Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors
Author:
Affiliation:
1. Meta Reality Lab, Redmond, WA, USA
2. Biosolution Limited, Dublin, Ireland
3. Maker Village, Digital University Kerala, Thiruvananthapuram, India
4. School of Electrical and Electronic Engineering, University College Dublin, Dublin 4, Ireland
Funder
Microelectronic Circuit Centre Ireland
JEDAI Project under the Horizon 2020 FET Chist-Era Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/10194321/10078241.pdf?arnumber=10078241
Reference39 articles.
1. Floating search methods in feature selection
2. Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms
3. An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-Processor Design for QRS Detection
4. An association rule mining-based methodology for automated detection of ischemic ECG beats
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