Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors

Electrocardiogram (ECG) analysis is widely used in the diagnosis of cardiovascular diseases. This paper proposes an explainable rule-mining strategy for prioritizing abnormal class detection in ECG data. The proposed method utilizes a biased-trained Artificial Neural Network (ANN) with input feature...

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Veröffentlicht in:	IEEE internet of things journal Jg. 10; H. 15; S. 1
Hauptverfasser:	Sivapalan, Gawsalyan, Nundy, Koushik Kumar, James, Alex, Cardiff, Barry, John, Deepu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	ANN Anomalies Anomaly Detection Classification algorithms Complexity theory ECG Electrocardiography Explainability Feature extraction HRV Internet of Things IoT Edge Sensors Machine learning Power consumption Real time Rule Based Algorithm Sensitivity Sensors Training
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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