The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in sleep medicine. However, manual detection limits their study because it is time-consuming and affected by significant inter-expert variability, motivating automatic approaches. We propose a deep learning approach based on convolutional and recurrent neural networks 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). Unlike previous approaches, a fixed time window is avoided and temporal context is integrated to better emulate the visual criteria of experts. 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. Although the CWT-domain model obtained a similar performance than its time-domain counterpart, the former allows in principle a more interpretable input representation due to the use of a spectrogram. The proposed approach is event-agnostic and can be used directly to detect other types of sleep events.
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time consuming and tedious process with a large inter-scorer variability. Automatic algorithms have been developed to detect various types of events but these are event-specific. We propose a deep learning method that jointly predicts locations, durations and types of events in EEG time…
University of Moratuwa（莫拉图瓦大学）；Harvard University（哈佛大学）；Aarhus University（奥胡斯大学）；Florida International University（佛罗里达国际大学）
Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation …
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical micro-architecture events which exhibit variable durations and shapes, such as spindles, K-complexes or arousals. Annotating such events is traditionally performed by a trained sleep expert, making the process time consuming, tedious and subject to inter-scorer variability. To autom…
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 de…
University of California Los Angeles（加州大学洛杉矶分校）；Philips Research（飞利浦研究院）
Sleep is particularly important to the health of infants, children, and adolescents, and sleep scoring is the first step to accurate diagnosis and treatment of potentially life-threatening conditions. But pediatric sleep is severely under-researched compared to adult sleep in the context of machine learning for health, and sleep scoring algorithms developed for adults usually perform poorly on infants. Here, we present the first automated sleep scoring results on a recent large-scale pediatric sleep study dataset that was collected during standard clinical care. We develop a transformer-based …
MIT Department of BCS（麻省理工学院脑与认知科学系）；MIT Department of EECS（麻省理工学院电子工程与计算机科学系）；Harvard-MIT HST（哈佛-麻省理工健康科学与技术项目）；SEAS Department of Harvard（哈佛大学工程与应用科学学院）；MGH Department of Anesthesia, Critical Care and Pain Medicine（麻省总医院麻醉、重症监护与疼痛医学科）
Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research. The standard of care is to manually classify 30-second epochs of EEG time-domain traces into 5 discrete sleep stages. Unfortunately, this scoring process is subjective and time-consuming, and the defined stages do not capture the heterogeneous landscape of healthy and clinical neural dynamics. This motivates the search for a data-driven and principled way to identify the number and composition of salient, reoccurring brain states present during sleep. To this end, we propose …
Previous researches revealed the chaotic and nonlinear nature of EEG signal. In this paper we inspected the variability of chaotic indices of the sleep EEG signal such as largest Lyapunov exponent, mutual information, correlation dimension and minimum embedding dimension among different subjects, conditions and sleep stages. Empirical histograms of these indices are obtained from sleep EEG of 31 subjects, showing that, with a good accuracy, these indices in each stage of sleep vary from healthy human subjects to subjects suspected to have sleep-disordered breathing.
Korea Atomic Energy Research Institute（韩国原子能研究院）；Gwangju Institute of Science and Technology (GIST)（光州科学技术院）
A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep stage from half-minute EEG, called an epoch, sleep experts investigate sleep-related events and consider the transition rules between the found events. Similarly, IITNet extracts representative features at a sub-epoch level by a residual neural network and captures intra- and inter-epoch temporal contexts from the sequence of the features via bidirectional LSTM. The performance was investigated for three datasets as th…
Sleep staging based on electroencephalogram (EEG) plays an important role in the clinical diagnosis and treatment of sleep disorders. In order to emancipate human experts from heavy labeling work, deep neural networks have been employed to formulate automated sleep staging systems recently. However, EEG signals lose considerable detailed information in network propagation, which affects the representation of deep features. To address this problem, we propose a new framework, called MRNet, for data-driven sleep staging by integrating a multi-scale feature fusion model and a Markov-based sequent…
An electroencephalogram (EEG) signal is currently accepted as a standard for automatic sleep staging. Lately, Near-human accuracy in automated sleep staging has been achievable by Deep Learning (DL) based approaches, enabling multi-fold progress in this area. However, An extensive and expensive clinical setup is required for EEG based sleep staging. Additionally, the EEG setup being obtrusive in nature and requiring an expert for setup adds to the inconvenience of the subject under study, making it adverse in the point of care setting. An unobtrusive and more suitable alternative to EEG is Ele…
We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG. We employ real-time adaptation of a single channel Electroencephalography (EEG) to infer from a Time-Distributed 1-D Deep Convolutional Neural Network. Polysomnography (PSG)-the gold standard for sleep staging, requires a human scorer and is both complex and resource-intensive. Our work demonstrates an end-to-end on-smartphone pipeline that can infer sleep stages in just single 30-second epochs, with an overall accuracy of 83.5% on 20-fold cro…
Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical envi…
Northern Arizona University（北亚利桑那大学）；Ngee Ann Polytechnic（义安理工学院）；Singapore University of Social Sciences（新加坡社会科学大学）；University of Malaya（马来亚大学）
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs an…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30s segments as output. Experiments are carried out for two different scoring standards (5 and 6 stage classification) on the expanded PhysioNet Sleep-EDF dataset, which contains multi-source data from hospital and household polysomnography setups. The…
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this study, we propose an adaptive scheme to probabilistically encode, filter and accumulate the input signals and weight the resultant features by the half-Gaussian probabilities of signal intensities. The adaptive representations are subsequently fed into a transformer model to automatically mine the relevance between features and corresponding stages. Extensive experiment…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Relativity（Relativity公司）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Background: Deep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated Electroencephalogram (EEG) data. However, effectively utilizing a large amount of raw EEG remains a challenge. Objective: In this paper, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples. Methods: …
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
Institute of Biomedical Engineering, Ulm University（生物医学工程研究所，乌尔姆大学）；Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich（移动健康系统实验室，健康科学与技术系，苏黎世联邦理工学院）
FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification
Department of Biomedical Informatics, Emory University（埃默里大学生物医学信息学系）；Ritchie Centre, Hudson Institute of Medical Research（哈德逊医学研究所里奇中心）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Department of Obstetrics and Gynaecology, Monash University（莫纳什大学妇产科学系）；Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）
We propose a novel algorithm for sleep dynamics visualization and automatic annotation by applying diffusion geometry based sensor fusion algorithm to fuse spectral information from two electroencephalograms (EEG). The diffusion geometry approach helps organize the nonlinear dynamical structure hidden in the EEG signal. The visualization is achieved by the nonlinear dimension reduction capability of the chosen diffusion geometry algorithms. For the automatic annotation purpose, the {support vector machine} is trained to predict the sleep stage. The prediction performance is validated on a publ…
Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction
University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；University College Cork（科克大学）；University at Albany, SUNY（纽约州立大学奥尔巴尼分校）；Fudan University（复旦大学）；University of Sydney（悉尼大学）
Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we propose a pre-training technique that handles this challenge in sleep staging tasks. Inspired by conventional methods that experienced physicians have used to classify sleep states from the existence of characteristic waveform shapes, or template patterns, our method introduces a c…
BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Healthcare Technology and Innovation Center (HTIC), Indian Institute of Technology (IIT-M), India（医疗技术与创新中心（HTIC），印度理工学院马德拉斯分校（IIT-M））；Department of Electrical Engineering, Indian Institute of Technology, Madras (IITM), India（印度理工学院马德拉斯分校（IITM）电气工程系）
Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress in this area, allowing for near-human accuracy in automated sleep staging. However, EEG based sleep staging requires an extensive as well as an expensive clinical setup. Moreover, the requirement of an expert for setup and the added inconvenience to the subject under study renders it unfavourable in a point of care context. Electrocardiogram (ECG), an unobtrusive alternative to EEG, is more suitable, but its perform…
As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way.In particular, 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. The sleep models were built using sleep-sequence data and employed either statistical $n$-gram or deep neural network-ba…
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Korea University（高丽大学）；University of Cambridge（剑桥大学）；Stanford University School of Medicine（斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Korea University College of Medicine（高丽大学医学院）
North China University of Technology（北方工业大学）；Kailuan General Hospital（开滦总医院）；Sun Yat-sen University（中山大学）
Sleep staging plays an important role on the diagnosis of sleep disorders. In general, experts classify sleep stages manually based on polysomnography (PSG), which is quite time-consuming. Meanwhile, the acquisition process of multiple signals is much complex, which can affect the subject's sleep. Therefore, the use of single-channel electroencephalogram (EEG) for automatic sleep staging has become a popular research topic. In the literature, a large number of sleep staging methods based on single-channel EEG have been proposed with promising results and achieve the preliminary automation of s…
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
Amazon Alexa（亚马逊Alexa）；Aarhus University（奥胡斯大学）；KU Leuven（荷语鲁汶大学）；Lausanne University Hospital (CHUV)（洛桑大学医院）；University of Lausanne（洛桑大学）；University of Oxford（牛津大学）；University of Lübeck（吕贝克大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；The University of Adelaide（阿德莱德大学）；Queen Mary University of London（伦敦玛丽女王大学）；Alan Turing Institute（艾伦·图灵研究所）
Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components. Inputs were presented to a Scattering Transform (ST) representation layer which fed a recurrent neural network for sequence learning using three layers of Long Short-Term Memory (LSTM). The STs were calculated for each signal with downsampling parameters chosen to give approxima…
We propose a generative model for single-channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods-while the hidden variables control the durations, state transitions, and robustness, the observation architectures parameterize Normal-Gamma distributions. The resulting model allows for time series segmentation into local, reoccurring dynamical regimes by exploiting probabilistic models and deep learning. Unlike typical detectors, …
Processing and analyzing of massive clinical data are resource intensive and time consuming with traditional analytic tools. Electroencephalogram (EEG) is one of the major technologies in detecting and diagnosing various brain disorders, and produces huge volume big data to process. In this study, we propose a big data framework to diagnose sleep disorders by classifying the sleep stages from EEG signals. The framework is developed with open source SparkMlib Libraries. We also tested and evaluated the proposed framework by measuring the scalabilities of well-known classification algorithms on …
Osaka University（大阪大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Fudan University（复旦大学）；Waseda University（早稻田大学）
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated…
In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classif…
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The Fpz-Cz EEG signals from a publicly available Sleep-EDF dataset are used to train and test the model. Four convolutional filter layers were used to extract features and reduce the data dimension. Then, transformers were utilized to learn the time-variant features of the data. To …
University of Kent（肯特大学）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet. At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention…
The regulation of the autonomic nervous system changes with the sleep stages causing variations in the physiological variables. We exploit these changes with the aim of classifying the sleep stages in awake or asleep using pulse oximeter signals. We applied a recurrent neural network to heart rate and peripheral oxygen saturation signals to classify the sleep stage every 30 seconds. The network architecture consists of two stacked layers of bidirectional gated recurrent units (GRUs) and a softmax layer to classify the output. In this paper, we used 5000 patients from the Sleep Heart Health Stu…
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；National Institute of Mental Health and Neuro Sciences（国家精神卫生与神经科学研究所）
Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to lab…
Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD). However, existing algorithms to estimate the up-phase suffer from a poor phase accuracy at low EEG amplitudes and when SW frequencies are not constant. We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices. The…
INFANT Research Centre（INFANT研究中心）；University College Cork（科克大学）
Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Trace alternant (TA)-a characteristic pattern of EEG activity during quiet sleep in term neonates-is defined by alternating periods of short-duration, high-voltage activity (bursts) separated by lower-voltage activity (inter-bursts). This study presents a novel approach for detecting TA activity by first detecting the inter-bursts and then processing the temporal map of the bursts and inter-bursts. EEG recordings from 72 healthy term neonates were used to develop and evalu…
