Explainable artificial intelligence; Anomaly detection; Transformer; Variational autoencoder; Control chart; Support vector data description; Multivariate exponentially weighted moving average
Abstract :
[en] Anomaly detection is critical in various fields, especially in healthcare. The ability to identify anomalies from normal patterns can significantly contribute to early interventions and enhanced patient outcomes. In Electrocardiogram (ECG) analysis, timely detection of abnormal signals is essential for diagnosing and treating potentially life-threatening conditions. Despite the high performance of many AI-based anomaly detection methods, they often function as ``black boxes", making it difficult to interpret the results. In this paper, we propose integrating explainable artificial intelligence (XAI) into a transformer-based network combined with a support vector data description control chart and multivariate exponential weighted moving average technique (MEWMA-SVDD chart) for robust ECG monitoring. By incorporating XAI, we aim to enhance the transparency and reliability of our model, providing clear and interpretable results. We will demonstrate our proposed approach's effectiveness using a well-known ECG dataset and provide important insights into the detection mechanism. This approach illustrates the importance of combining advanced deep learning techniques with explainability to improve the reliability and efficiency of anomaly detection systems in monitoring healthcare.
Disciplines :
Computer science
Author, co-author :
Nguyen, Thi Thuy Van ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations
Heuchenne, Cédric ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations : Statistique appliquée à la gestion et à l'économie
Tran, Kim Duc; Dong A University, 50000 Danang, Vietnam > IAD
Tartare, Guillaume; University of Lille, 59000 Lille, France > ENSAIT, ULR 2461 - GEMTEX - Génie et Matériaux Textiles
Tran, Kim Phuc; University of Lille, 59000 Lille, France > ENSAIT, ULR 2461 - GEMTEX - Génie et Matériaux Textiles
Language :
English
Title :
Explainable Transformer-based Approach for ECG Anomaly Detection
Publication date :
10 January 2026
Event name :
EAI International Conference on Responsible Artificial Intelligence and Data Science
Event organizer :
European Alliance for Innovation & Dong A University
Event place :
Danang, Vietnam
Event date :
October 22-24, 2024
Audience :
International
Main work title :
First EAI International Conference proceedings: Responsible Artificial Intelligence and Data Science
Acheampong, F.A., Nunoo-Mensah, H., Chen, W.: Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif. Intell. Rev. 1–41 (2021)
Alabi RO Elmusrati M Leivo I Almangush A Mäkitie AA Machine learning explainability in nasopharyngeal cancer survival using lime and shap Sci. Rep. 2023 13 1 8984 2023NatSR.13.8984A 10.1038/s41598-023-35795-0 37268685 10238539
Albahri, A.S., et al.: A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion. Inf. Fusion (2023)
Amini K Mirzaei A Hosseini M Zandian H Azizpour I Haghi Y Assessment of electrocardiogram interpretation competency among healthcare professionals and students of ardabil university of medical sciences: a multidisciplinary study BMC Med. Educ. 2022 22 1 448 10.1186/s12909-022-03518-0 35681191 9179219
Anstine DM Isayev O Generative models as an emerging paradigm in the chemical sciences J. Am. Chem. Soc. 2023 145 16 8736 8750 2023JAChS.145.8736A 10.1021/jacs.2c13467 37052978 10141264
Bolhasani H Mohseni M Rahmani AM Deep learning applications for IoT in health care: a systematic review Inform. Med. Unlocked 2021 23 10.1016/j.imu.2021.100550 100550
Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299–12310 (2021)
Do, T.H., Nguyen, X.H., Nguyen, V.H., Nguyen, H.D., Truong, T.H., Kim, P.T.: Explainable anomaly detection for industrial control system cybersecurity. IFAC-PapersOnLine 55 (10), 1183–1188 (2022). 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022
Frusque G Mitchell D Blanche J Flynn D Fink O Non-contact sensing for anomaly detection in wind turbine blades: a focus-SVDD with complex-valued auto-encoder approach Mech. Syst. Signal Process. 2024 208 10.1016/j.ymssp.2023.111022 111022
Gao H Jia Z Detection of threats under inattentional blindness and perceptual load Curr. Psychol. 2017 36 733 739 10.1007/s12144-016-9460-0
Gaudilliere, P.L., Sigurthorsdottir, H., Aguet, C., Van Zaen, J., Lemay, M., Delgado-Gonzalo, R.: Generative pre-trained transformer for cardiac abnormality detection. In: 2021 Computing in Cardiology (CinC), vol. 48, pp. 1–4. IEEE (2021)
Jang, J.H., Kim, T.Y., Lim, H.S., Yoon, D.: Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder. PLoS One 16(12) (2021)
Kingma, D., Welling, M., et al.: An introduction to variational autoencoders. Found. Trends® Mach. Learn. 12(4), 307–392 (2019)
Kreitz, C., Furley, P., Memmert, D., Simons, D.: Inattentional blindness and individual differences in cognitive abilities. PLoS ONE 10(8) (2015)
Lang M A low-complexity model-free approach for real-time cardiac anomaly detection based on singular spectrum analysis and nonparametric control charts Technologies 2018 6 1 26 10.3390/technologies6010026
Liu B et al. Adaboost-based SVDD for anomaly detection with dictionary learning Expert Syst. Appl. 2024 238 10.1016/j.eswa.2023.121770 121770
Liu H Zhao Z Chen X Yu R She Q Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records Comput. Methods Programs Biomed. 2020 196 10.1016/j.cmpb.2020.105639 32674047 105639
Lundberg SM et al. From local explanations to global understanding with explainable AI for trees Nat. Mach. Intell. 2020 2 1 56 67 10.1038/s42256-019-0138-9 32607472 7326367
Lundberg, S.M., Erion, G.G., Lee, S.I.: Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888 (2018)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc. (2017)
Lundberg, S.M., et al.: Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2(10), 749–760 (2018)
Martins, T., De Almeida, A.M., Cardoso, E., Nunes, L.: Explainable artificial intelligence (XAI): a systematic literature review on taxonomies and applications in finance. IEEE Access (2023)
Muzammil, M.A., et al.: Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J. Electrocardiol. (2024)
Nguyen, T.T.V., Heuchenne, C., Tran, K.D., Tran, K.P.: A novel transformer-based anomaly detection approach for ECG monitoring healthcare system. In: International Conference on Safety and Security in IoT, pp. 111–129. Springer, Cham (2023)
Panwar H Gupta P Siddiqui MK Morales-Menendez R Bhardwaj P Singh V A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images Chaos Solitons Fractals 2020 140 4155272 10.1016/j.chaos.2020.110190 32836918 7413068 110190
Prendin F Pavan J Cappon G Del Favero S Sparacino G Facchinetti A The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using shap Sci. Rep. 2023 13 1 16865 2023NatSR.1316865P 10.1038/s41598-023-44155-x 37803177 10558434
Raza A Tran KP Koehl L Li S Anofed: adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description Eng. Appl. Artif. Intell. 2023 121 10.1016/j.engappai.2023.106051 106051
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Saint-Lot, J., Imbert, J., Dehais, F.: Red alert: a cognitive countermeasure to mitigate attentional tunneling. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (2020)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Shah, H.A., Saeed, F., Diyan, M., Almujally, N.A., Kang, J.M.: ECG-transcovnet: a hybrid transformer model for accurate arrhythmia detection using electrocardiogram signals. CAAI Trans. Intell. Technol. (2024)
Shin DH Park RC Chung K Decision boundary-based anomaly detection model using improved anogan from ECG data IEEE Access 2020 8 108664 108674 10.1109/ACCESS.2020.3000638
Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016)
Singh S Mahmood A The NLP cookbook: modern recipes for transformer based deep learning architectures IEEE Access 2021 9 68675 68702 10.1109/ACCESS.2021.3077350
Smola, A., Scholkopf, B., Muller, K.: The connection between regularization operators and support vector kernels. Neural Netw. 637–649 (1998)
Suman G Prajapati D Control chart applications in healthcare: a literature review Int. J. Metrol. Qual. Eng. 2018 9 5 10.1051/ijmqe/2018003
Tang, A., Castagliola, P., Hu, X., Xie, F.: An assessment for the conditional performance of an support vector data description (SVDD)-based chart. Qual. Reliab. Eng. Int. 1–17 (2022)
Tax DMJ Duin RPW Support vector data description Mach. Learn. 2004 54 1 45 66 10.1023/B:MACH.0000008084.60811.49
Vapnik VN Statistical Learning Theory 1998 Hoboken Wiley
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc. (2017)
Zhang C et al. VESC: a new variational autoencoder based model for anomaly detection Int. J. Mach. Learn. Cybern. 2023 14 3 683 696 10.1007/s13042-022-01657-w
Zhang Z Deng X Anomaly detection using improved deep SVDD model with data structure preservation Pattern Recogn. Lett. 2021 148 1 6 2021PaReL.148..1Z 10.1016/j.patrec.2021.04.020
Zhou H Kan C Tensor-based ECG anomaly detection toward cardiac monitoring in the internet of health things Sensors 2021 21 12 4173 2021Senso.21.4173Z 10.3390/s21124173 34204575 8234952
