A novel method for EKG anomaly detection based on the double sliding window technique.

Authors

Samuel A Torres-de-Anda, Luis F Cisneros-Sinencio, Alejandro Díaz-Sánchez, Rubén Salas-Cabrera

Published in

Computer methods in biomechanics and biomedical engineering. Pages 1-9. Jul 16, 2026. Epub Jul 16, 2026.

Abstract

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%.

PMID:
42460479
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.