Detecting anomalies is critical in various fields, especially in healthcare. The ability to identify abnormal patterns from normal ones can help clinicians make early interventions and improve patient outcomes. In Electrocardiogram (ECG) analysis, timely detection of unusual signals is crucial for diagnosing and treating serious, life-threatening health problems. However, although many AI-based anomaly detection models offer high performance, they often function as “black boxes”, making us 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) to obtain a robust ECG anomaly detection model. By incorporating XAI, we aim to enhance the transparency and reliability of our model, providing clear and interpretable results. We will demonstrate our approach’s effectiveness by 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 XAI to improve the reliability and efficiency of anomaly detection systems in monitoring healthcare.