A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device

• URL: http://arxiv.org/abs/2211.13005v1
• Date: Sun, 20 Nov 2022 16:22:30 GMT
• Title: A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device
• Authors: Zongyan Yao, Xilin Liu
• Abstract summary: This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. We tested a reduced-sized version of the proposed model on a low-cost Arduino Nano 33 BLE board and it was fully functional and accurate.
• Score: 2.5672176409865686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The Fpz-Cz EEG signals from a publicly available Sleep-EDF dataset are used to train and test the model. Four convolutional filter layers were used to extract features and reduce the data dimension. Then, transformers were utilized to learn the time-variant features of the data. To improve performance, we also implemented a subject specific training before the inference (i.e., prediction) stage. With the subject specific training, the F1 score was 0.91, 0.37, 0.84, 0.877, and 0.73 for wake, N1-N3, and rapid eye movement (REM) stages, respectively. The performance of the model was comparable to the state-of-the-art works with significantly greater computational costs. We tested a reduced-sized version of the proposed model on a low-cost Arduino Nano 33 BLE board and it was fully functional and accurate. In the future, a fully integrated wireless EEG sensor with edge DL will be developed for sleep research in pre-clinical and clinical experiments, such as real-time sleep modulation.

Related papers

• Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons [0.0]
  We present an algorithmically reinterpreted the adaptive integrate-and-fire (ALIF) model. We obtain over a $50times$ training speedup using small DTs on synthetic benchmarks. We also showcase how our model makes it possible to quickly and accurately fit real electrophysiological recordings of cortical neurons.
  arXiv  Detail & Related papers  (2023-11-19T18:21:45Z)
• Efficient Model Adaptation for Continual Learning at the Edge [15.334881190102895]
  Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. Data distributions often shift over time due to changes in environmental factors, sensor characteristics, and task-of-interest. This paper presents theAdaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
  arXiv  Detail & Related papers  (2023-08-03T23:55:17Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• 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)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage [18.059360820527687]
  Sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy.
  arXiv  Detail & Related papers  (2023-02-17T07:37:54Z)
• Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring [1.6339105551302067]
  We argue that most deep learning solutions for sleep scoring are limited in their real-world applicability as they are hard to train, deploy, and reproduce. In this work, we revisit the problem of sleep stage classification using classical machine learning. Results show that state-of-the-art performance can be achieved with a conventional machine learning pipeline.
  arXiv  Detail & Related papers  (2022-07-15T21:03:11Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• 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)
• Wireless Localisation in WiFi using Novel Deep Architectures [4.541069830146568]
  This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. We present a novel shallow neural network (SNN) in which features are extracted from the channel state information corresponding to WiFi subcarriers received on different antennas.
  arXiv  Detail & Related papers  (2020-10-16T22:48:29Z)
• REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild [62.36144064259933]
  We propose REST, a new method that simultaneously tackles both issues via adversarial training and controlling the Lipschitz constant of the neural network. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.
  arXiv  Detail & Related papers  (2020-01-29T17:23:16Z)

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.