Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

• URL: http://arxiv.org/abs/2208.11933v1
• Date: Thu, 25 Aug 2022 08:36:44 GMT
• Title: Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels
• Authors: Saeed Montazeri Moghadam, P\"aivi Nevalainen, Nathan J. Stevenson, Sampsa Vanhatalo
• Abstract summary: We develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. A deep learning -based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86%) in all bipolar derivations available from the 4-electrode recordings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods: A deep learning -based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep detector, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs. Results: The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86%) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output. Conclusions: Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors. Significance: The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.

Related papers

• Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification [1.565361244756411]
  We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition. Our proposed SE-Resnet-Transformer model provides an accurate classification of five distinct sleep stages from raw EOG signal.
  arXiv  Detail & Related papers  (2023-09-25T16:23:39Z)
• 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)
• Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data [67.60224656603823]
  Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
  arXiv  Detail & Related papers  (2023-01-17T15:18:45Z)
• ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation [4.014524824655106]
  This work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint-temporal graphs. The proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.
  arXiv  Detail & Related papers  (2022-12-09T14:34:58Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• 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)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  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.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)

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.