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Moctezuma LA, Suzuki Y, Furuki J, Molinas M, Abe T. GRU-powered sleep stage classification with permutation-based EEG channel selection. Sci Rep 2024; 14:17952. [PMID: 39095608 PMCID: PMC11297028 DOI: 10.1038/s41598-024-68978-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 07/30/2024] [Indexed: 08/04/2024] Open
Abstract
We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.
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Affiliation(s)
- Luis Alfredo Moctezuma
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan.
| | - Yoko Suzuki
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Junya Furuki
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Marta Molinas
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Takashi Abe
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
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Luo X, Zhou B, Shi J, Li G, Zhu Y. Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep. Front Psychiatry 2024; 15:1433316. [PMID: 39045546 PMCID: PMC11264056 DOI: 10.3389/fpsyt.2024.1433316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. Methods This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussion The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.
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Affiliation(s)
- Xiaodong Luo
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
| | - Bin Zhou
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Jilong Shi
- College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Yixia Zhu
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
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van Gorp H, van Gilst MM, Overeem S, Dujardin S, Pijpers A, van Wetten B, Fonseca P, van Sloun RJG. Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders. Physiol Meas 2024; 45:055007. [PMID: 38653318 DOI: 10.1088/1361-6579/ad4251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/23/2024] [Indexed: 04/25/2024]
Abstract
Objective.Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.Approach.We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.Main results.For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.Significance.The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.
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Affiliation(s)
- Hans van Gorp
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
- Philips Sleep and Respiratory Care, Eindhoven, The Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
- Sleep Medicine Centre Kempenhaeghe, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
- Sleep Medicine Centre Kempenhaeghe, Heeze, The Netherlands
| | | | | | | | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
- Philips Sleep and Respiratory Care, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
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Proost R, Heremans E, Lagae L, Van Paesschen W, De Vos M, Jansen K. Automated sleep staging on reduced channels in children with epilepsy. Front Neurol 2024; 15:1390465. [PMID: 38798709 PMCID: PMC11116721 DOI: 10.3389/fneur.2024.1390465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Objectives This study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE). Methods Overnight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG. Results In 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep. Conclusion We concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.
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Affiliation(s)
- Renee Proost
- Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Elisabeth Heremans
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Lieven Lagae
- Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Wim Van Paesschen
- Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Katrien Jansen
- Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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Coon WG, Ogg M. Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis and Beyond. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576246. [PMID: 38293196 PMCID: PMC10827185 DOI: 10.1101/2024.01.18.576246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present novel opportunities for data-driven discovery efforts. Transformers are flexible neural networks that not only excel at classification tasks, but also can enable data-driven discovery through un- or self-supervised learning, which requires no human annotations to the input data. While transformers have been extensively used in supervised learning scenarios for sleep stage classification, they have not been fully explored or optimized in forms designed from the ground up for use in un- or self-supervised learning tasks in sleep. A necessary first step will be to study these models on a canonical benchmark supervised learning task (5-class sleep stage classification). Hence, to lay the groundwork for future data-driven discovery efforts, we evaluated optimizations of a transformer-based architecture that has already demonstrated substantial success in self-supervised learning in another domain (audio speech recognition), and trained it to perform the canonical 5-class sleep stage classification task, to establish foundational baselines in the sleep domain. We found that small transformer models designed from the start for (later) self-supervised learning can match other state-of-the-art automated sleep scoring techniques, while also providing the basis for future data-driven discovery efforts using large sleep data sets.
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Affiliation(s)
- William G Coon
- Research & Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, USA
| | - Mattson Ogg
- Research & Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, USA
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Yao W. Permutation time irreversibility in sleep electroencephalograms: Dependence on sleep stage and the effect of equal values. Phys Rev E 2024; 109:054104. [PMID: 38907450 DOI: 10.1103/physreve.109.054104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/05/2024] [Indexed: 06/24/2024]
Abstract
Time irreversibility (TIR) refers to the manifestation of nonequilibrium brain activity influenced by various physiological conditions; however, the influence of sleep on electroencephalogram (EEG) TIR has not been sufficiently investigated. In this paper, a comprehensive study on permutation TIR (pTIR) of EEG data under different sleep stages is conducted. Two basic ordinal patterns (i.e., the original and amplitude permutations) are distinguished to simplify sleep EEGs, and then the influences of equal values and forbidden permutation on pTIR are elucidated. To detect pTIR of brain electric signals, five groups of EEGs in the awake, stages I, II, III, and rapid eye movement (REM) stages are collected from the public Polysomnographic Database in PhysioNet. Test results suggested that the pTIR of sleep EEGs significantly decreases as the sleep stage increases (p<0.001), with the awake and REM EEGs demonstrating greater differences than others. Comparative analysis and numerical simulations support the importance of equal values. Distribution of equal states, a simple quantification of amplitude fluctuations, significantly increases with the sleep stage (p<0.001). If these equalities are ignored, incorrect probabilistic differences may arise in the forward-backward and symmetric permutations of TIR, leading to contradictory results; moreover, the ascending and descending orders for symmetric permutations also lead different outcomes in sleep EEGs. Overall, pTIR in sleep EEGs contributes to our understanding of quantitative TIR and classification of sleep EEGs.
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Affiliation(s)
- Wenpo Yao
- State Key Laboratory of Organic Electronics and Information Displays, Institute of Advanced Materials, School of Chemistry and Life Sciences, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
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An P, Zhao J, Du B, Zhao W, Zhang T, Yuan Z. Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:6492-6506. [PMID: 36215384 DOI: 10.1109/tnnls.2022.3210384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.
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8
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Baumert M, Phan H. A perspective on automated rapid eye movement sleep assessment. J Sleep Res 2024:e14223. [PMID: 38650539 DOI: 10.1111/jsr.14223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/18/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024]
Abstract
Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how they can be exploited for automated sleep assessment. We summarise key historical developments in automated sleep staging systems, having now achieved classification accuracy on par with human expert scorers and their role in the clinical setting. We also discuss rapid eye movement sleep assessment with consumer sleep trackers and its potential for unprecedented sleep assessment on a global scale. We conclude by providing a future outlook of computerised rapid eye movement sleep assessment and the role AI systems may play.
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Affiliation(s)
- Mathias Baumert
- Discipline of Biomedical Engineering, School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide, Australia
| | - Huy Phan
- Amazon, Cambridge, Massachusetts, USA
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9
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Zhai B, Elder GJ, Godfrey A. Challenges and opportunities of deep learning for wearable-based objective sleep assessment. NPJ Digit Med 2024; 7:85. [PMID: 38575794 PMCID: PMC10995158 DOI: 10.1038/s41746-024-01086-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024] Open
Affiliation(s)
- Bing Zhai
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK
| | - Greg J Elder
- Northumbria Sleep Research, Department of Psychology, Northumbria University, Newcastle upon Tyne, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
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10
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Shen Y, Huai B, Wang X, Chen M, Shen X, Han M, Su F, Xin T. Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine. CNS Neurosci Ther 2024; 30:e14708. [PMID: 38600857 PMCID: PMC11007385 DOI: 10.1111/cns.14708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 04/12/2024] Open
Abstract
AIMS Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. METHODS The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. RESULTS Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79). CONCLUSION This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.
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Affiliation(s)
- Yin Shen
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
| | - Baogeng Huai
- First Clinical Medical College, Shandong University of Traditional Chinese MedicineJinanP. R. China
| | - Xiaofeng Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
| | - Min Chen
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Department of RadiologyShandong First Medical University & Shandong Academy of Medical SciencesTaianP. R. China
| | - Xiaoyue Shen
- First Clinical Medical College, Shandong University of Traditional Chinese MedicineJinanP. R. China
| | - Min Han
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
| | - Fei Su
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Department of RadiologyShandong First Medical University & Shandong Academy of Medical SciencesTaianP. R. China
| | - Tao Xin
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
- Institute of Brain Science and Brain‐inspired Research, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongP. R. China
- Shandong Institute of Brain Science and Brain‐inspired ResearchJinanShandongP. R. China
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He Z, Chen L, Xu J, Lv H, Zhou RN, Hu J, Chen Y, Gao Y. Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data. BIOLOGY 2024; 13:203. [PMID: 38666815 PMCID: PMC11048286 DOI: 10.3390/biology13040203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/13/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects.
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Affiliation(s)
- Zhengda He
- The Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Linjie Chen
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Jiaying Xu
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Hao Lv
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Rui-ning Zhou
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Jianhua Hu
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Yang Gao
- The Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;
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Rayan A, Agarwal A, Samanta A, Severijnen E, van der Meij J, Genzel L. Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues. Eur J Neurosci 2024; 59:526-553. [PMID: 36479908 DOI: 10.1111/ejn.15884] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 11/01/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022]
Abstract
There is nothing we spend as much time on in our lives as we do sleeping, which makes it even more surprising that we currently do not know why we need to sleep. Most of the research addressing this question is performed in rodents to allow for invasive, mechanistic approaches. However, in contrast to human sleep, we currently do not have shared and agreed upon standards on sleep states in rodents. In this article, we present an overview on sleep stages in humans and rodents and a historical perspective on the development of automatic sleep scoring systems in rodents. Further, we highlight specific issues in rodent sleep that also call into question some of the standards used in human sleep research.
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Affiliation(s)
- Abdelrahman Rayan
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Anjali Agarwal
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Anumita Samanta
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Eva Severijnen
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jacqueline van der Meij
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Lisa Genzel
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
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Rayan A, Szabo AB, Genzel L. The pros and cons of using automated sleep scoring in sleep research. Sleep 2024; 47:zsad275. [PMID: 37889222 PMCID: PMC10782493 DOI: 10.1093/sleep/zsad275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Indexed: 10/28/2023] Open
Abstract
Sleep scoring plays a pivotal role both in sleep research and in clinical practice. Traditionally, this process has relied on manual scoring by human experts, but it is marred by time constraints, and inconsistencies between different scorers. Consequently, the quest for more efficient and reliable approaches has sparked a great interest in the realm of automatic sleep-scoring methods. In this article, we provide an exploration of the merits and drawbacks of automatic sleep scoring, alongside the pressing challenges and critical considerations that demand attention in this evolving field.
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Affiliation(s)
- Abdelrahman Rayan
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Anna B Szabo
- Research Center on Animal Cognition (CRCA) and Brain and Cognition Research, Toulouse University, Toulouse, France
| | - Lisa Genzel
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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14
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Jeong J, Yoon W, Lee JG, Kim D, Woo Y, Kim DK, Shin HW. Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification. Sleep 2023; 46:zsad242. [PMID: 37703391 DOI: 10.1093/sleep/zsad242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 08/11/2023] [Indexed: 09/15/2023] Open
Abstract
STUDY OBJECTIVES Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments. METHODS All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model, and validated in an external dataset. RESULTS We constructed 10253 image-based PSG datasets using a standardized format. Among these, 7745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance. CONCLUSIONS Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases.
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Affiliation(s)
- Jaemin Jeong
- Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea
| | | | - Jeong-Gun Lee
- Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Dongyoung Kim
- Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Yunhee Woo
- Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Dong-Kyu Kim
- OUaR LaB, Inc, Seoul, Republic of Korea
- Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea¸
| | - Hyun-Woo Shin
- OUaR LaB, Inc, Seoul, Republic of Korea
- Obstructive Upper Airway Research (OUaR) Laboratory, Department of Pharmacology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
- Sensory Organ Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
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15
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Grassi M, Daccò S, Caldirola D, Perna G, Schruers K, Defillo A. Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring. Front Artif Intell 2023; 6:1278593. [PMID: 38145233 PMCID: PMC10739507 DOI: 10.3389/frai.2023.1278593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/14/2023] [Indexed: 12/26/2023] Open
Abstract
Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.
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Affiliation(s)
- Massimiliano Grassi
- Medibio Limited, Savage, MN, United States
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
| | - Silvia Daccò
- Medibio Limited, Savage, MN, United States
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
| | - Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
| | - Giampaolo Perna
- Medibio Limited, Savage, MN, United States
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine, and Life Sciences, Research Institute of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Koen Schruers
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine, and Life Sciences, Research Institute of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
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16
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Anido-Alonso A, Alvarez-Estevez D. Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging. IEEE J Biomed Health Inform 2023; 27:5610-5621. [PMID: 37651482 DOI: 10.1109/jbhi.2023.3310869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, while promising results have reported in past literature, uptake of automatic sleep scoring in the clinical setting remains low. One of the current issues regards the difficulty to generalization performance results beyond the local testing scenario, i.e. across data from different clinics. Issues derived from data-privacy restrictions, that generally apply in the medical domain, pose additional difficulties in the successful development of these methods. We propose the use of several decentralized deep-learning approaches, namely ensemble models and federated learning, for robust inter-database performance generalization and data-privacy preservation in automatic sleep staging scenario. Specifically, we explore four ensemble combination strategies (max-voting, output averaging, size-proportional weighting, and Nelder-Mead) and present a new federated learning algorithm, so-called sub-sampled federated stochastic gradient descent (ssFedSGD). To evaluate generalization capabilities of such approaches, experimental procedures are carried out using a leaving-one-database-out direct-transfer scenario on six independent and heterogeneous public sleep staging databases. The resulting performance is compared with respect to two baseline approaches involving single-database and centralized multiple-database derived models. Our results show that proposed decentralized learning methods outperform baseline local approaches, and provide similar generalization results to centralized database-combined approaches. We conclude that these methods are more preferable choices, as they come with additional advantages concerning improved scalability, flexible design, and data-privacy preservation.
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17
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van Gorp H, van Gilst MM, Fonseca P, Overeem S, van Sloun RJG. Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics Using Deep Generative Learning. IEEE J Biomed Health Inform 2023; 27:5599-5609. [PMID: 37561616 DOI: 10.1109/jbhi.2023.3304010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Sleep staging is the process by which an overnight polysomnographic measurement is segmented into epochs of 30 seconds, each of which is annotated as belonging to one of five discrete sleep stages. The resulting scoring is graphically depicted as a hypnogram, and several overnight sleep statistics are derived, such as total sleep time and sleep onset latency. Gold standard sleep staging as performed by human technicians is time-consuming, costly, and comes with imperfect inter-scorer agreement, which also results in inter-scorer disagreement about the overnight statistics. Deep learning algorithms have shown promise in automating sleep scoring, but struggle to model inter-scorer disagreement in sleep statistics. To that end, we introduce a novel technique using conditional generative models based on Normalizing Flows that permits the modeling of the inter-rater disagreement of overnight sleep statistics, termed U-Flow. We compare U-Flow to other automatic scoring methods on a hold-out test set of 70 subjects, each scored by six independent scorers. The proposed method achieves similar sleep staging performance in terms of accuracy and Cohen's kappa on the majority-voted hypnograms. At the same time, U-Flow outperforms the other methods in terms of modeling the inter-rater disagreement of overnight sleep statistics. The consequences of inter-rater disagreement about overnight sleep statistics may be great, and the disagreement potentially carries diagnostic and scientifically relevant information about sleep structure. U-Flow is able to model this disagreement efficiently and can support further investigations into the impact inter-rater disagreement has on sleep medicine and basic sleep research.
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18
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Huang X, Schmelter F, Irshad MT, Piet A, Nisar MA, Sina C, Grzegorzek M. Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning. Comput Biol Med 2023; 166:107501. [PMID: 37742416 DOI: 10.1016/j.compbiomed.2023.107501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/15/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31-92.34%, MF1 of 88.21-90.08%, and Cohen's Kappa coefficient k of 0.87-0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.
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Affiliation(s)
- Xinyu Huang
- Institute of Medical Informatics, University of Lübeck, Germany.
| | - Franziska Schmelter
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany.
| | | | - Artur Piet
- Institute of Medical Informatics, University of Lübeck, Germany.
| | | | - Christian Sina
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany; Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Germany; Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
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19
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Vaquerizo-Villar F, Gutiérrez-Tobal GC, Calvo E, Álvarez D, Kheirandish-Gozal L, Del Campo F, Gozal D, Hornero R. An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea. Comput Biol Med 2023; 165:107419. [PMID: 37703716 DOI: 10.1016/j.compbiomed.2023.107419] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/26/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests.
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Affiliation(s)
- Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Eva Calvo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Departments of Neurology and Child Health and Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Félix Del Campo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Office of The Dean, Joan C. Edwards School of Medicine, Marshall University, 1600 Medical Center Dr, Huntington, WV, 25701, USA
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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20
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Phan H, Lorenzen KP, Heremans E, Chen OY, Tran MC, Koch P, Mertins A, Baumert M, Mikkelsen KB, De Vos M. L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging. IEEE J Biomed Health Inform 2023; 27:4748-4757. [PMID: 37552591 DOI: 10.1109/jbhi.2023.3303197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.
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21
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Li W, Gao J. Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals. PeerJ Comput Sci 2023; 9:e1561. [PMID: 37810362 PMCID: PMC10557479 DOI: 10.7717/peerj-cs.1561] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/10/2023] [Indexed: 10/10/2023]
Abstract
Sleep staging is crucial for assessing sleep quality and diagnosing sleep disorders. Recent advances in deep learning methods with electroencephalogram (EEG) signals have shown remarkable success in automatic sleep staging. However, the use of deeper neural networks may lead to the issues of gradient disappearance and explosion, while the non-stationary nature and low signal-to-noise ratio of EEG signals can negatively impact feature representation. To overcome these challenges, we proposed a novel lightweight sequence-to-sequence deep learning model, 1D-ResNet-SE-LSTM, to classify sleep stages into five classes using single-channel raw EEG signals. Our proposed model consists of two main components: a one-dimensional residual convolutional neural network with a squeeze-and-excitation module to extract and reweight features from EEG signals, and a long short-term memory network to capture the transition rules among sleep stages. In addition, we applied the weighted cross-entropy loss function to alleviate the class imbalance problem. We evaluated the performance of our model on two publicly available datasets; Sleep-EDF Expanded consists of 153 overnight PSG recordings collected from 78 healthy subjects and ISRUC-Sleep includes 100 PSG recordings collected from 100 subjects diagnosed with various sleep disorders, and obtained an overall accuracy rate of 86.39% and 81.97%, respectively, along with corresponding macro average F1-scores of 81.95% and 79.94%. Our model outperforms existing sleep staging models in terms of overall performance metrics and per-class F1-scores for several sleep stages, particularly for the N1 stage, where it achieves F1-scores of 59.00% and 55.53%. The kappa coefficient is 0.812 and 0.766 for the Sleep-EDF Expanded and ISRUC-Sleep datasets, respectively, indicating strong agreement with certified sleep experts. We also investigated the effect of different weight coefficient combinations and sequence lengths of EEG epochs used as input to the model on its performance. Furthermore, the ablation study was conducted to evaluate the contribution of each component to the model's performance. The results demonstrate the effectiveness and robustness of the proposed model in classifying sleep stages, and highlights its potential to reduce human clinicians' workload, making sleep assessment and diagnosis more effective. However, the proposed model is subject to several limitations. Firstly, the model is a sequence-to-sequence network, which requires input sequences of EEG epochs. Secondly, the weight coefficients in the loss function could be further optimized to balance the classification performance of each sleep stage. Finally, apart from the channel attention mechanism, incorporating more advanced attention mechanisms could enhance the model's effectiveness.
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Affiliation(s)
- Weiming Li
- Shanghai Nuanhe Brain Technology Co. Ltd., Shanghai, China
| | - Junhui Gao
- Shanghai Nuanhe Brain Technology Co. Ltd., Shanghai, China
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22
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Muto V, Berthomier C. Looking for a balance between visual and automatic sleep scoring. NPJ Digit Med 2023; 6:165. [PMID: 37670135 PMCID: PMC10480143 DOI: 10.1038/s41746-023-00915-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/22/2023] [Indexed: 09/07/2023] Open
Affiliation(s)
- Vincenzo Muto
- GIGA CRC In Vivo Imaging, Université de Liège, Liège, Belgium.
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23
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Dai Y, Li X, Liang S, Wang L, Duan Q, Yang H, Zhang C, Chen X, Li L, Li X, Liao X. MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG. IEEE J Biomed Health Inform 2023; 27:4204-4215. [PMID: 37289607 DOI: 10.1109/jbhi.2023.3284160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Automatic sleep stage classification plays an essential role in sleep quality measurement and sleep disorder diagnosis. Although many approaches have been developed, most use only single-channel electroencephalogram signals for classification. Polysomnography (PSG) provides multiple channels of signal recording, enabling the use of the appropriate method to extract and integrate the information from different channels to achieve higher sleep staging performance. We present a transformer encoder-based model, MultiChannelSleepNet, for automatic sleep stage classification with multichannel PSG data, whose architecture is implemented based on the transformer encoder for single-channel feature extraction and multichannel feature fusion. In a single-channel feature extraction block, transformer encoders extract features from time-frequency images of each channel independently. Based on our integration strategy, the feature maps extracted from each channel are fused in the multichannel feature fusion block. Another set of transformer encoders further capture joint features, and a residual connection preserves the original information from each channel in this block. Experimental results on three publicly available datasets demonstrate that our method achieves higher classification performance than state-of-the-art techniques. MultiChannelSleepNet is an efficient method to extract and integrate the information from multichannel PSG data, which facilitates precision sleep staging in clinical applications.
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Abstract
Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this technology at scale in the routine workflow of sleep centers. The field, however, is complex and presents unsolved challenges in a number of areas. Recent developments in computer science and artificial intelligence are nevertheless closing the gap. Technological advances are also opening new pathways for expanding our current understanding of the domain and its analysis.
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Affiliation(s)
- Diego Alvarez-Estevez
- Center for Information and Communications Technology Research (CITIC), Universidade da Coruña, 15071 A Coruña, Spain.
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25
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Gaiduk M, Serrano Alarcón Á, Seepold R, Martínez Madrid N. Current status and prospects of automatic sleep stages scoring: Review. Biomed Eng Lett 2023; 13:247-272. [PMID: 37519865 PMCID: PMC10382458 DOI: 10.1007/s13534-023-00299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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Affiliation(s)
- Maksym Gaiduk
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
| | | | - Ralf Seepold
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
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26
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Jiang X, Ren Y, Wu H, Li Y, Liu F. Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection. Front Neurosci 2023; 17:1222715. [PMID: 37547138 PMCID: PMC10400763 DOI: 10.3389/fnins.2023.1222715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome. Methods In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments. Results and Discussion The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.
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Affiliation(s)
- Xinge Jiang
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
| | - YongLian Ren
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Hua Wu
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
| | - Yanxiu Li
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
| | - Feifei Liu
- School of Science, Shandong Jianzhu University, Jinan, China
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Boiko A, Martínez Madrid N, Seepold R. Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115038. [PMID: 37299762 DOI: 10.3390/s23115038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
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Affiliation(s)
- Andrei Boiko
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
| | - Natividad Martínez Madrid
- Internet of Things Laboratory, School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany
| | - Ralf Seepold
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
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Masuda K, Katsuda Y, Niwa Y, Sakurai T, Hirano A. Analysis of circadian rhythm components in EEG/EMG data of aged mice. Front Neurosci 2023; 17:1173537. [PMID: 37250413 PMCID: PMC10213445 DOI: 10.3389/fnins.2023.1173537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
Aging disrupts circadian clocks, as evidenced by a reduction in the amplitude of circadian rhythms. Because the circadian clock strongly influences sleep-wake behavior in mammals, age-related alterations in sleep-wake patterns may be attributable, at least partly, to functional changes in the circadian clock. However, the effect of aging on the circadian characteristics of sleep architecture has not been well assessed, as circadian behaviors are usually evaluated through long-term behavioral recording with wheel-running or infrared sensors. In this study, we examined age-related changes in circadian sleep-wake behavior using circadian components extracted from electroencephalography (EEG) and electromyography (EMG) data. EEG and EMG were recorded from 12 to 17-week-old and 78 to 83-week-old mice for 3 days under light/dark and constant dark conditions. We analyzed time-dependent changes in the duration of sleep. Rapid eye movement (REM) and non-REM (NREM) sleep significantly increased during the night phase in old mice, whereas no significant change was observed during the light phase. The circadian components were then extracted from the EEG data for each sleep-wake stage, revealing that the circadian rhythm in the power of delta waves during NREM sleep was attenuated and delayed in old mice. Furthermore, we used machine learning to evaluate the phase of the circadian rhythm, with EEG data serving as the input and the phase of the sleep-wake rhythm (environmental time) as the output. The results indicated that the output time for the old mice data tended to be delayed, specifically at night. These results indicate that the aging process significantly impacts the circadian rhythm in the EEG power spectrum despite the circadian rhythm in the amounts of sleep and wake attenuated but still remaining in old mice. Moreover, EEG/EMG analysis is useful not only for evaluating sleep-wake stages but also for circadian rhythms in the brain.
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Affiliation(s)
- Kosaku Masuda
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yoko Katsuda
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yasutaka Niwa
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
- Graduate School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan
| | - Takeshi Sakurai
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Arisa Hirano
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
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Aristimunha B, Bayerlein AJ, Cardoso MJ, Pinaya WHL, De Camargo RY. Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2023; 11:34595-34602. [PMID: 38292346 PMCID: PMC10824396 DOI: 10.1109/access.2023.3263477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 02/01/2024]
Abstract
Sleep is essential for physical and mental health. Polysomnography (PSG) procedures are labour-intensive and time-consuming, making diagnosing sleep disorders difficult. Automatic sleep staging using Machine Learning (ML) - based methods has been studied extensively, but frequently provides noisier predictions incompatible with typical manually annotated hypnograms. We propose an energy optimization method to improve the quality of hypnograms generated by automatic sleep staging procedures. The method evaluates the system's total energy based on conditional probabilities for each epoch's stage and employs an energy minimisation procedure. It can be used as a meta-optimisation layer over the sleep stage sequences generated by any classifier that generates prediction probabilities. The method improved the accuracy of state-of-the-art Deep Learning models in the Sleep EDFx dataset by 4.0% and in the DRM-SUB dataset by 2.8%.
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Affiliation(s)
- Bruno Aristimunha
- Center for Mathematics, Computing and Cognition (CMCC)Federal University of ABC (UFABC)São Paulo09210-580Brazil
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonWC2R 2LSLondonU.K
| | - Alexandre Janoni Bayerlein
- Center for Mathematics, Computing and Cognition (CMCC)Federal University of ABC (UFABC)São Paulo09210-580Brazil
| | - M. Jorge Cardoso
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonWC2R 2LSLondonU.K
| | - Walter Hugo Lopez Pinaya
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonWC2R 2LSLondonU.K
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Automatic sleep scoring using patient-specific ensemble models and knowledge distillation for ear-EEG data. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Haghayegh S, Hu K, Stone K, Redline S, Schernhammer E. Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study. J Med Internet Res 2023; 25:e40211. [PMID: 36763454 PMCID: PMC9960035 DOI: 10.2196/40211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/09/2022] [Accepted: 01/09/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. OBJECTIVE We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). METHODS SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). RESULTS The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. CONCLUSIONS SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.
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Affiliation(s)
- Shahab Haghayegh
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Kun Hu
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Katie Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, United States
| | - Susan Redline
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Eva Schernhammer
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
- Medical University of Vienna, Vienna, Austria
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A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG. Biomedicines 2023; 11:biomedicines11020327. [PMID: 36830864 PMCID: PMC9953225 DOI: 10.3390/biomedicines11020327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen's kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging.
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33
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Stanley N. The Future of Sleep Staging, Revisited. Nat Sci Sleep 2023; 15:313-322. [PMID: 37159812 PMCID: PMC10163901 DOI: 10.2147/nss.s405663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/19/2023] [Indexed: 05/11/2023] Open
Abstract
In 1996, I published a paper entitled "The Future of Sleep Staging". At this time, paper and ink records were the standard way of recording sleep records. Computerised systems had only recently become commercially available. The original article was a response to those initial computer-based systems, pointing out the potential limitations of the systems. Now, digital sleep recording is ubiquitous and software and hardware capabilities have improved immeasurably. However, I will argue that despite 50 years of progress, there has not been an increase in the accuracy of sleep staging. I will propose that this is due to the limitations of the task that we have set the automatic analysis methods.
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Affiliation(s)
- Neil Stanley
- Independent Sleep Expert, Farnborough, Hampshire, UK
- Correspondence: Neil Stanley, Email
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Somaskandhan P, Leppänen T, Terrill PI, Sigurdardottir S, Arnardottir ES, Ólafsdóttir KA, Serwatko M, Sigurðardóttir SÞ, Clausen M, Töyräs J, Korkalainen H. Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls. Front Neurol 2023; 14:1162998. [PMID: 37122306 PMCID: PMC10140398 DOI: 10.3389/fneur.2023.1162998] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/23/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.
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Affiliation(s)
- Pranavan Somaskandhan
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Pranavan Somaskandhan,
| | - Timo Leppänen
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Philip I. Terrill
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
| | - Sigridur Sigurdardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Erna Sif Arnardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
- Internal Medicine Services, Landspitali–The National University Hospital of Iceland, Reykjavik, Iceland
| | - Kristín A. Ólafsdóttir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Marta Serwatko
- Department of Clinical Engineering, Landspitali University Hospital, Reykjavik, Iceland
| | - Sigurveig Þ. Sigurðardóttir
- Department of Immunology, Landspitali University Hospital, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Michael Clausen
- Department of Allergy, Landspitali University Hospital, Reykjavik, Iceland
- Children's Hospital Reykjavik, Reykjavik, Iceland
| | - Juha Töyräs
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Henri Korkalainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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Kjaer TW, Rank ML, Hemmsen MC, Kidmose P, Mikkelsen K. Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography. PLOS DIGITAL HEALTH 2022; 1:e0000134. [PMID: 36812563 PMCID: PMC9931275 DOI: 10.1371/journal.pdig.0000134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/25/2022] [Indexed: 11/07/2022]
Abstract
While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.
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Affiliation(s)
| | | | | | - Preben Kidmose
- Department of Electrical and Computer Engineering, University of Aarhus, Denmark
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, University of Aarhus, Denmark,* E-mail:
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