Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

Arrhythmia detection through deep learning is mainly classified through supervised learning. Supervised learning progresses through the labeled data. However, in the medical field, it is challenging to collect ECG data of patients with arrhythmia than ECG data of healthy people, and thus data bias o...

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Bibliographic Details
Main Authors: Dong-Hoon Shin, Roy C. Park, Kyungyong Chung
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Heart disease
arrhythmia
health care
deep learning
electrocardiogram
generative adversarial network
Online Access:https://ieeexplore.ieee.org/document/9110549/

Internet

https://ieeexplore.ieee.org/document/9110549/

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