Early prediction of sudden cardiac death risk with Nested LSTM based on electrocardiogram sequential features

Early prediction of sudden cardiac death risk with Nested LSTM based on electrocardiogram sequential features

Abstract Electrocardiogram (ECG) signals are very important for heart disease diagnosis. In this paper, a novel early prediction method based on Nested Long Short-Term Memory (Nested LSTM) is developed for sudden cardiac death risk detection. First, wavelet denoising and normalization techniques are...

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Bibliographic Details
Main Authors: Ke Wang, Kai Zhang, Banteng Liu, Wei Chen, Meng Han
Format: Article
Language:English
Published: BMC 2024-04-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Time series prediction
Nested LSTM
ECG signal
Sudden cardiac death
Online Access:https://doi.org/10.1186/s12911-024-02493-4

Internet

https://doi.org/10.1186/s12911-024-02493-4

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