.png){kind=icon}
.png){kind=icon}
Return
ECGencode: Compact and computationally efficient deep learning feature encoder for ECG signals
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.124775.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The visual interpretation of electrocardiogram (ECG) data is driven by human pattern recognition and requires in-depth medical knowledge. Although state-of-the-art deep learning models can automate and improve ECG feature extraction and analysis, they face deployment challenges, particularly on medical edge devices, due to their extensive computational demands and large parameter counts. To address these limitations, this work introduces ECGencode, a novel deep learning feature encoder optimised for ECG data. ECGencode is characterised by its intuitive, compact, and expert-inspired architecture, drawing from the Filter Bank Common Spatial Patterns method traditionally used in EEG signal analysis. It leverages depthwise and separable convolutions to provide state-of-the-art analysis performance at a fraction of the computational cost. Designed for intuitive model configuration and providing a latent space that retains the structure of an ECG, ECGencode can be incorporated into a wide variety of ECG analysis models. Furthermore, a novel spatial Gaussian noise regularisation technique is introduced, promoting the learning of more generalisable features. ECGencode stands out for its reduced computational requirements, using only 3.79% of the trainable parameters and 12.39% of the FLOPs compared to the benchmark model for normal sinus rhythm atrial fibrillation detection and new-onset prediction. Furthermore, an LSTM-extended ECGencode model matches the performance of leading multi-label classification models with a tenfold reduction in parameters. These attributes position ECGencode as a highly efficient tool for ECG analysis, with the potential to facilitate its adaptation in resource constrained cardiac diagnostics and monitoring settings.
Keywords:
Electrocardiography
Deep learning
Feature encoder
Computational efficiency
Arrhythmia detection
Predictive modelling
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W