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LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from
Insights
LTH-ECG significantly reduces deep learning model size for atrial fibrillation (AF) detection by 142x. This enables practical deployment of AF screening on wearable devices with minimal performance loss.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Atrial Fibrillation (AF) is a significant cause of cardiovascular morbidity, including stroke and heart failure.
- Electrocardiograms (ECG) are crucial for diagnosing AF, with single-lead ECG offering practical advantages for portable devices.
- Current deep learning (DL) models for AF detection from ECG are highly accurate but too large for practical deployment on resource-constrained devices.
Purpose of the Study:
- To develop a computationally efficient deep learning model for single-lead ECG-based Atrial Fibrillation detection.
- To significantly reduce the model size of existing DL algorithms without compromising diagnostic performance.
- To enable the deployment of AF screening tools in wearable and implantable devices.
Main Methods:
- Proposed LTH-ECG, a novel goal-driven method based on the Lottery Ticket Hypothesis (LTH) for iterative model pruning.
- Implemented LTH-ECG to achieve over-pruning avoidance during the model compression process.
- Evaluated the performance of the compressed model on single-lead short-time ECG signals for AF classification.
Main Results:
- LTH-ECG achieved a 142x reduction in model size.
- The model compression resulted in an insignificant loss of classification performance, with less than a 1% penalty in test F1-score.
- The compressed model maintains cardiologist-level accuracy for AF detection.
Conclusions:
- LTH-ECG effectively addresses the deployment challenges of DL models for AF detection in wearable devices.
- The method enables practical remote screening of AF, empowering patients with on-demand monitoring capabilities.
- LTH-ECG serves as an early warning system for timely detection and management of atrial fibrillation.
Abstract:
Atrial Fibrillation (AF) is a kind of arrhythmia, which is a major morbidity factor, and AF can lead to stroke, heart failure and other cardiovascular complications. Electrocardiogram (ECG) is the basic marker to test the condition of heart and it can effectively detect AF condition. Single lead ECG has the practical advantage for being small form factor and it is easy to deploy. With the sophistication of the current deep learning (DL) models, researchers have been able to construct cardiologist-level models to detect different arrhythmias including AF condition detection from single lead short-time ECG signals. However, such models are computationally expensive and require huge memory size for deployment (more than 100 MB to deploy state-of-the-art 34-layer convolutional neural network-based ECG classification model). Such models need to be significantly trimmed with insignificantly loss of its classification performance for deployment in practical applications like single lead ECG classification in wearable and implantable devices. We have found that classical deep learning model compression techniques like pruning, quantization are not capable of substantial model size reduction without compromising on the model performance. In this paper, we propose LTH-ECG, which is our novel goal-driven winning lottery ticket discovery method, where lottery ticket hypothesis (LTH)-based iterative model pruning is used with the aim of over-pruning avoidance. LTH-ECG reduces the model size by 142x times with insignificant loss of classification performance (less than 1 % test F1-score penalty). Clinical Relevance- LTH-ECG will enable practical deployment for remote screening of AF condition using single lead short-time ECG recordings such that patients can on-demand monitor AF condition remotely through wearable ECG sensing devices and report cardiological abnormality to the concerned physician. LTH-ECG acts as an early warning system for effective AF condition screening.
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