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Cui Y, Li Y, Li Q, Huang J, Tan X, Zhan CA. Alpha anteriorization and theta posteriorization during deep sleep. J Neurosci Res 2024; 102:e25325. [PMID: 38562056 DOI: 10.1002/jnr.25325] [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: 05/15/2023] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
Brain states (wake, sleep, general anesthesia, etc.) are profoundly associated with the spatiotemporal dynamics of brain oscillations. Previous studies showed that the EEG alpha power shifted from the occipital cortex to the frontal cortex (alpha anteriorization) after being induced into a state of general anesthesia via propofol. The sleep research literature suggests that slow waves and sleep spindles are generated locally and propagated gradually to different brain regions. Since sleep and general anesthesia are conceptualized under the same framework of consciousness, the present study examines whether alpha anteriorization similarly occurs during sleep and how the EEG power in other frequency bands changes during different sleep stages. The results from the analysis of three polysomnography datasets of 234 participants show consistent alpha anteriorization during the sleep stages N2 and N3, beta anteriorization during stage REM, and theta posteriorization during stages N2 and N3. Although it is known that the neural circuits responsible for sleep are not exactly the same for general anesthesia, the findings of alpha anteriorization in this study suggest that, at macro level, the circuits for alpha oscillations are organized in the similar cortical areas. The spatial shifts of EEG power in different frequency bands during sleep may offer meaningful neurophysiological markers for the level of consciousness.
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Affiliation(s)
- Yue Cui
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yu Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qiqi Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Xiaodan Tan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Alattar M, Govind A, Mainali S. Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review. Bioengineering (Basel) 2024; 11:206. [PMID: 38534480 DOI: 10.3390/bioengineering11030206] [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: 12/04/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 03/28/2024] Open
Abstract
Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.
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Affiliation(s)
- Maha Alattar
- Division of Adult Neurology, Sleep Medicine, Vascular Neurology, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Alok Govind
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
| | - Shraddha Mainali
- Division of Vascular Neurology and Neurocritical Care, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA
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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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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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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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Choi I, Sung W. Single-Channel Ecg-Based Sleep Stage Classification With End-To-End Trainable Deep Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083334 DOI: 10.1109/embc40787.2023.10340302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The demand for automatic sleep stage classification using easily obtainable signals like electrocardiograms (ECGs) is rising due to the growing number of sleep disorder cases. Our study examined the potential of using single-channel ECG signals for user-friendly automatic sleep stage classification. Unlike previous studies that relied on manual features such as heart rate and variability, we propose using fully neural network-based features. The proposed model uses a ContextNet-based feature encoder applied to the ECG spectrogram, and a Transformer model to capture the temporal properties of sleep cycles over the course of the night.
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He Z, Tang M, Wang P, Du L, Chen X, Cheng G, Fang Z. Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Yang CY, Chen PC, Huang WC. Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models. SENSORS (BASEL, SWITZERLAND) 2023; 23:2458. [PMID: 36904661 PMCID: PMC10007254 DOI: 10.3390/s23052458] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG-EEG or EEG-ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging systems, respectively. The seizure model detected interictal and preictal periods, whereas the sleep staging model classified signals into five stages. The patient-specific seizure prediction model with six frozen layers achieved 100% accuracy for seven out of nine patients and required only 40 s of training time for personalization. Moreover, the cross-signal transfer learning EEG-ECG model for sleep staging achieved an accuracy approximately 2.5% higher than that of the ECG model; additionally, the training time was reduced by >50%. In summary, transfer learning from an EEG model to produce personalized models for a more convenient signal can both reduce the training time and increase the accuracy; moreover, challenges such as data insufficiency, variability, and inefficiency can be effectively overcome.
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Bakker JP, Ross M, Cerny A, Vasko R, Shaw E, Kuna S, Magalang UJ, Punjabi NM, Anderer P. Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring. Sleep 2023; 46:6628222. [PMID: 35780449 PMCID: PMC9905781 DOI: 10.1093/sleep/zsac154] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/22/2022] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. METHODS We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. RESULTS The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). CONCLUSIONS Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.
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Affiliation(s)
| | - Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
| | | | - Ray Vasko
- Philips Sleep and Respiratory Care, Pittsburgh, PA,USA
| | - Edmund Shaw
- Philips Sleep and Respiratory Care, Pittsburgh, PA,USA
| | - Samuel Kuna
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,USA.,Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA,USA
| | - Ulysses J Magalang
- Division of Pulmonary, Critical Care, and Sleep Medicine, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Naresh M Punjabi
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami FL, USA
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Alvarez-Estevez D, Rijsman RM. Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times. PLoS One 2022; 17:e0275530. [PMID: 36174095 PMCID: PMC9522290 DOI: 10.1371/journal.pone.0275530] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/19/2022] [Indexed: 11/18/2022] Open
Abstract
Study objectives To investigate inter-scorer agreement and scoring time differences associated with visual and computer-assisted analysis of polysomnographic (PSG) recordings. Methods A group of 12 expert scorers reviewed 5 PSGs that were independently selected in the context of each of the following tasks: (i) sleep staging, (ii) scoring of leg movements, (iii) detection of respiratory (apneic-related) events, and (iv) of electroencephalographic (EEG) arousals. All scorers independently reviewed the same recordings, hence resulting in 20 scoring exercises per scorer from an equal amount of different subjects. The procedure was repeated, separately, using the classical visual manual approach and a computer-assisted (semi-automatic) procedure. Resulting inter-scorer agreement and scoring times were examined and compared among the two methods. Results Computer-assisted sleep scoring showed a consistent and statistically relevant effect toward less time required for the completion of each of the PSG scoring tasks. Gain factors ranged from 1.26 (EEG arousals) to 2.41 (leg movements). Inter-scorer kappa agreement was also consistently increased with the use of supervised semi-automatic scoring. Specifically, agreement increased from Κ = 0.76 to K = 0.80 (sleep stages), Κ = 0.72 to K = 0.91 (leg movements), Κ = 0.55 to K = 0.66 (respiratory events), and Κ = 0.58 to Κ = 0.65 (EEG arousals). Inter-scorer agreement on the examined set of diagnostic indices did also show a trend toward higher Interclass Correlation Coefficient scores when using the semi-automatic scoring approach. Conclusions Computer-assisted analysis can improve inter-scorer agreement and scoring times associated with the review of PSG studies resulting in higher efficiency and overall quality in the diagnosis sleep disorders.
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Affiliation(s)
- Diego Alvarez-Estevez
- Center for Information and Communications Technology Research (CITIC), Universidade da Coruña, A Coruña, Spain
- * E-mail:
| | - Roselyne M. Rijsman
- Sleep Center and Clinical Neurophysiology Department, Haaglanden Medisch Centrum, The Hague, The Netherlands
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Hussain I, Hossain MA, Jany R, Bari MA, Uddin M, Kamal ARM, Ku Y, Kim JS. Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages. SENSORS 2022; 22:s22083079. [PMID: 35459064 PMCID: PMC9028257 DOI: 10.3390/s22083079] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/11/2022] [Accepted: 04/15/2022] [Indexed: 11/16/2022]
Abstract
Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.
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Affiliation(s)
- Iqram Hussain
- Department of Biomedical Engineering, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Korea;
| | - Md Azam Hossain
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (M.A.H.); (R.J.); (M.A.B.); (M.U.); (A.R.M.K.)
| | - Rafsan Jany
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (M.A.H.); (R.J.); (M.A.B.); (M.U.); (A.R.M.K.)
| | - Md Abdul Bari
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (M.A.H.); (R.J.); (M.A.B.); (M.U.); (A.R.M.K.)
| | - Musfik Uddin
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (M.A.H.); (R.J.); (M.A.B.); (M.U.); (A.R.M.K.)
| | - Abu Raihan Mostafa Kamal
- Network and Data Analysis Group, Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (M.A.H.); (R.J.); (M.A.B.); (M.U.); (A.R.M.K.)
| | - Yunseo Ku
- Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon 35015, Korea;
| | - Jik-Soo Kim
- Department of Computer Engineering, Myongji University, Yongin 17058, Korea
- Correspondence:
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