Key result
Double-sliding-window technique achieves ~95% accuracy for real-time ECG anomaly detection.
Why the study?
Traditional ECG anomaly detection methods rely on fixed thresholding or machine-learning models, which can be computationally expensive.
Does a double-sliding-window technique improve real-time EKG anomaly detection compared to traditional methods?
Does a double-sliding-window technique improve real-time EKG anomaly detection compared to traditional methods?
Answers are grounded in this paper and cite their sources.
A novel double-sliding-window technique for ECG anomaly detection demonstrates high accuracy (95.33%) and low computational cost, making it suitable for real-time systems.
Traditional detection methods often rely on fixed thresholding or machine-learning models, which can be computationally expensive. This study introduces a double-sliding-window technique to detect anomalies in ECG signals through adaptive-signal analysis. This method uses two independent sliding windows to dynamically track signal variations, enabling real-time identification of deviations in heart rhythm without prior knowledge of data distribution. The detection module achieved an average execution-time of 0.03 seconds with using less than 16 MB of memory, supporting its suitability for real-time systems. Furthermore, confusion matrix analysis yielded an accuracy of 95.33%, a sensitivity of 95.00%, a precision of 100.00%, and a specificity of 100.00%.
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Torres-de-Anda et al. (2026) studied ECG anomalies. Double-sliding-window technique was evaluated on Anomaly detection accuracy. A double-sliding-window technique for real-time ECG anomaly detection achieved 95.33% accuracy, 95.00% sensitivity, and 100.00% specificity with an average execution time of 0.03 seconds.
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