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
Journal title :
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISSN :
1867-8211
Special issue title :
Responsible Artificial Intelligence and Data Science (RAIDS 2024)
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