A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence.

Sleep monitoring typically requires the uncomfortable and expensive polysomnography (PSG) test to determine the sleep stages. Body movement and cardiopulmonary signals provide an alternative way to perform sleep staging. In recent years, long-short term memory (LSTM) networks and convolutional neural networks (CNN) have dominated automatic sleep staging due to their better learning ability than machine learning classifiers. However, LSTM may lose information when dealing with long sequences, while CNN is not good at sequence modeling. As an improvement, we develop a hierarchical attention-based deep learning method for sleep staging using body movement, electrocardiogram (ECG), and abdominal breathing signals. We apply the multi-head self-attention to model the global context of feature sequences and coupled it with CNN to achieve a hierarchical self-attention weight assignment. We evaluate the performance of the method using two public datasets. Our method outperforms other baselines in the three sleep stages, achieving an accuracy of 84.3%, an F1 score of 0.8038, and a Cohen's Kappa coefficient of 0.7036. The result demonstrates the effectiveness of the hierarchical self-attention mechanism when processing feature sequences in the sleep stage classification problem. This paper provides new possibilities for long-term sleep monitoring using movement and cardiopulmonary signals obtained from non-invasive devices. The code can be found at: https://github.com/scutrd/attention-sleep-staging.

Cardiovascular Disease and Sleep Disordered Breathing: Are Children Vulnerable?

What is the central question of this study? A transcranial magnetic stimulation (TMS)-induced twitch applied on isolated single breaths over the motor cortex somatotopic representation of the tongue briefly recruits submental muscles and improves airflow dynamics of flow-limited respiratory cycles without arousing sleep apnoea patients. However, the mechanical impact of the TMS-induced twitch applied during consecutive breathing cycles on airflow dynamics remains unknown. What is the main finding and what is its importance? Our results show that application of TMS with the stimulator output set at the sleep submental motor threshold intensity on consecutive respiratory cycles increases inspiratory flow and reduces the turbulent airflow component. These results indicate an improvement of airflow pattern after two single consecutive TMS-induced twitches without arousing sleep apnoea patients. Transcranial magnetic stimulation (TMS)-induced twitches applied on isolated breaths briefly recruit upper airway dilator muscles and improve airflow and inspiratory volume without arousing apnoeic patients from sleep, but the effects of applying such twitches consecutively on airflow dynamics is unknown. The objective of this study was to quantify the effects of five consecutive TMS-induced twitches applied on sleep-induced obstructive hypopnoeic breaths in 10 obstructive sleep apnoea patients. Submental muscle motor threshold (SUBMT) and motor-evoked potential were measured during wakefulness and sleep. The TMS-induced twitches were applied during stable non-rapid eye movement (NREM) sleep, at the beginning of inspiration of consecutive flow-limited respiratory cycles, with the stimulator output set at sleep SUBMT. Maximal inspiratory flow, inspiratory volume, inspiratory time, shifts of electroencephalogram frequency and pulse rate variability were assessed. During sleep, SUBMT increased (wakefulness, 25.3 ± 4.9%; NREM sleep, 27.0 ± 6.2%; P = 0.02). During each series of stimulations there was a rise in maximal inspiratory flow (from 306.7 ± 123.2 to 359.8 ± 154.1 ml s(-1); P = 0.0002) and in inspiratory volume (from 346.1 ± 128.1 to 414.9 ± 171.2 ml; P = 0.02) without differences in thoraco-abdominal efforts and inspiratory time. These responses were observed in the absence of arousals and ceased immediately after TMS interruption. Transcranial magnetic stimulation-induced cortical and/or autonomic arousal was observed in 30.2% of all series of stimulation. Consecutive twitch TMS of submental muscles may lead to arousals in a minority of patients but can be applied on consecutive respiratory cycles during sleep and can significantly improve maximal inspiratory flow and inspiratory volume of flow-limited cycles.

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

The mental and physical health of an individual related to sleep, and thus sleep staging is essential to identify sleep-related diseases and disorders. Traditional methods require expert manual scoring, which takes time. Although various automated systems have been developed over decades, they are unreliable and inaccurate to an unacceptable degree. Therefore, this study aims to explore the critical analysis of recent literature in the field of automated sleep stage scoring and provide a solution that is accurate, reliable and efficient for automatically classifying Electroencephalogram (EEG) signals across various sleep stages. In particular, we propose a system based on Long Short-Term Memory (LSTM) and define a taxonomy of four components that are Data, feature Extraction, Classification Processing, and View (DECV). These components serve as validation criteria and are required for the implementation of an automated sleep stage scoring system. The LSTM helps to capture long-term dependencies, which is a crucial component for the accurate classification of sleep stages and helps sleep experts and Polysomnography (PSG). The proposed model provides superior results in terms of coverage as it offers presence and potential as well as sleep disorder detection. In addition, the proposed model can also process multi-dimensional data, which is not the case with the state-of-the-art model.

Sleep stage classification is closely related to human health and plays an effective role in sleep disorders diagnose. At present, the accuracy rate of conventional automatic sleep staging system is still not high, and there is still a large space for improvement. To solve the above problems, this paper proposed an automatic sleep stage classification method based on the Long Short-Term Memory (LSTM) and fuzzy entropy. We firstly adopt fuzzy entropy to extract the feature from electroencephalography (EEG) signals, then these features are put into the designed LSTM to carry out the automatic sleep stage classification and attain the final sleep stages. To verify the validity of our experiment, we tested it on public datasets called ISRUC_Sleep. The results demonstrate that this method improves the classification accuracy of sleep stages.

The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages by monitoring the signals, which is time consuming and frustrating task and can be affected by human error. New studies propose fully automated techniques for classifying sleep stages that makes sleep scoring possible at home. Despite comprehensive studies have been presented in this field the classification results have not yet reached the gold standard due to the concentration on the use of a limited source of information such as single channel EEG. Therefore, this article introduces a new method for fusing two sources of information, including electroencephalogram (EEG) and electrooculogram (EOG), to achieve promising results in the classification of sleep stages. In the proposed method, extracted features from the EEG and EOG signals, are divided into two feature sets consisting of the EEG features and fused features of EEG and EOG. Then, each feature set transformed into a horizontal visibility graph (HVG). The images of the HVG are produced in a novel framework and classified by proposed transfer learning convolutional neural network for data fusion (TLCNN-DF). Employing transfer learning at the training stage of the model has accelerated the training process of the CNN and improved the performance of the model. The proposed algorithm is used to classify the Sleep-EDF and Sleep-EDFx benchmark datasets. The algorithm can classify the Sleep-EDF dataset with an accuracy of 93.58% and Cohen’s kappa coefficient of 0.899. The results show proposed method can achieve superior performance compared to state-of-the-art studies on classification of sleep stages. Furthermore, it can attain reliable results as an alternative to conventional sleep staging.

Sleep restriction and emotion, electroencephalography (EEG) and dream recall, and insomnia and punctuality

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

Sleep staging is very important for the effective diagnosis and pre-intervention of patients with sleep disorders. The traditional sleep staging method based on manual scoring is highly subjective, time-consuming and the collection methods are complicated and cumbersome. From the perspective of optimizing the selection of EEG channels in automatic sleep staging, we collected PSG signals from 26 subjects in the sleep laboratory, extracted the frequency domain features based on power spectral density and the time-frequency features of wavelet transform of 12 channel sleep EEG signals located in the forehead-central area. The separability and validity of the extracted features were explored by the One Way Analysis of Variance (ANOVA). The light Gradient Boosting Machine (LGB) and leave-one-out cross-validation method is used to classify the four stages of sleep: wakefulness (W), light sleep (LS), deep sleep (DS), and rapid eye movement (REM). Through channel screening in different EEG reference montages, an EEG channel selection that can be applied to wearable sleep monitoring devices is proposed. Moreover, in order to verify the generalization ability of the staging model. Sleep-EDFx, a public data set provided by PhysioNet, is used to evaluate and compare the performance of the proposed model. The experimental results show that the T4-AVG channel with ear electrodes as a reference achieves the highest classification accuracy of 92.25% and 0.857 kappa coefficient, which provides better wearability and portability than electrodes placed in the parietal-occipital region. All datasets show relatively stable classification performance, which reflects the portability and robustness of the model.

Sleep stage classification can be used to monitor sleep quality and diagnose sleep disorders. Sleep disorders can be correlated to health conditions such as Alzheimer’s and Parkinson’s disease. This project uses a hydraulic bed sensor positioned under the mattress, as well as a deep learning approach, for sleep stage classification. Our motivation is to provide an automatic, non-invasive and more accessible method of classifying sleep stages by using deep learning to analyze data gathered from the hydraulic bed sensor. The test subjects for this project were elderly patients with sleep disorders. Polysomnography (PSG) data, the current gold standard, was also collected in a Sleep Lab to serve as the ground truth for the bed sensor data. In this study, sleep stages are categorized into 3 categories: Wake, Rapid Eye Movement (REM), and Non-Rapid Eye Movement (NREM). This paper uses a Convolutional Neural Network (CNN)-Long-Short Term Memory (LSTM) hybrid model with 2 CNNs of different filter sizes for feature extraction. These features are then fed into the LSTM for classification. Our results show an average accuracy of about 76% using the leave-one-subject-out (LOSO) validation. These results are promising and show that the hydraulic bed sensor combined with a deep learning approach is capable of providing an automatic and non-invasive method of classifying sleep stages.

Don’t Let Your Patients Lose Sleep Over Insomnia

<h2>Abstract</h2> Sleep is vital for our physical and mental health, and sound sleep can help us to focus on daily activities. Therefore, a sleep study that includes sleep patterns and disorders is crucial to enhancing our knowledge about individual health status. The findings on sleep stages and sleep disorders relied on Polysomnography and self-report measures, and then the study went through clinical assessments by expert physicians. Artificial Intelligence has made the evaluation process of sleep stages and sleep disorders classification more efficient. Many studies have focused on analyzing various datasets using advanced techniques and algorithms to improve computational ease and accuracy. This review aims to comprehensively analyze recent literature on different approaches and outcomes in sleep studies, specifically on ‘sleep stages classification' and ‘sleep disorder detection' using Artificial Intelligence. Initially, 185 articles were selected from top journals, and eventually, 81 of them were reviewed in detail, covering the period from 2016 to 2023. Brain waves are the most commonly used body signals for studying sleep patterns and disorders. Almost 36 % of the research exclusively used brain activity signals, and 80 % combined them with other body parameters in sleep staging. The Neural Network algorithms are the most popular, having 47 % of the total usage. At the same time, Long Short-Term Memory, Ensemble Learning, Support Vector Machine, and Random Forest accounted for 15 %, 12 %, 7 %, and 6 % of usage, respectively. For evaluating AI model performance, accuracy or precision is used in 86.42 % of cases, followed by an F1 score of 46.91 %, Kappa of 39.51 %, Specificity of 30.86 %, Sensitivity of 29.63 %, along with other metrics. This article would help physicians and researchers get the gist of Artificial Intelligence's contribution to sleep studies and the feasibility of their intended work.

Electroencephalogram (EEG) signal is often used to assess sleep quality and treat sleep disorders. Many existing methods usually obtain high accuracy through a large number of feature preprocessing and feature extraction of EEG signals, which need a lot of prior knowledge as the basis. In this paper, a novel sleep stage classification framework, named FRL&S2SL, is proposed. The framework combines fast representation learning (FRL) and semantic-to-signal learning (S2SL) and uses single-channel EEG without any preprocessing of EEG signals. In the proposed framework, we utilize convolutional neural networks (CNN) to extract time-invariant features and bidirectional long short-term memory (BiLSTM) models to extract temporal features. Furthermore, auxiliary classifier generative adversarial network (ACGAN) is used to embed semantic features into signal features and to extract knowledge domain features of EEG signals for the first time. According to the American Academy of Sleep Medicine (AASM), sleep is divided into five stages: awake, rapid eye movement (REM) and three non-rapid eye movement (N1/N2/N3). We evaluated our framework using single-channel EEG (Fpz-Oz) from Sleep-EDF dataset, which is subject to the standards specified by AASM. The results show that our framework has achieved state-of-the-art in many evaluation metrics.

In this study, using the polysomnography data set, the classification of sleep stages was realized automatically with supervised learning method. In this study, sleep stage classification was carried out in three stages. In the first stage, the biomedical signal was divided into its independent components by Independent Component Analysis method. In the second stage, feature extraction was performed by using Mel Kepstrum Coefficient method. In the third stage, artificial neural networks were trained by using the extracted features and the sleep phases were estimated by using software architecture called Long ShortTerm Memory. As a result of the classification process performed in this way, the accuracy rate of the ten fold cross validation obtained for the binary classification (asleep / wake) was found to be 97,87%. For the five fold classification problem, the accuracy rate of the subject dependent algorithm and polysomnography data obtained using a single EEG channel was found to be 93,36% as a result of the ten-fold cross validation process.