Classification of ECG anomaly with dynamically-biased LSTM for continuous cardiac monitoring

Classification of ECG anomaly with dynamically-biased LSTM for continuous cardiac monitoring

This paper presents an electrocardiogram (ECG) signal classification model based on dynamically-biased Long Short-Term Memory (DB-LSTM) network. Compared to conventional LSTM networks, DB-LSTM introduces a set of parameters C which save the previous time-step cell gate states of the unit cell. Hence...

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Bibliographic Details
Main Authors: Hu, Jinhai, Goh, Wang Ling, Gao, Yuan
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2024
Subjects:
Engineering
Long short-term memory
ECG classification
DB-LSTM
Online Access:https://hdl.handle.net/10356/179112
https://ieeexplore.ieee.org/abstract/document/10181690

Internet

https://hdl.handle.net/10356/179112
https://ieeexplore.ieee.org/abstract/document/10181690

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