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An Introduction To The Event Related Potential Technique

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs’ labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

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Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Sleep spindles are burstlike signals in the electroencephalogram (EEG) of the sleeping mammalian brain and electrical surface correlates of neuronal oscillations in thalamus. As one of the most inh...

https://journals.physiology.org/doi/pdf/10.1152/physrev.00042.2018

Sleep Spindles: Mechanisms and Functions.

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

https://www.mdpi.com/1099-4300/16/12/6573/pdf

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Falling asleep affects our behavior immediately and profoundly. During sleep, large electrical waves appear across the brain in areas responsible for consciousness, sensation and movement. In the cortex – the outer layer of the brain – sleep waves arise from networks that connect to the thalamus, a deeper structure within the brain. However, not all areas of the brain sleep equally. We know this intuitively because sensory stimuli, such as an alarm clock or a baby’s cry, can still wake us up. By contrast, we typically do not move much or take major decisions while we sleep. Therefore, the brain areas involved in sensation should not be expected to sleep in the same way as areas involved in movement or reasoning. Neighboring brain areas generally show very different sleep waves. The brain regions that we use during the day can also affect how sleep varies from one area to the next. It is not well understood what determines these ‘local’ sleep properties. By studying the brains of mice, Fernandez et al. now show that the networks between the cortex and thalamus are much more varied than previously thought, in particular regarding a thalamic nucleus that is relevant for sleep wave generation. These previously unrecognized differences deep within the brain are part of the origin of local sleep in the outer layer of the brain. Sleep wave activity differed depending on whether the networks were involved in sensory or non-sensory roles. The networks allow sensory areas to switch efficiently between different forms of local sleep. This might underlie how the brain’s sensory activity during the day can influence local sleep at night. There is growing evidence that major sleep disorders are due to disturbances to local sleep. Techniques to modify or restore specific sleep waves locally in the brain could help to develop new sleep therapies. For example, having a detailed map of electrical waves within the sleep-disordered brain could help researchers to apply transcranial stimulation techniques in ways that might help to treat these debilitating disorders.

https://serval.unil.ch/resource/serval:BIB_872EE93FB4BC.P001/REF.pdf

Thalamic reticular control of local sleep in mouse sensory cortex.

In spite of the uniform appearance of sleep as a behavior, the sleeping brain does not produce electrical activities in unison. Different types of brain rhythms arise during sleep and vary between layers, areas, or from one functional system to another. Local heterogeneity of such activities, here referred to as local sleep, overturns fundamental tenets of sleep as a globally regulated state. However, little is still known about the neuronal circuits involved and how they can generate their own specifically-tuned sleep patterns. NREM sleep patterns emerge in the brain from interplay of activity between thalamic and cortical networks. Within this fundamental circuitry, it now turns out that the thalamic reticular nucleus (TRN) acts as a key player in local sleep control. This is based on a marked heterogeneity of the TRN in terms of its cellular and synaptic architecture, which leads to a regional diversity of NREM sleep hallmarks, such as sleep spindles, delta waves and slow oscillations. This provides first evidence for a subcortical circuit as a determinant of cortical local sleep features. Here, we review novel cellular and functional insights supporting TRN heterogeneity and how these elements come together to account for local NREM sleep. We also discuss open questions arising from these studies, focusing on mechanisms of sleep regulation and the role of local sleep in brain plasticity and cognitive functions.

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Regulation of Local Sleep by the Thalamic Reticular Nucleus

Noradrenergic circuit control of non-REM sleep substates.

For decades, numerous seminal studies have built our understanding of the locus coeruleus (LC), the vertebrate brain’s principal noradrenergic system. Containing a numerically small but broadly efferent cell population, the LC provides brain-wide noradrenergic modulation that optimizes network function in the context of attentive and flexible interaction with the sensory environment. This review turns attention to the LC’s roles during sleep. We show that these roles go beyond down-scaled versions of the ones in wakefulness. Novel dynamic assessments of noradrenaline signaling and LC activity uncover a rich diversity of activity patterns that establish the LC as an integral portion of sleep regulation and function. The LC could be involved in beneficial functions for the sleeping brain, and even minute alterations in its functionality may prove quintessential in sleep disorders.

/pdf/when-the-locus-coeruleus-speaks-up-in-sleep-recent-insights-ukgi200t.pdf

Alejandro Osorio-Forero

Papers

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Thalamic reticular control of local sleep in mouse sensory cortex.

Regulation of Local Sleep by the Thalamic Reticular Nucleus

Noradrenergic circuit control of non-REM sleep substates.

When the Locus Coeruleus Speaks Up in Sleep: Recent Insights, Emerging Perspectives