Abstract
Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering that no training dataset can exhaust all possible cardiac disorders. This paper proposes using anomaly detection to identify any unhealthy status, with normal ECGs solely for training. However, detecting anomalies in ECG can be challenging due to significant inter-individual differences and anomalies present in both global rhythm and local morphology. To address this challenge, this paper introduces a novel multi-scale cross-restoration framework for ECG anomaly detection and localization that considers both local and global ECG characteristics. The proposed framework employs a two-branch autoencoder to facilitate multi-scale feature learning through a masking and restoration process, with one branch focusing on global features from the entire ECG and the other on local features from heartbeat-level details, mimicking the diagnostic process of cardiologists. Anomalies are identified by their high restoration errors. To evaluate the performance on a large number of individuals, this paper introduces a new challenging benchmark with signal point-level ground truths annotated by experienced cardiologists. The proposed method demonstrates state-of-the-art performance on this benchmark and two other well-known ECG datasets. The benchmark dataset and source code are available at: https://github.com/MediaBrain-SJTU/ECGAD
A. Jiang and C. Huang—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M.A.: Usad: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3395–3404 (2020)
Chalapathy, R., Menon, A.K., Chawla, S.: Robust, deep and inductive anomaly detection. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 36–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_3
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019)
Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4027–4035 (2021)
van Gent, P., Farah, H., van Nes, N., van Arem, B.: Analysing noisy driver physiology real-time using off-the-shelf sensors: Heart rate analysis software from the taking the fast lane project. J. Open Res. Softw. 7(1) (2019)
Keogh, E., Lin, J., Fu, A.: Hot sax: finding the most unusual time series subsequence: Algorithms and applications. In: Proceedings of the IEEE International Conference on Data Mining, pp. 440–449. Citeseer (2004)
Khan, M.G.: Step-by-step method for accurate electrocardiogram interpretation. In: Rapid ECG Interpretation, pp. 25–80. Humana Press, Totowa, NJ (2008)
Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-d convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2015)
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703–716. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30490-4_56
Li, H., Boulanger, P.: A survey of heart anomaly detection using ambulatory electrocardiogram (ECG). Sensors 20(5), 1461 (2020)
Liu, S., et al.: Time series anomaly detection with adversarial reconstruction networks. IEEE Trans. Knowl. Data Eng. (2022)
Mao, Y., Xue, F.-F., Wang, R., Zhang, J., Zheng, W.-S., Liu, H.: Abnormality detection in chest X-Ray images using uncertainty prediction autoencoders. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 529–538. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_51
Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393–4402. PMLR (2018)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Shaker, A.M., Tantawi, M., Shedeed, H.A., Tolba, M.F.: Generalization of convolutional neural networks for ECG classification using generative adversarial networks. IEEE Access 8, 35592–35605 (2020)
Shen, L., Yu, Z., Ma, Q., Kwok, J.T.: Time series anomaly detection with multiresolution ensemble decoding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9567–9575 (2021)
Tuli, S., Casale, G., Jennings, N.R.: Tranad: deep transformer networks for anomaly detection in multivariate time series data. In: International Conference on Very Large Databases 15(6), pp. 1201–1214 (2022)
Van Gent, P., Farah, H., Van Nes, N., Van Arem, B.: Heartpy: a novel heart rate algorithm for the analysis of noisy signals. Transport. Res. F: Traffic Psychol. Behav. 66, 368–378 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S., Kumar, R.: ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access 6, 9767–9773 (2018)
Wagner, P., et al.: Ptb-xl, a large publicly available electrocardiography dataset. Sci. Data 7(1), 1–15 (2020)
Wang, J., et al.: Automated ECG classification using a non-local convolutional block attention module. Comput. Methods Programs Biomed. 203, 106006 (2021)
Wolleb, J., Bieder, F., Sandkühler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: Medical Image Computing and Computer Assisted Intervention-MICCAI 2022, pp. 35–45. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_4
Xu, J., Wu, H., Wang, J., Long, M.: Anomaly transformer: time series anomaly detection with association discrepancy. In: International Conference on Learning Representations (2022)
Ye, F., Huang, C., Cao, J., Li, M., Zhang, Y., Lu, C.: Attribute restoration framework for anomaly detection. IEEE Trans. Multimedia 24, 116–127 (2022)
Zhang, C., et al.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1409–1416 (2019)
Zhang, Y., Chen, Y., Wang, J., Pan, Z.: Unsupervised deep anomaly detection for multi-sensor time-series signals. IEEE Trans. Knowl. Data Eng. 35(2), 2118–2132 (2021)
Zheng, Y., Liu, Z., Mo, R., Chen, Z., Zheng, W.s., Wang, R.: Task-oriented self-supervised learning for anomaly detection in electroencephalography. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 193–203. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_19
Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
Acknowledgement
This work is supported by the National Key R &D Program of China (No. 2022ZD0160702), STCSM (No. 22511106101, No. 18DZ2270700, No. 21DZ1100100), 111 plan (No. BP0719010), the Youth Science Fund of National Natural Science Foundation of China (No. 7210040772) and National Facility for Translational Medicine (Shanghai) (No. TMSK-2021-501), and State Key Laboratory of UHD Video and Audio Production and Presentation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, A. et al. (2023). Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-43907-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43906-3
Online ISBN: 978-3-031-43907-0
eBook Packages: Computer ScienceComputer Science (R0)