Sleep Staging Using Plausibility Score: A Novel Feature Selection Method Based on Metric Learning

Zhang, Tao; Jiang, Zhonghui; Li, Dan; Wei, Xiao; Guo, Bing; Huang, Wu; Xu, Guangquan

doi:10.1109/jbhi.2020.2993644

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…Several technologies exist for measuring sleep stage signals, for instance, EEG [122], EOG [123], acceleration [124], respiratory [125], and photoplethysmography [126]. The most informative of these methods is EEG because it shows that the alternation in brain waves follows the awake and sleep stages [127][128][129]. By interpretating signals during the transition from NREM to REM sleep, one can gain insight into various aspects of the sleep process, such as sleep efficiency in individuals with insomnia, prognosis of coma recovery, and the detection of drowsiness [130].…”

Section: Orexinsmentioning

confidence: 99%

Does EGFR Signaling Mediate Orexin System Activity in Sleep Initiation?

Kniazkina

Dyachuk

2023

IJMS

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Sleep–wake cycle disorders are an important symptom of many neurological diseases, including Parkinson’s disease, Alzheimer’s disease, and multiple sclerosis. Circadian rhythms and sleep–wake cycles play a key role in maintaining the health of organisms. To date, these processes are still poorly understood and, therefore, need more detailed elucidation. The sleep process has been extensively studied in vertebrates, such as mammals and, to a lesser extent, in invertebrates. A complex, multi-step interaction of homeostatic processes and neurotransmitters provides the sleep–wake cycle. Many other regulatory molecules are also involved in the cycle regulation, but their functions remain largely unclear. One of these signaling systems is epidermal growth factor receptor (EGFR), which regulates the activity of neurons in the modulation of the sleep–wake cycle in vertebrates. We have evaluated the possible role of the EGFR signaling pathway in the molecular regulation of sleep. Understanding the molecular mechanisms that underlie sleep–wake regulation will provide critical insight into the fundamental regulatory functions of the brain. New findings of sleep-regulatory pathways may provide new drug targets and approaches for the treatment of sleep-related diseases.

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Section: Orexinsmentioning

confidence: 99%

Does EGFR Signaling Mediate Orexin System Activity in Sleep Initiation?

Kniazkina

Dyachuk

2023

IJMS

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“…Therefore, numerous studies have been proposing automatic sleep stage classification by using the main signals containing the characteristics of each stage including EEG, EOG, and sub-mental EMG. The techniques can be categorized into three groups: 1) human-engineered feature extraction with automatic decision making algorithms [7]- [16], 2) the application of extracted features on the Deep Learning (DL) approach [17], [18], and 3) the use of an end-to-end training of the DL approach including Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) [19]- [25], which involved state-of-the-art sleep staging networks, e.g., DeepSleepNet [26] and SeqSleepNet [27].…”

Section: Introductionmentioning

confidence: 99%

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

Banluesombatkul

Ouppaphan

Leelaarporn

et al. 2021

IEEE J. Biomed. Health Inform.

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Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://github.com/IoBT-VISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a nonconventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.

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Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach

Satapathy

Loganathan

2022

Multimed Tools Appl

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Sleep Staging Using Plausibility Score: A Novel Feature Selection Method Based on Metric Learning

Cited by 9 publications

References 44 publications

Does EGFR Signaling Mediate Orexin System Activity in Sleep Initiation?

Does EGFR Signaling Mediate Orexin System Activity in Sleep Initiation?

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach

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