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Di Marco T, Scammell TE, Sadeghi K, Datta AN, Little D, Tjiptarto N, Djonlagic I, Olivieri A, Zammit G, Krystal A, Pathmanathan J, Donoghue J, Hubbard J, Dauvilliers Y. Hyperarousal features in the sleep architecture of individuals with and without insomnia. J Sleep Res 2024:e14256. [PMID: 38853521 DOI: 10.1111/jsr.14256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/08/2024] [Accepted: 05/20/2024] [Indexed: 06/11/2024]
Abstract
Sleep architecture encodes relevant information on the structure of sleep and has been used to assess hyperarousal in insomnia. This study investigated whether polysomnography-derived sleep architecture displays signs of hyperarousal in individuals with insomnia compared with individuals without insomnia. Data from Phase 3 clinical trials, private clinics and a cohort study were analysed. A comprehensive set of sleep architecture features previously associated with hyperarousal were retrospectively analysed focusing on sleep-wake transition probabilities, electroencephalographic spectra and sleep spindles, and enriched with a novel machine learning algorithm called the Wake Electroencephalographic Similarity Index. This analysis included 1710 individuals with insomnia and 1455 individuals without insomnia. Results indicate that individuals with insomnia had a higher likelihood of waking from all sleep stages, and showed increased relative alpha during Wake and N1 sleep and increased theta power during Wake when compared with individuals without insomnia. Relative delta power was decreased and Wake Electroencephalographic Similarity Index scores were elevated across all sleep stages except N3, suggesting more wake-like activity during these stages in individuals with insomnia. Additionally, sleep spindle density was decreased, and spindle dispersion was increased in individuals with insomnia. These findings suggest that insomnia is characterized by a dysfunction in sleep quality with a continuous hyperarousal, evidenced by changes in sleep-wake architecture.
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Affiliation(s)
- Tobias Di Marco
- Idorsia Pharmaceuticals Ltd, Allschwil, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
| | - Thomas E Scammell
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | | | - David Little
- Beacon Biosignals, Inc., Boston, Massachusetts, USA
| | | | - Ina Djonlagic
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | - Gary Zammit
- Clinilabs Drug Development Corporation, New York, New York, USA
| | - Andrew Krystal
- University of California, San Francisco, California, USA
| | | | | | | | - Yves Dauvilliers
- Centre National de Référence Narcolepsie, Unité du Sommeil, CHU Montpellier, Hôpital Gui-de-Chauliac, Université de Montpellier, INSERM INM, Montpellier, France
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Moguilner SG, Berezuk C, Bender AC, Pellerin KR, Gomperts SN, Cash SS, Sarkis RA, Lam AD. Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease. Alzheimers Dement 2024; 20:4234-4249. [PMID: 38764252 PMCID: PMC11180941 DOI: 10.1002/alz.13861] [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: 02/12/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition. METHODS We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy. RESULTS AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified "fast cognitive decliners" among study participants who had AD. DISCUSSION Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development. HIGHLIGHTS Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.
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Affiliation(s)
- Sebastian G. Moguilner
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Courtney Berezuk
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Alex C. Bender
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kyle R. Pellerin
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Stephen N. Gomperts
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Sydney S. Cash
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Rani A. Sarkis
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyBrigham and Women's HospitalBostonMassachusettsUSA
| | - Alice D. Lam
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
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Binzagr F. Explainable AI-driven model for gastrointestinal cancer classification. Front Med (Lausanne) 2024; 11:1349373. [PMID: 38686367 PMCID: PMC11056557 DOI: 10.3389/fmed.2024.1349373] [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: 12/04/2023] [Accepted: 04/04/2024] [Indexed: 05/02/2024] Open
Abstract
Although the detection procedure has been shown to be highly effective, there are several obstacles to overcome in the usage of AI-assisted cancer cell detection in clinical settings. These issues stem mostly from the failure to identify the underlying processes. Because AI-assisted diagnosis does not offer a clear decision-making process, doctors are dubious about it. In this instance, the advent of Explainable Artificial Intelligence (XAI), which offers explanations for prediction models, solves the AI black box issue. The SHapley Additive exPlanations (SHAP) approach, which results in the interpretation of model predictions, is the main emphasis of this work. The intermediate layer in this study was a hybrid model made up of three Convolutional Neural Networks (CNNs) (InceptionV3, InceptionResNetV2, and VGG16) that combined their predictions. The KvasirV2 dataset, which comprises pathological symptoms associated to cancer, was used to train the model. Our combined model yielded an accuracy of 93.17% and an F1 score of 97%. After training the combined model, we use SHAP to analyze images from these three groups to provide an explanation of the decision that affects the model prediction.
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Affiliation(s)
- Faisal Binzagr
- Department of Computer Science, King Abdulaziz University, Rabigh, Saudi Arabia
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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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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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Rosenblum Y, Weber FD, Rak M, Zavecz Z, Kunath N, Breitenstein B, Rasch B, Zeising M, Uhr M, Steiger A, Dresler M. Sustained polyphasic sleep restriction abolishes human growth hormone release. Sleep 2024; 47:zsad321. [PMID: 38124288 DOI: 10.1093/sleep/zsad321] [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: 08/14/2023] [Revised: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
STUDY OBJECTIVES Voluntary sleep restriction is a common phenomenon in industrialized societies aiming to increase time spent awake and thus productivity. We explored how restricting sleep to a radically polyphasic schedule affects neural, cognitive, and endocrine characteristics. METHODS Ten young healthy participants were restricted to one 20-minute nap opportunity at the end of every 4 hours (i.e. six sleep episodes per 24 hours) without any extended core sleep window, which resulted in a cumulative sleep amount of just 2 hours per day (i.e. ~20 minutes per bout). RESULTS All but one participant terminated this schedule during the first month. The remaining participant (a 25-year-old male) succeeded in adhering to a polyphasic schedule for five out of the eight planned weeks. Cognitive and psychiatric measures showed modest changes during polyphasic as compared to monophasic sleep, while in-blood cortisol or melatonin release patterns and amounts were apparently unaltered. In contrast, growth hormone release was almost entirely abolished (>95% decrease), with the residual release showing a considerably changed polyphasic secretional pattern. CONCLUSIONS Even though the study was initiated by volunteers with exceptional intrinsic motivation and commitment, none of them could tolerate the intended 8 weeks of the polyphasic schedule. Considering the decreased vigilance, abolished growth hormone release, and neurophysiological sleep changes observed, it is doubtful that radically polyphasic sleep schedules can subserve the different functions of sleep to a sufficient degree.
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Affiliation(s)
- Yevgenia Rosenblum
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Frederik D Weber
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Michael Rak
- Department of Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Zsófia Zavecz
- Center for Human Sleep Science, Department of Psychology, University of California Berkeley, Berkeley, CA, USA
| | - Nicolas Kunath
- Department of Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | | | - Björn Rasch
- Department of Psychology, Division of Biopsychology, University of Zurich, Zurich, Switzerland
| | - Marcel Zeising
- Klinikum Ingolstadt, Centre of Mental Health, Ingolstadt, Germany
| | - Manfred Uhr
- Department of Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Axel Steiger
- Department of Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Centre, Nijmegen, Netherlands
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Chato L, Regentova E. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. J Pers Med 2023; 13:1703. [PMID: 38138930 PMCID: PMC10744730 DOI: 10.3390/jpm13121703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.
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Affiliation(s)
- Lina Chato
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA;
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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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Bender AC, Jaleel A, Pellerin KR, Moguilner S, Sarkis RA, Cash SS, Lam AD. Altered Sleep Microarchitecture and Cognitive Impairment in Patients With Temporal Lobe Epilepsy. Neurology 2023; 101:e2376-e2387. [PMID: 37848332 PMCID: PMC10752648 DOI: 10.1212/wnl.0000000000207942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/28/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate the spatiotemporal characteristics of sleep waveforms in temporal lobe epilepsy (TLE) and examine their association with cognition. METHODS In this retrospective, cross-sectional study, we examined overnight EEG data from adult patients with TLE and nonepilepsy comparisons (NECs) admitted to the epilepsy monitoring unit at Mass General Brigham hospitals. Automated algorithms were used to characterize sleep macroarchitecture (sleep stages) and microarchitecture (spindles, slow oscillations [SOs]) on scalp EEG and to detect hippocampal interictal epileptiform discharges (hIEDs) from foramen ovale electrodes simultaneously recorded in a subset of patients with TLE. We examined the association of sleep features and hIEDs with memory and executive function from clinical neuropsychological evaluations. RESULTS A total of 81 adult patients with TLE and 28 NEC adult patients were included with similar mean ages. There were no significant differences in sleep macroarchitecture between groups, including relative time spent in each sleep stage, sleep efficiency, and sleep fragmentation. By contrast, the spatiotemporal characteristics of sleep microarchitecture were altered in TLE compared with NEC and were associated with cognitive impairments. Specifically, we observed a ∼30% reduction in spindle density in patients with TLE compared with NEC, which was significantly associated with worse memory performance. Spindle-SO coupling strength was also reduced in TLE and, in contrast to spindles, was associated with diminished executive function. We found no significant association between sleep macroarchitectural and microarchitectural parameters and hIEDs. DISCUSSION There is a fundamental alteration of sleep microarchitecture in TLE, characterized by a reduction in spindle density and spindle-SO coupling, and these changes may contribute to neurocognitive comorbidity in this disorder.
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Affiliation(s)
- Alex C Bender
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA.
| | - Afareen Jaleel
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Kyle R Pellerin
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Sebastian Moguilner
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Rani A Sarkis
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Sydney S Cash
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
| | - Alice D Lam
- From the Epilepsy Service (A.C.B., A.J., K.R.P., S.M., S.S.C., A.D.L.), Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston; and Epilepsy Service (R.A.S.), Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA
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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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12
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Gao DR, Li J, Wang MQ, Wang LT, Zhang YQ. Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention. Front Neurosci 2023; 17:1143495. [PMID: 37090812 PMCID: PMC10117677 DOI: 10.3389/fnins.2023.1143495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/13/2023] [Indexed: 04/09/2023] Open
Abstract
The diagnosis and management of sleep problems depend heavily on sleep staging. For autonomous sleep staging, many data-driven deep learning models have been presented by trying to construct a large-labeled auxiliary sleep dataset and test it by electroencephalograms on different subjects. These approaches suffer a significant setback cause it assumes the training and test data come from the same or similar distribution. However, this is almost impossible in scenario cross-dataset due to inherent domain shift between domains. Unsupervised domain adaption was recently created to address the domain shift issue. However, only a few customized UDA solutions for sleep staging due to two limitations in previous UDA methods. First, the domain classifier does not consider boundaries between classes. Second, they depend on a shared model to align the domain that could miss the information of domains when extracting features. Given those restrictions, we present a novel UDA approach that combines category decision boundaries and domain discriminator to align the distributions of source and target domains. Also, to keep the domain-specific features, we create an unshared attention method. In addition, we investigated effective data augmentation in cross-dataset sleep scenarios. The experimental results on three datasets validate the efficacy of our approach and show that the proposed method is superior to state-of-the-art UDA methods on accuracy and MF1-Score.
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Affiliation(s)
- Dong-Rui Gao
- School of Computer Science, Chengdu University of Information Technology, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Li
- School of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Man-Qing Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu-Tao Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Yong-Qing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, China
- *Correspondence: Yong-Qing Zhang
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Wȩsierski D, Rufuie MR, Milczarek O, Ziembla W, Ogniewski P, Kołodziejak A, Niedbalski P. Rating by detection: an artifact detection protocol for rating EEG quality with average event duration. J Neural Eng 2023; 20. [PMID: 36758229 DOI: 10.1088/1741-2552/acbabe] [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: 05/10/2022] [Accepted: 02/09/2023] [Indexed: 02/11/2023]
Abstract
Objective. Quantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real electroencephalography (EEG) optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically corrected EEG without access to ground truth in real-world conditions.Approach. Our offline evaluation protocol uses a detector to score the presence of artifacts. It computes their average duration, which measures the recovered EEG's deviation from the modeled background activity with a single score. As we expect the detector to make generalization errors, we employ a generic and configurable Wiener-based artifact removal method to validate the reliability of our detection protocol.Main results. Quantitative experiments extensively compare many Wiener filters and show their consistent rankings agree with their theoretical assumptions and expectations.Significance. The rating-by-detection protocol with the average event duration measure should be of value for EEG practitioners and developers. After removing artifacts from real EEG, the protocol experimentally shows that reliable comparisons between many artifact filtering configurations are possible despite the missing ground-truth neural signals.
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Affiliation(s)
- Daniel Wȩsierski
- Gdańsk University of Technology, Faculty of Electronics, Telecommunications, and Informatics, Gdańsk, Poland
| | | | - Olga Milczarek
- Department of Children's Neurosurgery, Jagiellonian University Medical College, Cracow, Poland
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Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2023; 27:39-55. [PMID: 35262853 PMCID: PMC8904207 DOI: 10.1007/s11325-022-02592-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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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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Azami H, Moguilner S, Penagos H, Sarkis RA, Arnold SE, Gomperts SN, Lam AD. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease. J Alzheimers Dis 2023; 91:1557-1572. [PMID: 36641682 PMCID: PMC10039707 DOI: 10.3233/jad-221152] [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] [Indexed: 01/15/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. OBJECTIVE To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. METHODS We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. RESULTS SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. CONCLUSION SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.
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Affiliation(s)
- Hamed Azami
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sebastian Moguilner
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hector Penagos
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rani A. Sarkis
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Steven E. Arnold
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Stephen N. Gomperts
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alice D. Lam
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
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Pei W, He T, Yang P, Lv X, Jiao B, Meng F, Yan Y, Cui L, He G, Zhou X, Wen G, Ruan J, Lu L. Acupuncture combined with cognitive-behavioural therapy for insomnia (CBT-I) in patients with insomnia: study protocol for a randomised controlled trial. BMJ Open 2022; 12:e063442. [PMID: 36585134 PMCID: PMC9809230 DOI: 10.1136/bmjopen-2022-063442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
INTRODUCTION Insomnia affects physical and mental health due to the lack of continuous and complete sleep architecture. Polysomnograms (PSGs) are used to record electrical information to perform sleep architecture using deep learning. Although acupuncture combined with cognitive-behavioural therapy for insomnia (CBT-I) could not only improve sleep quality, solve anxiety, depression but also ameliorate poor sleep habits and detrimental cognition. Therefore, this study will focus on the effects of electroacupuncture combined with CBT-I on sleep architecture with deep learning. METHODS AND ANALYSIS This randomised controlled trial will evaluate the efficacy and effectiveness of electroacupuncture combined with CBT-I in patients with insomnia. Participants will be randomised to receive either electroacupuncture combined with CBT-I or sham acupuncture combined with CBT-I and followed up for 4 weeks. The primary outcome is sleep quality, which is evaluated by the Pittsburgh Sleep Quality Index. The secondary outcome measures include a measurement of depression severity, anxiety, maladaptive cognitions associated with sleep and adverse events. Sleep architecture will be assessed using deep learning on PSGs. ETHICS AND DISSEMINATION This trial has been approved by the institutional review boards and ethics committees of the First Affiliated Hospital of Sun Yat-sun University (2021763). The results will be disseminated through peer-reviewed journals. The results of this trial will be disseminated through peer-reviewed publications and conference abstracts or posters. TRIAL REGISTRATION NUMBER CTR2100052502.
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Affiliation(s)
- Wenya Pei
- Department of Acupuncture, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Te He
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Pei Yang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xiaozhou Lv
- Department of Traditional Chinese Medicine, Zhongshan School of Medicine, Sun Yat-senUniversity, Guangzhou, China
| | - Boyu Jiao
- Department of Acupuncture, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Fanqi Meng
- Department of Acupuncture, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yingshuo Yan
- Department of Respiratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Liqian Cui
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzho, China
| | - Guanheng He
- Department of Acupuncture, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xin Zhou
- Department of Acupuncture, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guihua Wen
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Jingwen Ruan
- Department of Acupuncture, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Liming Lu
- South China Research Center for Acupuncture and Moxibustion, Medical College of Acu-Moxi and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China
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18
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Avberšek LK, Repovš G. Deep learning in neuroimaging data analysis: Applications, challenges, and solutions. FRONTIERS IN NEUROIMAGING 2022; 1:981642. [PMID: 37555142 PMCID: PMC10406264 DOI: 10.3389/fnimg.2022.981642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/10/2022] [Indexed: 08/10/2023]
Abstract
Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.
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Affiliation(s)
- Lev Kiar Avberšek
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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19
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Dupre AE, Cronin MFM, Schmugge S, Tate S, Wack A, Prescott BR, Li C, Auerbach S, Suchdev K, Al-Faraj A, He W, Cervantes-Arslanian AM, Abdennadher M, Saxena A, Lehan W, Russo M, Pugsley B, Greer D, Shin M, Ong CJ. A machine learning eye movement detection algorithm using electrooculography. Sleep 2022; 46:6762708. [PMID: 36255119 DOI: 10.1093/sleep/zsac254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/25/2022] [Indexed: 12/04/2022] Open
Abstract
Abstract
Study Objectives
Eye movement quantification in polysomnograms (PSG) is difficult and resource intensive. Automated eye movement detection would enable further study of eye movement patterns in normal and abnormal sleep, which could be clinically diagnostic of neurologic disorders, or used to monitor potential treatments. We trained a long short-term memory (LSTM) algorithm that can identify eye movement occurrence with high sensitivity and specificity.
Methods
We conducted a retrospective, single-center study using one-hour PSG samples from 47 patients 18–90 years of age. Team members manually identified and trained an LSTM algorithm to detect eye movement presence, direction, and speed. We performed a 5-fold cross validation and implemented a “fuzzy” evaluation method to account for misclassification in the preceding and subsequent 1-second of gold standard manually labeled eye movements. We assessed G-means, discrimination, sensitivity, and specificity.
Results
Overall, eye movements occurred in 9.4% of the analyzed EOG recording time from 47 patients. Eye movements were present 3.2% of N2 (lighter stages of sleep) time, 2.9% of N3 (deep sleep), and 19.8% of REM sleep. Our LSTM model had average sensitivity of 0.88 and specificity of 0.89 in 5-fold cross validation, which improved to 0.93 and 0.92 respectively using the fuzzy evaluation scheme.
Conclusion
An automated algorithm can detect eye movements from EOG with excellent sensitivity and specificity. Noninvasive, automated eye movement detection has several potential clinical implications in improving sleep study stage classification and establishing normal eye movement distributions in healthy and unhealthy sleep, and in patients with and without brain injury.
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Affiliation(s)
- Alicia E Dupre
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Michael F M Cronin
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Stephen Schmugge
- Department of Computer Science, University of North Carolina , Charlotte, NC, 28223 , USA
| | - Samuel Tate
- Department of Computer Science, University of North Carolina , Charlotte, NC, 28223 , USA
| | - Audrey Wack
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Brenton R Prescott
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Cheyi Li
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Sanford Auerbach
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Kushak Suchdev
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Abrar Al-Faraj
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Wei He
- Department of Pulmonology and Critical Care Medicine, Tufts Medical Center , Boston, MA, 02111 , USA
| | - Anna M Cervantes-Arslanian
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Myriam Abdennadher
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Aneeta Saxena
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Walter Lehan
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Mary Russo
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Brian Pugsley
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - David Greer
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
| | - Min Shin
- Department of Computer Science, University of North Carolina , Charlotte, NC, 28223 , USA
| | - Charlene J Ong
- Department of Neurology, Boston Medical Center , Boston, MA, 02118 , USA
- Department of Neurology, Boston University School of Medicine , Boston , MA, 02118 , USA
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Pini N, Ong JL, Yilmaz G, Chee NIYN, Siting Z, Awasthi A, Biju S, Kishan K, Patanaik A, Fifer WP, Lucchini M. An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device. Front Neurosci 2022; 16:974192. [PMID: 36278001 PMCID: PMC9584568 DOI: 10.3389/fnins.2022.974192] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Background The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG). Materials and methods This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch. Results We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusion The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.
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Affiliation(s)
- Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicholas I. Y. N. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhao Siting
- Electronic and Information Engineering, Imperial College London, London, United Kingdom
| | - Animesh Awasthi
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, India
| | - Siddharth Biju
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, India
| | | | | | - William P. Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States
| | - Maristella Lucchini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
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Zou G, Liu J, Zou Q, Gao JH. A-PASS: An automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. J Neural Eng 2022; 19. [PMID: 35878599 DOI: 10.1088/1741-2552/ac83f2] [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: 03/12/2022] [Accepted: 07/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. APPROACH A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods. MAIN RESULTS The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs. SIGNIFICANCE A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.
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Affiliation(s)
- Guangyuan Zou
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Jiayi Liu
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Qihong Zou
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Jia-Hong Gao
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
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Simple and Autonomous Sleep Signal Processing System for the Detection of Obstructive Sleep Apneas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116934. [PMID: 35682516 PMCID: PMC9180386 DOI: 10.3390/ijerph19116934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/03/2022] [Indexed: 02/04/2023]
Abstract
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive upper airway obstruction, intermittent hypoxemia, and recurrent awakenings during sleep. The most used treatment for this syndrome is a device that generates a positive airway pressure—Continuous Positive Airway Pressure (CPAP), but it works continuously, whether or not there is apnea. An alternative consists on systems that detect apnea episodes and produce a stimulus that eliminates them. Article focuses on the development of a simple and autonomous processing system for the detection of obstructive sleep apneas, using polysomnography (PSG) signals: electroencephalography (EEG), electromyography (EMG), respiratory effort (RE), respiratory flow (RF), and oxygen saturation (SO2). The system is evaluated using, as a gold standard, 20 PSG tests labeled by sleep experts and it performs two analyses. A first analysis detects awake/sleep stages and is based on the accumulated amplitude in a channel-dependent frequency range, according to the criteria of the American Academy of Sleep Medicine (AASM). The second analysis detects hypopneas and apneas, based on analysis of the breathing cycle and oxygen saturation. The results show a good estimation of sleep events, where for 75% of the cases of patients analyzed it is possible to determine the awake/asleep states with an effectiveness of >92% and apneas and hypopneas with an effectiveness of >55%, through a simple processing system that could be implemented in an electronic device to be used in possible OSA treatments.
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Abou Jaoude M, Jacobs CS, Sarkis RA, Jing J, Pellerin KR, Cole AJ, Cash SS, Westover MB, Lam AD. Noninvasive Detection of Hippocampal Epileptiform Activity on Scalp Electroencephalogram. JAMA Neurol 2022; 79:614-622. [PMID: 35499837 PMCID: PMC9062772 DOI: 10.1001/jamaneurol.2022.0888] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 03/09/2022] [Indexed: 01/18/2023]
Abstract
Importance The hippocampus is a highly epileptogenic brain region, yet over 90% of hippocampal epileptiform activity (HEA) cannot be identified on scalp electroencephalogram (EEG) by human experts. Currently, detection of HEA requires intracranial electrodes, which limits our understanding of the role of HEA in brain diseases. Objective To develop and validate a machine learning algorithm that accurately detects HEA from a standard scalp EEG, without the need for intracranial electrodes. Design, Setting, and Participants In this diagnostic study, conducted from 2008 to 2021, EEG data were used from patients with temporal lobe epilepsy (TLE) and healthy controls (HCs) to train and validate a deep neural network, HEAnet, to detect HEA on scalp EEG. Participants were evaluated at tertiary-level epilepsy centers at 2 academic hospitals: Massachusetts General Hospital (MGH) or Brigham and Women's Hospital (BWH). Included in the study were patients aged 12 to 78 years with a clinical diagnosis of TLE and HCs without epilepsy. Patients with TLE and HCs with a history of intracranial surgery were excluded from the study. Exposures Simultaneous intracranial EEG and/or scalp EEG. Main Outcomes and Measures Performance was assessed using cross-validated areas under the receiver operating characteristic curve (AUC ROC) and precision-recall curve (AUC PR) and additional clinically relevant metrics. Results HEAnet was trained and validated using data sets that were derived from a convenience sample of 141 eligible participants (97 with TLE and 44 HCs without epilepsy) whose retrospective EEG data were readily available. Data set 1 included the simultaneous scalp EEG and intracranial electrode recordings of 51 patients with TLE (mean [SD] age, 40.7 [15.9] years; 30 men [59%]) at MGH. An automatically generated training data set with 972 095 positive HEA examples was created, in addition to a held-out expert-annotated testing data set with 22 762 positive HEA examples. HEAnet's performance was validated on 2 independent scalp EEG data sets: (1) data set 2 (at MGH; 24 patients with TLE and 20 HCs; mean [SD] age, 42.3 [16.2] years; 17 men [39%]) and (2) data set 3 (at BWH; 22 patients with TLE and 24 HCs; mean [SD] age, 43.0 [14.4] years; 20 men [43%]). For single-event detection of HEA on data set 1, HEAnet achieved a mean (SD) AUC ROC of 0.89 (0.01) and a mean (SD) AUC PR of 0.39 (0.03). On external validation with data sets 2 and 3, HEAnet accurately distinguished TLE from HC (AUC ROC of 0.88 and 0.95, respectively) and predicted epilepsy lateralization with 100% and 92% accuracy, respectively. HEAnet tracked dynamic changes in HEA in response to seizure medication adjustments and performed comparably with human experts in diagnosing TLE from 1-hour scalp EEG recordings, diagnosing TLE in several individuals that experts missed. Without reducing specificity, addition of HEAnet to human expert EEG review increased sensitivity for diagnosing TLE in humans from 50% to 58% to 63% to 67%. Conclusions and Relevance Results of this diagnostic study suggest that HEAnet provides a novel, noninvasive, quantitative, and clinically relevant biomarker of hippocampal hyperexcitability in humans.
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Affiliation(s)
| | - Claire S. Jacobs
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Rani A. Sarkis
- Harvard Medical School, Boston, Massachusetts
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston
| | | | - Andrew J. Cole
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Alice D. Lam
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
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Thakre TP, Kulkarni H, Adams KS, Mischel R, Hayes R, Pandurangi A. Polysomnographic identification of anxiety and depression using deep learning. J Psychiatr Res 2022; 150:54-63. [PMID: 35358832 DOI: 10.1016/j.jpsychires.2022.03.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conserved health state. Anxiety and depression have a bidirectional relationship with sleep. This study reports on the use of analysis of polysomnographic data using deep learning methods to detect the presence of anxiety and depression. Polysomnography data on 940 patients performed at an academic sleep center during the 3-year period from 01/01/2016 to 12/31/2018 were identified for analysis. The data were divided into 3 subgroups: 205 patients with Anxiety/Depression, 349 patients with no Anxiety/Depression, and 386 patients with likely Anxiety/Depression. The first two subgroups were used for training and testing of the deep learning algorithm, and the third subgroup was used for external validation of the resulting model. Hypnograms were constructed via automatic sleep staging, with the 12-channel PSG data being transformed into three-channel RGB (red, green, blue channels) images for analysis. Composite patient images were generated and utilized for training the Xception model, which provided a validation set accuracy of 0.9782 on the ninth training epoch. In the independent test set, the model achieved a high accuracy (0.9688), precision (0.9533), recall (0.9630), and F1-score (0.9581). Classification performance of most other mainstream deep learning models was comparable. These findings suggest that machine learning techniques have the potential to accurately detect the presence of anxiety and depression from analysis of sleep study data. Further studies are needed to explore the utility of these techniques in the field of psychiatry.
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Affiliation(s)
- Tushar P Thakre
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA; Center for Sleep Medicine, Virginia Commonwealth University Health, Richmond, VA, USA
| | | | - Katie S Adams
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA; Department of Pharmacy Services, Virginia Commonwealth University Health, Richmond, VA, USA
| | - Ryan Mischel
- Department of Psychiatry, Washington University at St. Louis School of Medicine, St. Louis, MO, USA
| | - Ronnie Hayes
- Center for Sleep Medicine, Virginia Commonwealth University Health, Richmond, VA, USA
| | - Ananda Pandurangi
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
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25
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Li X, Wang Y, Zhao Y, Wei Y. Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning. Front Physiol 2022; 13:880966. [PMID: 35492597 PMCID: PMC9043555 DOI: 10.3389/fphys.2022.880966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 03/28/2022] [Indexed: 11/22/2022] Open
Abstract
The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.
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Affiliation(s)
- Xiaofeng Li
- Department of Information Engineering, Heilongjiang International University, Harbin, China
- *Correspondence: Xiaofeng Li,
| | - Yanwei Wang
- School of Mechanical Engineering, Harbin Institute of Petroleum, Harbin, China
| | | | - Yanbo Wei
- School of Automatic Control Engineering, Harbin Institute of Petroleum, Harbin, China
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Phan H, Mikkelsen K. Automatic sleep staging of EEG signals: recent development, challenges, and future directions. Physiol Meas 2022; 43. [PMID: 35320788 DOI: 10.1088/1361-6579/ac6049] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.
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Affiliation(s)
- Huy Phan
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Rd, London, E1 4NS, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, Aarhus Universitet, Finlandsgade 22, Aarhus, 8000, DENMARK
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27
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Ebbehoj A, Thunbo MØ, Andersen OE, Glindtvad MV, Hulman A. Transfer learning for non-image data in clinical research: A scoping review. PLOS DIGITAL HEALTH 2022; 1:e0000014. [PMID: 36812540 PMCID: PMC9931256 DOI: 10.1371/journal.pdig.0000014] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/15/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. METHODS AND FINDINGS We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). CONCLUSIONS In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.
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Affiliation(s)
- Andreas Ebbehoj
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Aarhus University, Denmark
| | | | | | | | - Adam Hulman
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Denmark
- * E-mail:
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28
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Anderer P, Ross M, Cerny A, Shaw E. Automated Scoring of Sleep and Associated Events. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:107-130. [PMID: 36217081 DOI: 10.1007/978-3-031-06413-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Conventionally, sleep and associated events are scored visually by trained technologists according to the rules summarized in the American Academy of Sleep Medicine Manual. Since its first publication in 2007, the manual was continuously updated; the most recent version as of this writing was published in 2020. Human expert scoring is considered as gold standard, even though there is increasing evidence of limited interrater reliability between human scorers. Significant advances in machine learning have resulted in powerful methods for addressing complex classification problems such as automated scoring of sleep and associated events. Evidence is increasing that these autoscoring systems deliver performance comparable to manual scoring and offer several advantages to visual scoring: (1) avoidance of the rather expensive, time-consuming, and difficult visual scoring task that can be performed only by well-trained and experienced human scorers, (2) attainment of consistent scoring results, and (3) proposition of added value such as scoring in real time, sleep stage probabilities per epoch (hypnodensity), estimates of signal quality and sleep/wake-related features, identifications of periods with clinically relevant ambiguities (confidence trends), configurable sensitivity and rule settings, as well as cardiorespiratory sleep staging for home sleep apnea testing. This chapter describes the development of autoscoring systems since the first attempts in the 1970s up to the most recent solutions based on deep neural network approaches which achieve an accuracy that allows to use the autoscoring results directly for review and interpretation by a physician.
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Affiliation(s)
- Peter Anderer
- Philips Sleep and Respiratory Care, Vienna, Austria.
- The Siesta Group Schlafanalyse GmbH, Vienna, Austria.
| | - Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
| | | | - Edmund Shaw
- Philips Sleep and Respiratory Care, Pittsburgh, PA, USA
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Usmani RSA, Pillai TR, Hashem IAT, Marjani M, Shaharudin R, Latif MT. Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:56759-56771. [PMID: 34075501 DOI: 10.1007/s11356-021-14305-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/03/2021] [Indexed: 06/12/2023]
Abstract
Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.
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Affiliation(s)
- Raja Sher Afgun Usmani
- School of Computer Science and Engineering, Taylor's University, Subang Jaya, Selangor, Malaysia.
| | | | - Ibrahim Abaker Targio Hashem
- College of Computing and Informatics, Department of Computer Science, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Mohsen Marjani
- School of Computer Science and Engineering, Taylor's University, Subang Jaya, Selangor, Malaysia
| | - Rafiza Shaharudin
- Environmental Health Research Centre, Institute for Medical Research, Ministry of Health Malaysia, Setia Alam, 40170, Shah Alam, Selangor, Malaysia
| | - Mohd Talib Latif
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
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Yoo C, Lee HW, Kang JW. Transferring Structured Knowledge in Unsupervised Domain Adaptation of a Sleep Staging Network. IEEE J Biomed Health Inform 2021; 26:1273-1284. [PMID: 34388101 DOI: 10.1109/jbhi.2021.3103614] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automatic sleep staging based on deep learning (DL) has been attracting attention for analyzing sleep quality and determining treatment effects. It is challenging to acquire long-term sleep data from numerous subjects and manually labeling them even though most DL-based models are trained using large-scale sleep data to provide state-of-the-art performance. One way to overcome this data shortage is to create a pre-trained network with an existing large-scale dataset (source domain) that is applicable to small cohorts of datasets (target domain); however, discrepancies in data distribution between the domains prevent successful refinement of this approach. In this paper, we propose an unsupervised domain adaptation method for sleep staging networks to reduce discrepancies by realigning the domains in the same space and producing domain-invariant features. Specifically, in addition to a classical domain discriminator, we introduce local dis-criminators-subject and stage-to maintain the intrinsic structure of sleep data to decrease local misalignments while using adversarial learning to play a minimax game between the feature extractor and discriminators. Moreover, we present several optimization schemes during training because the conventional adversarial learning is not effective to our training scheme. We evaluate the performance of the proposed method by examining the staging performances of a baseline network compared with direct transfer (DT) learning in various conditions. The experimental results demonstrate that the proposed domain adaptation significantly improves the performance though it needs no labeled sleep data in target domain.
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31
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Balzekas I, Sladky V, Nejedly P, Brinkmann BH, Crepeau D, Mivalt F, Gregg NM, Pal Attia T, Marks VS, Wheeler L, Riccelli TE, Staab JP, Lundstrom BN, Miller KJ, Van Gompel J, Kremen V, Croarkin PE, Worrell GA. Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies. Front Hum Neurosci 2021; 15:702605. [PMID: 34381344 PMCID: PMC8349989 DOI: 10.3389/fnhum.2021.702605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/29/2021] [Indexed: 01/10/2023] Open
Abstract
Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.
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Affiliation(s)
- Irena Balzekas
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
| | - Vladimir Sladky
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Petr Nejedly
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czechia
| | - Benjamin H. Brinkmann
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Daniel Crepeau
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Filip Mivalt
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Nicholas M. Gregg
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Tal Pal Attia
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Victoria S. Marks
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
| | - Lydia Wheeler
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
| | - Tori E. Riccelli
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
| | - Jeffrey P. Staab
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
- Department of Otorhinolaryngology, Mayo Clinic, Rochester, MN, United States
| | - Brian Nils Lundstrom
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Kai J. Miller
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Jamie Van Gompel
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Vaclav Kremen
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
| | - Paul E. Croarkin
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Gregory A. Worrell
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
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Cesari M, Stefani A, Penzel T, Ibrahim A, Hackner H, Heidbreder A, Szentkirályi A, Stubbe B, Völzke H, Berger K, Högl B. Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm. J Clin Sleep Med 2021; 17:1237-1247. [PMID: 33599203 DOI: 10.5664/jcsm.9174] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
STUDY OBJECTIVES The objective of this study was to evaluate interrater reliability between manual sleep stage scoring performed in 2 European sleep centers and automatic sleep stage scoring performed by the previously validated artificial intelligence-based Stanford-STAGES algorithm. METHODS Full night polysomnographies of 1,066 participants were included. Sleep stages were manually scored in Berlin and Innsbruck sleep centers and automatically scored with the Stanford-STAGES algorithm. For each participant, we compared (1) Innsbruck to Berlin scorings (INN vs BER); (2) Innsbruck to automatic scorings (INN vs AUTO); (3) Berlin to automatic scorings (BER vs AUTO); (4) epochs where scorers from Innsbruck and Berlin had consensus to automatic scoring (CONS vs AUTO); and (5) both Innsbruck and Berlin manual scorings (MAN) to the automatic ones (MAN vs AUTO). Interrater reliability was evaluated with several measures, including overall and sleep stage-specific Cohen's κ. RESULTS Overall agreement across participants was substantial for INN vs BER (κ = 0.66 ± 0.13), INN vs AUTO (κ = 0.68 ± 0.14), CONS vs AUTO (κ = 0.73 ± 0.14), and MAN vs AUTO (κ = 0.61 ± 0.14), and moderate for BER vs AUTO (κ = 0.55 ± 0.15). Human scorers had the highest disagreement for N1 sleep (κN1 = 0.40 ± 0.16 for INN vs BER). Automatic scoring had lowest agreement with manual scorings for N1 and N3 sleep (κN1 = 0.25 ± 0.14 and κN3 = 0.42 ± 0.32 for MAN vs AUTO). CONCLUSIONS Interrater reliability for sleep stage scoring between human scorers was in line with previous findings, and the algorithm achieved an overall substantial agreement with manual scoring. In this cohort, the Stanford-STAGES algorithm showed similar performances to the ones achieved in the original study, suggesting that it is generalizable to new cohorts. Before its integration in clinical practice, future independent studies should further evaluate it in other cohorts.
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Affiliation(s)
- Matteo Cesari
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Ambra Stefani
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Saratov State University, Saratov, Russian Federation
| | - Abubaker Ibrahim
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Heinz Hackner
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Heidbreder
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - András Szentkirályi
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Beate Stubbe
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Birgit Högl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
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33
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Li X, Cui L, Zhang GQ, Lhatoo SD. Can Big Data guide prognosis and clinical decisions in epilepsy? Epilepsia 2021; 62 Suppl 2:S106-S115. [PMID: 33529363 PMCID: PMC8011949 DOI: 10.1111/epi.16786] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/19/2020] [Accepted: 11/19/2020] [Indexed: 01/16/2023]
Abstract
Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Licong Cui
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Guo-Qiang Zhang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Samden D. Lhatoo
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
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34
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Imtiaz SA. A Systematic Review of Sensing Technologies for Wearable Sleep Staging. SENSORS (BASEL, SWITZERLAND) 2021; 21:1562. [PMID: 33668118 PMCID: PMC7956647 DOI: 10.3390/s21051562] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/13/2021] [Accepted: 02/20/2021] [Indexed: 12/15/2022]
Abstract
Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.
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Affiliation(s)
- Syed Anas Imtiaz
- Wearable Technologies Lab, Imperial College London, London SW7 2AZ, UK
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35
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Elgart M, Redline S, Sofer T. Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research. Neurotherapeutics 2021; 18:228-243. [PMID: 33829409 PMCID: PMC8116376 DOI: 10.1007/s13311-021-01014-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/18/2021] [Indexed: 12/11/2022] Open
Abstract
Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
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