ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors

In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wearable Internet of Things (IoT) Edge sensors. The proposed network utilizes a novel hybrid architecture consisting of Long Short Term Memory (LSTM) cel...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on biomedical circuits and systems 2022-02, Vol.16 (1), p.24-35
Hauptverfasser:	Sivapalan, Gawsalyan, Nundy, Koushik Kumar, Dev, Soumyabrata, Cardiff, Barry, John, Deepu
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Anomalies Anomaly detection Arrhythmia Arrhythmias, Cardiac - diagnosis Classifiers Complexity Computer applications Datasets edge computing EKG Electrocardiography Feature extraction Gating Heart Rate Humans Internet of Things IoT sensors Laboratory tests Lightweight Long short-term memory LSTM MLP Multilayer perceptrons Multilayers Neural networks Neural Networks, Computer Power consumption Power demand Power management power reduction Sensors Training Wearable technology Wireless communication Wireless networks Wireless sensor networks
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!