Automatic detection of microsleep episodes with deep learning

• URL: http://arxiv.org/abs/2009.03027v2
• Date: Tue, 2 Mar 2021 17:40:15 GMT
• Title: Automatic detection of microsleep episodes with deep learning
• Authors: Alexander Malafeev, Anneke Hertig-Godeschalk, David R. Schreier, Jelena Skorucak, Johannes Mathis, Peter Achermann
• Abstract summary: Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
• Score: 55.41644538483948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30-s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e. with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. Code of algorithms ( https://github.com/alexander-malafeev/microsleep-detection ) and data ( https://zenodo.org/record/3251716 ) are available.

Related papers

• NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG [2.3310092106321365]
  Sleep stage classification is a pivotal aspect of diagnosing sleep disorders and evaluating sleep quality. Recent advancements in deep learning have substantially propelled the automation of sleep stage classification. This paper introduces NeuroNet, a self-supervised learning framework designed to harness unlabeled single-channel sleep electroencephalogram (EEG) signals.
  arXiv  Detail & Related papers  (2024-04-10T18:32:22Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• 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)
• Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware [50.380319968947035]
  Recent deep learning approaches have reached accuracy in such tasks, but their implementation on conventional embedded solutions is still computationally very and energy expensive. We propose a new benchmark for computing tactile pattern recognition at the edge through letters reading. We trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihimorphic chip for efficient inference. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy
  arXiv  Detail & Related papers  (2022-05-30T14:30:45Z)
• Predicting Sleeping Quality using Convolutional Neural Networks [6.236890292833385]
  We propose a Convolution Neural Network (CNN) architecture that improves the classification performance. We benchmark the classification performance from different methods, including traditional machine learning methods. The accuracy, sensitivity, specificity, precision, recall, and F-score are reported and will serve as a baseline to simulate the research.
  arXiv  Detail & Related papers  (2022-04-24T21:48:54Z)
• Sleep Staging Based on Serialized Dual Attention Network [0.0]
  We propose a deep learning model SDAN based on raw EEG. It serially combines the channel attention and spatial attention mechanisms to filter and highlight key information. It achieves excellent results in the N1 sleep stage compared to other methods.
  arXiv  Detail & Related papers  (2021-07-18T13:18:12Z)
• Sleep syndromes onset detection based on automatic sleep staging algorithm [0.0]
  A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information. A deep convolutional LSTM neural network is trained for sleep stage classification. The code evaluation showed impressive results, reaching an accuracy of 86.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss of 0.09.
  arXiv  Detail & Related papers  (2021-07-07T15:38:47Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection [0.0]
  We propose a deep learning approach for sleep EEG event detection called Recurrent Event Detector (RED) RED uses one of two input representations: a) the time-domain EEG signal, or b) a complex spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT) When evaluated on the MASS dataset, our detectors outperform the state of the art in both sleep spindle and K-complex detection with a mean F1-score of at least 80.9% and 82.6%, respectively.
  arXiv  Detail & Related papers  (2020-05-15T21:48:26Z)

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.