Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device

Arrhythmia can lead to severe complications and early detection of arrhythmia is crucial to prevent progression. Electrocardiograms are the most reliable measure for detecting arrhythmia. This study aims to implement a practical ultra-edge-computing system for automated arrhythmia classification, in...

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Bibliographic Details
Published in:IEEE Access
Main Authors: Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park
Format: Article
Language:English
Published: IEEE 2024-01-01
Subjects:
Edge computing
knowledge distillation
model compression
on-device inference
pruning
Online Access:https://ieeexplore.ieee.org/document/10704628/

Internet

https://ieeexplore.ieee.org/document/10704628/

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