Enabling on-device classification of ECG with compressed learning for health IoT-Reference-Cited by-同舟云学术

Enabling on-device classification of ECG with compressed learning for health IoT

Published:2021-09 Issue: Volume:115 Page:105188
ISSN:0026-2692
Container-title:Microelectronics Journal
language:en
Short-container-title:Microelectronics Journal

Author:

Li Wenzhuo^ORCID,Chu Haoming,Huang Boming,Huan Yuxiang^ORCID,Zheng Lirong^ORCID,Zou Zhuo

Publisher

Elsevier BV

Subject

General Engineering

Reference34 articles.

1. Recommendation to use wearable-based mhealth in closed-loop management of acute cardiovascular disease patients during the COVID-19 pandemic;Ji;IEEE J. Biomed. Health Inf.,2021

2. An ultra-low voltage chaos-based true random number generator for IoT applications;Hsueh;Microelectron. J.,2019

3. Live demonstration: An AIoT wearable ECG patch with decision tree for arrhythmia analysis;Lin,2019

4. An energy-efficient ECG processor with weak-strong hybrid classifier for arrhythmia detection;Chen;IEEE Trans. Circuits Syst. II,2018

5. A 3D tiled low power accelerator for convolutional neural network;Huan,2018

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3. Abnormal ECG Detection in Wearable Devices Using Compressed Learning;2023 IEEE International Conference on Networking, Sensing and Control (ICNSC);2023-10-25

4. An Energy Aware VLSI Framework for ECG Features Extraction for Wearable Devices in Healthcare;2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC);2023-10-11

5. ECG Signals Analysis based on Compressed Sensing and Learning Techniques for Heart Disease Recognition;2023 7th International Multi-Topic ICT Conference (IMTIC);2023-05-10