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Kafashan M, Gupte G, Kang P, Hyche O, Luong AH, Prateek GV, Ju YES, Palanca BJA. A personalized semi-automatic sleep spindle detection (PSASD) framework. J Neurosci Methods 2024; 407:110064. [PMID: 38301832 PMCID: PMC11219251 DOI: 10.1016/j.jneumeth.2024.110064] [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: 07/27/2023] [Revised: 01/19/2024] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. NEW METHODS A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. RESULTS A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. COMPARISON WITH EXISTING METHODS PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. CONCLUSIONS Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.
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Affiliation(s)
- MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA.
| | - Gaurang Gupte
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Paul Kang
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Orlandrea Hyche
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Anhthi H Luong
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - G V Prateek
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Yo-El S Ju
- Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA; Department of Neurology, Division of Sleep Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ben Julian A Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Bandyopadhyay A, Oks M, Sun H, Prasad B, Rusk S, Jefferson F, Malkani RG, Haghayegh S, Sachdeva R, Hwang D, Agustsson J, Mignot E, Summers M, Fabbri D, Deak M, Anastasi M, Sampson A, Van Hout S, Seixas A. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med 2024; 20:1183-1191. [PMID: 38533757 PMCID: PMC11217619 DOI: 10.5664/jcsm.11132] [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: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies 3 pivotal areas in sleep medicine that can benefit from AI technologies: clinical care, lifestyle management, and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI-enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI-enabled technologies and offers possible solutions. CITATION Bandyopadhyay A, Oks M, Sun H, et al. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med. 2024;20(7):1183-1191.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Margarita Oks
- Department of Medicine, Northwell Health System, New York, New York
| | - Haoqi Sun
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Bharati Prasad
- Department of Medicine, University of Illinois, Chicago, Illinois
| | - Sam Rusk
- EnsoData Research, EnsoData, Madison, Wisconsin
| | - Felicia Jefferson
- Department of Biochemistry and Molecular Biology, University of Nevada, Reno, Nevada
| | - Roneil Gopal Malkani
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Neurology Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois
| | - Shahab Haghayegh
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Ramesh Sachdeva
- Children’s Hospital of Michigan and Central Michigan University College of Medicine, Detroit, Michigan
| | - Dennis Hwang
- Kaiser Permanente Southern California, Los Angeles, California
| | | | - Emmanuel Mignot
- Stanford University, School of Medicine, Stanford, California
| | - Michael Summers
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Nebraska Medical Center, Omaha, Nebraska
| | | | | | | | | | | | - Azizi Seixas
- Department of Informatics and Health Data Science, University of Miami Miller School of Medicine, Miami, Florida
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Gillon CJ, Baker C, Ly R, Balzani E, Brunton BW, Schottdorf M, Ghosh S, Dehghani N. Open Data In Neurophysiology: Advancements, Solutions & Challenges. ARXIV 2024:arXiv:2407.00976v1. [PMID: 39010879 PMCID: PMC11247910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress1,2. For example, structural biology3 and genomics4,5 have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began (e.g.6). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. To this end, new social structures along with active and open scientific communities are essential7 to facilitate and expand the still limited adoption of open science practices in our field8. Unified by shared values of openness, we set out to organize a symposium for Open Data in Neuroscience (ODIN) to strengthen our community and facilitate transformative neuroscience research at large. In this report, we share what we learned during this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.
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Affiliation(s)
- Colleen J Gillon
- These authors contributed equally to this paper
- Department of Bioengineering, Imperial College London, London, UK
| | - Cody Baker
- These authors contributed equally to this paper
- CatalystNeuro, Benicia, CA, USA
| | - Ryan Ly
- These authors contributed equally to this paper
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Edoardo Balzani
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Manuel Schottdorf
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Satrajit Ghosh
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Nima Dehghani
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- These authors contributed equally to this paper
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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [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: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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Durka P, Dovgialo M, Duszyk-Bogorodzka A, Biegański P. Two-Stage Atomic Decomposition of Multichannel EEG and the Previously Undetectable Sleep Spindles. SENSORS (BASEL, SWITZERLAND) 2024; 24:842. [PMID: 38339559 PMCID: PMC10856903 DOI: 10.3390/s24030842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/16/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
We propose a two-step procedure for atomic decomposition of multichannel EEGs, based upon multivariate matching pursuit and dipolar inverse solution, from which atoms representing relevant EEG structures are selected according to prior knowledge. We detect sleep spindles in 147 polysomnographic recordings from the Montreal Archive of Sleep Studies. Detection is compared with human scorers and two state-of-the-art algorithms, which find only about a third of the structures conforming to the definition of sleep spindles and detected by the proposed method. We provide arguments supporting the thesis that the previously undetectable sleep spindles share the same properties as those marked by human experts and previously applied methods, and were previously omitted only because of unfavorable local signal-to-noise ratios, obscuring their visibility to both human experts and algorithms replicating their markings. All detected EEG structures are automatically parametrized by their time and frequency centers, width duration, phase, and spatial location of an equivalent dipolar source within the brain. It allowed us, for the first time, to estimate the spatial gradient of sleep spindles frequencies, which not only confirmed quantitatively the well-known prevalence of higher frequencies in posterior regions, but also revealed a significant gradient in the sagittal plane. The software used in this study is freely available.
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Affiliation(s)
- Piotr Durka
- Faculty of Physics, University of Warsaw, 02-093 Warsaw, Poland; (M.D.); (P.B.)
| | - Marian Dovgialo
- Faculty of Physics, University of Warsaw, 02-093 Warsaw, Poland; (M.D.); (P.B.)
| | - Anna Duszyk-Bogorodzka
- Behavioural Neuroscience Lab, Institute of Psychology, SWPS University, 03-815 Warsaw, Poland;
| | - Piotr Biegański
- Faculty of Physics, University of Warsaw, 02-093 Warsaw, Poland; (M.D.); (P.B.)
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Sui R, Li J, Shi Y, Yuan S, Wang H, Liao J, Gao X, Han D, Li Y, Wang X. Associations Between Sleep Spindle Metrics, Age, Education and Executive Function in Young Adult and Middle-Aged Patients with Obstructive Sleep Apnea. Nat Sci Sleep 2024; 16:1-15. [PMID: 38213412 PMCID: PMC10778138 DOI: 10.2147/nss.s436824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/18/2023] [Indexed: 01/13/2024] Open
Abstract
Purpose This study aimed to investigate the association between sleep spindle metrics and executive function in individuals with obstructive sleep apnea (OSA). Furthermore, we examined the association of age and education on executive function. Patients and Methods A total of 230 (40.90 ± 8.83 years, F/M = 45/185) participants were enrolled. Overnight electroencephalogram (C3-M2) recording detected sleep spindles by a novel U-Net-type neural network that integrates temporal information with time-frequency images. Sleep spindle metrics, including frequency (Hz), overall density (events/min), fast density (events/min), slow density (events/min), duration (sec) and amplitude (µV), were measured. Executive function was assessed using standardized neuropsychological tests. Associations between sleep spindle metrics, executive function, and demographic factors were analyzed using multivariate linear regression. Results In fully adjusted linear regression models, higher overall sleep spindle density (TMT-A, B=-1.279, p=0.009; TMT-B, B=-1.813, p=0.008), fast sleep spindle density (TMT-A, B=-1.542, p=0.048; TMT-B, B=-2.187, p=0.036) and slow sleep spindle density (TMT-A, B=-1.731, p=0.037; TMT-B, B=-2.449, p=0.034) were associated with better executive function. And the sleep spindle duration both during N2 sleep time (TMT-A, B=-13.932, p=0.027; TMT-B, B=-19.001, p=0.034) and N3 sleep time (TMT-B, B=-29.916, p=0.009; Stroop-incongruous, B=-21.303, p=0.035) was independently associated with better executive function in this population. Additionally, age and education were found to be highly associated with executive function. Conclusion Specific sleep spindle metrics, higher overall density, fast density and slow density during N2 sleep time, and longer duration during N2 and N3 sleep time, are independent and sensitive indicators of better executive function in young adult and middle-aged patients with OSA. Further research is needed to explore the underlying mechanisms and clinical implications of these findings.
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Affiliation(s)
- Rongcui Sui
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Jie Li
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People’s Republic of China
| | - Yunhan Shi
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Shizhen Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Huijun Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Jianhong Liao
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Xiang Gao
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Demin Han
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Yanru Li
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China
- Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China
- Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China
| | - Xingjun Wang
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People’s Republic of China
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Gavaret M, Iftimovici A, Pruvost-Robieux E. EEG: Current relevance and promising quantitative analyses. Rev Neurol (Paris) 2023; 179:352-360. [PMID: 36907708 DOI: 10.1016/j.neurol.2022.12.008] [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: 09/09/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 03/12/2023]
Abstract
Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.
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Affiliation(s)
- M Gavaret
- Université Paris Cité, INSERM UMR 1266, IPNP (Institute of Psychiatry and Neuroscience of Paris), France; Service de Neurophysiologie Clinique et Epileptologie, GHU Paris Psychiatrie et Neurosciences, Paris, France; FHU NeuroVasc, Paris, France.
| | - A Iftimovici
- Université Paris Cité, INSERM UMR 1266, IPNP (Institute of Psychiatry and Neuroscience of Paris), France; NeuroSpin, Atomic Energy Commission, Gif-sur-Yvette, France; Pôle PEPIT, GHU Paris Psychiatrie et Neurosciences, Paris, France
| | - E Pruvost-Robieux
- Université Paris Cité, INSERM UMR 1266, IPNP (Institute of Psychiatry and Neuroscience of Paris), France; Service de Neurophysiologie Clinique et Epileptologie, GHU Paris Psychiatrie et Neurosciences, Paris, France; FHU NeuroVasc, Paris, France
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Wei L, Ventura S, Ryan MA, Mathieson S, Boylan GB, Lowery M, Mooney C. Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles. Comput Biol Med 2022; 150:106096. [PMID: 36162199 DOI: 10.1016/j.compbiomed.2022.106096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 08/14/2022] [Accepted: 09/10/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis. METHOD We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set. RESULT Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions. CONCLUSION The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.
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Affiliation(s)
- Lan Wei
- UCD School of Computer Science, University College Dublin, Dublin, Ireland
| | - Soraia Ventura
- Department of Paediatrics & Child Health, University College Cork, Cork, Ireland; INFANT Research Centre, University College Cork, Cork, Ireland
| | - Mary Anne Ryan
- Department of Paediatrics & Child Health, University College Cork, Cork, Ireland; INFANT Research Centre, University College Cork, Cork, Ireland
| | - Sean Mathieson
- Department of Paediatrics & Child Health, University College Cork, Cork, Ireland; INFANT Research Centre, University College Cork, Cork, Ireland
| | - Geraldine B Boylan
- Department of Paediatrics & Child Health, University College Cork, Cork, Ireland; INFANT Research Centre, University College Cork, Cork, Ireland
| | - Madeleine Lowery
- UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Catherine Mooney
- UCD School of Computer Science, University College Dublin, Dublin, Ireland.
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Hassan U, Feld GB, Bergmann TO. Automated real-time EEG sleep spindle detection for brain-state-dependent brain stimulation. J Sleep Res 2022; 31:e13733. [PMID: 36130730 DOI: 10.1111/jsr.13733] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 10/14/2022]
Abstract
Sleep spindles are a hallmark electroencephalographic feature of non-rapid eye movement sleep, and are believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain-stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real-time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector, a robust multi-channel electroencephalographic signal-processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the real-time spindle detection method by streaming pre-recorded sleep electroencephalographic datasets to a real-time computer system running a Simulink® Real-Time™ implementation of the algorithm. Sleep spindles were detected with high levels of Sensitivity (~83%), Precision (~78%) and a convincing F1-Score (~81%) in reference to state-of-the-art offline algorithms (which reached similar or lower levels when compared with each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude and symmetry, and showed the typical time-frequency characteristics as well as a centroparietal topography. Spindles were detected close to their centre and reliably at the predefined target phase. The real-time spindle detection algorithm therefore empowers researchers to target spindles during human sleep, and apply the stimulation method and experimental paradigm of their choice.
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Affiliation(s)
- Umair Hassan
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany.,Department of Neurology & Stroke, Hertie Institute for Clinical Brain Research, Eberhard Karls University of Tübingen, Tübingen, Germany
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[Sleep spindles-Function, detection and use as biomarker for diagnostics in psychiatry]. DER NERVENARZT 2022; 93:882-891. [PMID: 35676333 DOI: 10.1007/s00115-022-01340-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/17/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND The sleep spindle is a graphoelement of an electroencephalogram (EEG), which can be observed in light and deep sleep. Alterations in spindle activity have been described for a range of psychiatric disorders. Due to their relatively constant properties, sleep spindles may therefore be potential biomarkers in psychiatric diagnostics. METHOD This article presents an overview of the state of the science on the characteristics and functions of the sleep spindle as well as known alterations of spindle activity in psychiatric disorders. Various methodological approaches and developments of spindle detection are explained with respect to their potential for application in psychiatric diagnostics. RESULTS AND CONCLUSION Although alterations in spindle activity in psychiatric disorders are known and have been described in detail, their exact potential for psychiatric diagnostics has yet to be fully determined. In this respect, the acquisition of knowledge in research is currently constrained by manual and automated methods for spindle detection, which require high levels of resources and are error prone. Newer approaches to spindle detection based on deep-learning procedures could overcome the difficulties of previous detection methods, and thus open up new possibilities for the practical application of sleep spindles as biomarkers in the psychiatric practice.
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