ECGGAN is proposed as a framework for effective and interpretable electrocardiogram anomaly detection.
The paper introduces ECGGAN, a framework aiming to overcome the limitations of existing methods by providing effective and interpretable automatic ECG anomaly detection.
Heart is the most important organ of the human body, and Electrocardiogram (ECG) is an essential tool for clinical monitoring of heart health and detecting cardiovascular diseases. Automatic detection of ECG anomalies is of great significance and clinical value in healthcare. However, performing automatic anomaly detection for the ECG data is challenging because we not only need to accurately detect the anomalies but also need to provide clinically meaningful interpretation of the results. Existing works on automatic ECG anomaly detection either rely on hand-crafted designs of feature extraction algorithms which are typically too simple to deliver good performance, or deep learning for automatically extracting features, which is not interpretable.
Wang et al. (Fri,) conducted a other in Cardiovascular diseases. ECGGAN vs. Existing hand-crafted or deep learning methods was evaluated on Anomaly detection performance and interpretability. ECGGAN is proposed as a framework for effective and interpretable electrocardiogram anomaly detection.
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