Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In this work, a convolutional neural network model is developed for detecting atrial fibrillation...
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| Main Authors | , , , , , , , , |
|---|---|
| Format | Journal Article |
| Language | English |
| Published |
14.06.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Deep neural networks (DNN) are a promising tool in medical applications.
However, the implementation of complex DNNs on battery-powered devices is
challenging due to high energy costs for communication. In this work, a
convolutional neural network model is developed for detecting atrial
fibrillation from electrocardiogram (ECG) signals. The model demonstrates high
performance despite being trained on limited, variable-length input data.
Weight pruning and logarithmic quantisation are combined to introduce sparsity
and reduce model size, which can be exploited for reduced data movement and
lower computational complexity. The final model achieved a 91.1% model
compression ratio while maintaining high model accuracy of 91.7% and less than
1% loss. |
|---|---|
| AbstractList | Deep neural networks (DNN) are a promising tool in medical applications.
However, the implementation of complex DNNs on battery-powered devices is
challenging due to high energy costs for communication. In this work, a
convolutional neural network model is developed for detecting atrial
fibrillation from electrocardiogram (ECG) signals. The model demonstrates high
performance despite being trained on limited, variable-length input data.
Weight pruning and logarithmic quantisation are combined to introduce sparsity
and reduce model size, which can be exploited for reduced data movement and
lower computational complexity. The final model achieved a 91.1% model
compression ratio while maintaining high model accuracy of 91.7% and less than
1% loss. |
| Author | Panicker, Rajesh C Choong, Benjamin Chen Ming John, Deepu Wang, Shuhui Chang, Xiu Qi Han, Rui Chew, Ann Feng Xiaolin, Li He, Wang |
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| BackLink | https://doi.org/10.48550/arXiv.2206.07649$$DView paper in arXiv |
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| Snippet | Deep neural networks (DNN) are a promising tool in medical applications.
However, the implementation of complex DNNs on battery-powered devices is
challenging... |
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| Title | Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks |
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