Analysing Performances of DL-Based ECG Noise Classification Models Deployed in Memory-Constraint IoT-Enabled Devices-Reference-Cited by-同舟云学术

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Analysing Performances of DL-Based ECG Noise Classification Models Deployed in Memory-Constraint IoT-Enabled Devices

Published:2024-02 Issue:1 Volume:70 Page:704-714
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Rahman Saifur¹^ORCID,Pal Shantanu¹^ORCID,Yearwood John¹^ORCID,Karmakar Chandan¹^ORCID

Affiliation:

1. School of Information Technology, Deakin University, Geelong, VIC, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10445720.pdf?arnumber=10445720

Reference26 articles.

1. A lightweight convolutional neural network hardware implementation for wearable heart rate anomaly detection

2. Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring

3. Signal quality and data fusion for false alarm reduction in the intensive care unit

4. Motion Artifact Reduction Algorithm for Wearable Electrocardiogram Monitoring Systems

5. ECG-based real-time arrhythmia monitoring using quantized deep neural networks: A feasibility study

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ECG Quality Detection and Noise Classification for Wearable Cardiac Health Monitoring Devices;2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2024-06-27

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