Multistage Pruning of CNN Based ECG Classifiers for Edge Devices

• URL: http://arxiv.org/abs/2109.00516v1
• Date: Tue, 31 Aug 2021 17:51:15 GMT
• Title: Multistage Pruning of CNN Based ECG Classifiers for Edge Devices
• Authors: Xiaolin Li, Rajesh Panicker, Barry Cardiff, and Deepu John
• Abstract summary: Convolutional neural network (CNN) based deep learning has been used successfully to detect anomalous beats in ECG. The computational complexity of existing CNN models prohibits them from being implemented in low-powered edge devices. This paper presents a novel multistage pruning technique that reduces CNN model complexity with negligible loss in performance.
• Score: 9.223908421919733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using smart wearable devices to monitor patients electrocardiogram (ECG) for real-time detection of arrhythmias can significantly improve healthcare outcomes. Convolutional neural network (CNN) based deep learning has been used successfully to detect anomalous beats in ECG. However, the computational complexity of existing CNN models prohibits them from being implemented in low-powered edge devices. Usually, such models are complex with lots of model parameters which results in large number of computations, memory, and power usage in edge devices. Network pruning techniques can reduce model complexity at the expense of performance in CNN models. This paper presents a novel multistage pruning technique that reduces CNN model complexity with negligible loss in performance compared to existing pruning techniques. An existing CNN model for ECG classification is used as a baseline reference. At 60% sparsity, the proposed technique achieves 97.7% accuracy and an F1 score of 93.59% for ECG classification tasks. This is an improvement of 3.3% and 9% for accuracy and F1 Score respectively, compared to traditional pruning with fine-tuning approach. Compared to the baseline model, we also achieve a 60.4% decrease in run-time complexity.

Related papers

• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN [3.8791511769387625]
  We have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
  arXiv  Detail & Related papers  (2023-03-06T13:26:29Z)
• Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks [11.964843902569925]
  Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. Wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity.
  arXiv  Detail & Related papers  (2022-10-27T14:24:48Z)
• Classification of ECG based on Hybrid Features using CNNs for Wearable Applications [2.0999222360659604]
  We demonstrate improved performance for ECG classification using hybrid features and three different models. An RR interval features based model proposed in this work achieved an accuracy of 98.98%, which is an improvement over the baseline model. Another model combining the frequency features and the RR interval features was developed, which achieved a high accuracy of 99% with good sustained performance in noisy environments.
  arXiv  Detail & Related papers  (2022-06-14T12:14:40Z)
• Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks [25.160063477248904]
  A convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram signals. The model demonstrates high performance despite being trained on limited, variable-length input data. The final model achieved a 91.1% model compression ratio while maintaining high model accuracy of 91.7% and less than 1% loss.
  arXiv  Detail & Related papers  (2022-06-14T11:47:04Z)
• Effective Model Sparsification by Scheduled Grow-and-Prune Methods [73.03533268740605]
  We propose a novel scheduled grow-and-prune (GaP) methodology without pre-training the dense models. Experiments have shown that such models can match or beat the quality of highly optimized dense models at 80% sparsity on a variety of tasks.
  arXiv  Detail & Related papers  (2021-06-18T01:03:13Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• DENS-ECG: A Deep Learning Approach for ECG Signal Delineation [15.648061765081264]
  This paper proposes a deep learning model for real-time segmentation of heartbeats. The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model.
  arXiv  Detail & Related papers  (2020-05-18T13:13:41Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.