ECG Classification With Event-Driven Sampling

ECG Classification With Event-Driven Sampling

Electrocardiogram (ECG) data’s high dimensionality challenges real-time arrhythmia classification. Our approach employs functional approximation to condense ECG recordings into a compact feature set for simpler classification using Chebyshev polynomials. These polynomials, with 200 time p...

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Bibliographic Details
Main Authors: Maryam Saeed, Olev Martens, Benoit Larras, Antoine Frappe, Deepu John, Barry Cardiff
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Level-crossing ADC
electrocardiograms
functional approximation
Chebyshev polynomials
artificial neural networks
arrhythmia
Online Access:https://ieeexplore.ieee.org/document/10427994/

Internet

https://ieeexplore.ieee.org/document/10427994/

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