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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE internet of things journal Ročník 10; číslo 15; s. 1
Hlavní autori:	Sivapalan, Gawsalyan, Nundy, Koushik Kumar, James, Alex, Cardiff, Barry, John, Deepu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!