K-nearest neighbor classifier. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds.) Intelligent Systems. Lecture Notes in Networks and Systems. Springer, Singapore (2021).
Mousavi, S., Fotoohinasab, A., Afghah, F.: Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks. PloS One 15(1), e0226990 (2020).
Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patientspecific ecg classification by 1-d convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2015)
Acharya, U.R., Fujita, H., Lih, O.S., Hagiwara, Y., Tan, J.H., Adam, M.: Automated detection of arrhythmias using different intervals of tachycardia ecg segments with convolutional neural network. Inf. Sci. 405, 81–90 (2017)
Zhai, X., Tin, C.: Automated ecg classification using dual heartbeat coupling based on convolutional neural network. IEEE Access 6, 27465–27472 (2018)
Sellami, A., Hwang, H.: A robust deep convolutional neural network with batch-weighted loss for heartbeat classification. Expert Syst. Appl. 122, 75–84 (2019)
Liu, W., Huang, Q., Chang, S., Wang, H., He, J.: Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed. Signal Process. Control 45, 22–32 (2018). https://doi.org/10.1016/j.bspc.2018.05.013
Li, D., Zhang, J., Zhang, Q., Wei, X.: Classification of ecg signals based on 1d convolution neural network. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6. IEEE (2017)
Yin, W., Yang, X., Zhang, L., Oki, E.: Ecg monitoring system integrated with ir-uwb radar based on cnn. IEEE Access 4, 6344–6351 (2016)
Salem, M., Taheri, S., Yuan, J.S.: Ecg arrhythmia classification using transfer learning from 2-dimensional deep cnn features. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2018)
Sharma, M., Acharya,U.R.: Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters. Inf. Med. Unlocked 16 (2019)
Wu, Q., Sun, Y., Yan, H., Wu, X.: Ecg signal classification with binarized convolutional neural network. Comput. Biol. Med. 121, 103800 (2020)
Li, Z., Zhou, D., Wan, L.: Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram. J. Electrocardiol. 58, 105–112 (2020)
Oh, S.L., Ng, E.Y., San Tan, R., Acharya, U.R.: Automated beat-wise arrhythmia diagnosis using modified u-net on extended electrocardiographic recordings with heterogeneous arrhythmia types. Comput. Biol. Med. 105, 92–101 (2019)
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)
Asgharzadeh-Bonab, A., Amirani, M.C., Mehri, A.: Spectral entropy and deep convolutional neural network for ecg beat classification. Biocybern. Biomed. Eng. 40(2), 691–700 (2020)
Kamaleswaran, R., Mahajan, R., Akbilgic, O.: A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length. Physiological measurement 39(3), 035006 (2018)
Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inf. Sci. 415, 190–198 (2017)
Strodthoff, N., Strodthoff, C.: Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiological measurement 40(1), 015001 (2019)
Yildirim, O.: A novel wavelet sequence based on deep¨ bidirectional lstm network model for ecg signal classification. Comput. Biol. Med. 96, 189–202 (2018)
Yıldırım, O., P lawiak, P., Tan, R.S., Acharya, U.R.: Arrhythmia detection using deep convolutional neural network with long duration ecg signals. Computers in biology and medicine 102, 411–420 (2018).
Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Mohebbanaaz, K.L.V.R., Sai, Y.P.: Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree. SIViP (2021). https://doi.org/10.1007/s11760-021-02009-x
Nurmaini, S., Darmawahyuni, A., Sakti Mukti, A.N., Rachmatullah, M.N., Firdaus, F., Tutuko, B.: Deep learning-based stacked denoising and autoencoder for ecg heartbeat classification. Electronics 9(1), 135 (2020)
Mohebbanaaz, S.Y.P., Kumari, L.R., et al.: Cognitive assistant deepnet model for detection of cardiac arrhythmia. Biomed. Signal Process. Control 71, 103221 (2022)
Yildirim, O., Talo, M., Ciaccio, E.J., San Tan, R., Acharya, U.R.: Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ecg records. Comput. Methods Programs Biomed. 197, 105740 (2020)
Pan, J., Tompkins, W.: Real time algorithm detection for qrs. IEEE Trans. Eng. Biomed Eng 32(3), 230–236 (1985)
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Mohebbanaaz, Kumar, L.V.R. & Sai, Y.P. A new transfer learning approach to detect cardiac arrhythmia from ECG signals. SIViP 16, 1945–1953 (2022). https://doi.org/10.1007/s11760-022-02155-w
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DOI: https://doi.org/10.1007/s11760-022-02155-w