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A novel transformer‐based ECG dimensionality reduction stacked auto‐encoders for arrhythmia beat detection by Chun Ding; Shenglun Wang; Xiaopeng Jin; Zhaoze Wang; Junsong Wang is a Medicine article available to read on EtoBox.

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## Abstract ## Background Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long‐distance reliance. ## Purpose To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto‐encoders model using Transformer for ECG‐based arrhythmia detection. And overcome the challenges of long‐term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing. ## Methods In this paper, a Transformer‐Based ECG Dimensionality Reduction Stacked Auto‐encoders model is proposed for ECG‐based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This result

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Author

Chun Ding; Shenglun Wang; Xiaopeng Jin; Zhaoze Wang; Junsong Wang

Publisher

Wiley

Published

2023

Language

EN

Field

Medicine (Health Sciences)