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Chen P, Chen D, Zhang L, Tang Y, Li X. Automated sleep spindle detection with mixed EEG features. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Smith SK, Nguyen T, Labonte AK, Kafashan M, Hyche O, Guay CS, Wilson E, Chan CW, Luong A, Hickman LB, Fritz BA, Emmert D, Graetz TJ, Melby SJ, Lucey BP, Ju YES, Wildes TS, Avidan MS, Palanca BJA. Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: a prospective observational study of delirium in elderly cardiac surgical patients. BMJ Open 2020; 10:e044295. [PMID: 33318123 PMCID: PMC7737109 DOI: 10.1136/bmjopen-2020-044295] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome. METHODS AND ANALYSIS P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time. ETHICS AND DISSEMINATION P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media. TRIAL REGISTRATION NUMBER NCT03291626.
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Affiliation(s)
- S Kendall Smith
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Thomas Nguyen
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Alyssa K Labonte
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - MohammadMehdi Kafashan
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Orlandrea Hyche
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Christian S Guay
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Elizabeth Wilson
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Courtney W Chan
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Anhthi Luong
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - L Brian Hickman
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Bradley A Fritz
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Daniel Emmert
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Thomas J Graetz
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Spencer J Melby
- Department of Surgery, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Brendan P Lucey
- Department of Neurology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Yo-El S Ju
- Department of Neurology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Troy S Wildes
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
| | - Ben J A Palanca
- Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University in St Louis, Saint Louis, Missouri, USA
- Division of Biology and Biomedical Sciences, Washington University in St Louis, Saint Louis, Missouri, USA
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Azimi H, Gunnarsdottir KM, Sarma SV, Gamaldo AA, Salas RME, Gamaldo CE. Identifying Sleep Biomarkers to Evaluate Cognition in HIV. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2332-2336. [PMID: 33018475 PMCID: PMC7939380 DOI: 10.1109/embc44109.2020.9176592] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Sleep disturbance and cognitive impairment represent two of the most common and debilitating conditions facing seropositive (HIV+) individuals who are otherwise well controlled with antiretroviral therapy. Sleep-assessment-based biomarkers represent an important step towards improving our understanding of the unique mechanistic features that may link sleep disruption and cognition in HIV+ individuals, ultimately leading to advancements in treatment and management options. In this study, a risk score was computed via a generalized linear model (GLM), which optimally combines polysomnography (PSG) features extracted from EEG, EMG, and EOG signals, to distinguish 18 HIV+ Black male individuals with and without cognitive impairment. The optimal set of features was identified via the least absolute shrinkage and selection operator (LASSO) approach, and the risk separation between the two groups, i.e., cognitively normal and cognitive impaired, was significant (and has a P-value < .001). The optimal set of predictive features were all EEG derived and sleep stage-specific. These preliminary findings suggest that sleep-based EEG features may be used as both diagnostic and prognostic biomarkers for cognition in HIV+ subjects.
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Kulkarni PM, Xiao Z, Robinson EJ, Jami AS, Zhang J, Zhou H, Henin SE, Liu AA, Osorio RS, Wang J, Chen Z. A deep learning approach for real-time detection of sleep spindles. J Neural Eng 2019; 16:036004. [PMID: 30790769 PMCID: PMC6527330 DOI: 10.1088/1741-2552/ab0933] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. APPROACH Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. MAIN RESULTS Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~two-three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. SIGNIFICANCE SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.
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Affiliation(s)
- Prathamesh M Kulkarni
- Department of Psychiatry, School of Medicine, New York University, New York, NY 10016, United States of America
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Kam K, Parekh A, Sharma RA, Andrade A, Lewin M, Castillo B, Bubu OM, Chua NJ, Miller MD, Mullins AE, Glodzik L, Mosconi L, Gosselin N, Prathamesh K, Chen Z, Blennow K, Zetterberg H, Bagchi N, Cavedoni B, Rapoport DM, Ayappa I, de Leon MJ, Petkova E, Varga AW, Osorio RS. Sleep oscillation-specific associations with Alzheimer's disease CSF biomarkers: novel roles for sleep spindles and tau. Mol Neurodegener 2019; 14:10. [PMID: 30791922 PMCID: PMC6385427 DOI: 10.1186/s13024-019-0309-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 02/08/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Based on associations between sleep spindles, cognition, and sleep-dependent memory processing, here we evaluated potential relationships between levels of CSF Aβ42, P-tau, and T-tau with sleep spindle density and other biophysical properties of sleep spindles in a sample of cognitively normal elderly individuals. METHODS One-night in-lab nocturnal polysomnography (NPSG) and morning to early afternoon CSF collection were performed to measure CSF Aβ42, P-tau and T-tau. Seven days of actigraphy were collected to assess habitual total sleep time. RESULTS Spindle density during NREM stage 2 (N2) sleep was negatively correlated with CSF Aβ42, P-tau and T-tau. From the three, CSF T-tau was the most significantly associated with spindle density, after adjusting for age, sex and ApoE4. Spindle duration, count and fast spindle density were also negatively correlated with T-tau levels. Sleep duration and other measures of sleep quality were not correlated with spindle characteristics and did not modify the associations between sleep spindle characteristics and the CSF biomarkers of AD. CONCLUSIONS Reduced spindles during N2 sleep may represent an early dysfunction related to tau, possibly reflecting axonal damage or altered neuronal tau secretion, rendering it a potentially novel biomarker for early neuronal dysfunction. Given their putative role in memory consolidation and neuroplasticity, sleep spindles may represent a mechanism by which tau impairs memory consolidation, as well as a possible target for therapeutic interventions in cognitive decline.
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Affiliation(s)
- Korey Kam
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Ankit Parekh
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Ram A. Sharma
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Andreia Andrade
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Monica Lewin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962 USA
| | - Bresne Castillo
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Omonigho M. Bubu
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Nicholas J. Chua
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Margo D. Miller
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Anna E. Mullins
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Lidia Glodzik
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Lisa Mosconi
- Department of Neurology, Weill Cornell Medical College, New York, NY USA
| | - Nadia Gosselin
- Center for Advanced Research in Sleep Medicine (CARSM), Department of Psychology, Hospital du Sacré-Coeur de Montreal, Montreal, Quebec, Canada and Université de Montreal, Montreal, Quebec Canada
| | | | - Zhe Chen
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Kaj Blennow
- Institute of Neuroscience and Psychiatry, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Institute of Neuroscience and Psychiatry, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Nisha Bagchi
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Bianca Cavedoni
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - David M. Rapoport
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Indu Ayappa
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Mony J. de Leon
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962 USA
| | - Eva Petkova
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
- Child and Adolescent Psychiatry, NYU School of Medicine, New York, NY 10016 USA
| | - Andrew W. Varga
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY 10029 USA
| | - Ricardo S. Osorio
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016 USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962 USA
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Sampson AL, Lainscsek C, Gonzalez CE, Ulbert I, Devinsky O, Fabó D, Madsen JR, Halgren E, Cash SS, Sejnowski TJ. Delay differential analysis for dynamical sleep spindle detection. J Neurosci Methods 2019; 316:12-21. [PMID: 30707917 DOI: 10.1016/j.jneumeth.2019.01.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 01/04/2019] [Accepted: 01/20/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND Sleep spindles are involved in memory consolidation and other cognitive functions. Numerous automated methods for detection of spindles have been proposed; most of these rely on spectral analysis in some form. However, none of these approaches are ideal, and novel approaches to the problem could provide additional insights. NEW METHOD Here, we apply delay differential analysis (DDA), a time-domain technique based on nonlinear dynamics to detect sleep spindles in human intracranial sleep data, including laminar electrode, stereoelectroencephalogram (sEEG), and electrocorticogram (ECoG) recordings. RESULTS We show that this approach is computationally fast, generalizable, requires minimal preprocessing, and provides excellent agreement with human scoring. COMPARISON WITH EXISTING METHODS We compared the method with established methods on a set of intracranial recordings and this method provided the highest agreement with human expert scoring when evaluated with F1 score while being the second-fastest to run. We also compared the results on the DREAMS surface EEG data, where the method produced a higher average F1 score than all other tested methods except the automated detections published with the DREAMS data. Further, in addition to being a fast and reliable method for spindle detection, DDA also provides a novel characterization of spindle activity based on nonlinear dynamical content of the data. CONCLUSIONS This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.
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Affiliation(s)
- Aaron L Sampson
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.
| | - Claudia Lainscsek
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
| | - Christopher E Gonzalez
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, H-1083 Budapest, Hungary
| | - Orrin Devinsky
- New York University Comprehensive Epilepsy Center, New York, NY 10016, USA
| | - Dániel Fabó
- Epilepsy Centrum, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Joseph R Madsen
- Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Eric Halgren
- Departments of Radiology and Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, MA 02114, USA
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA; Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
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Malerba P, Whitehurst LN, Simons SB, Mednick SC. Spatio-temporal structure of sleep slow oscillations on the electrode manifold and its relation to spindles. Sleep 2019; 42:5134206. [PMID: 30335179 PMCID: PMC6335956 DOI: 10.1093/sleep/zsy197] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2018] [Revised: 08/06/2018] [Indexed: 11/12/2022] Open
Abstract
Electrophysiological sleep rhythms have been shown to impact human waking cognition, but their spatio-temporal dynamics are not understood. We investigated how slow oscillations (SOs; 0.5-4 Hz) are organized during a night of polysomnographically-recorded sleep, focusing on the scalp electrode manifold. We detected troughs of SOs at all electrodes independently and analyzed the concurrent SO troughs found in every other electrode within ±400 ms. We used a k-clustering algorithm to categorize the spatial patterns of SO trough co-occurrence into three types (Global, Local or Frontal) depending on their footprint on the electrode manifold during the considered time window. When comparing the clusters across non-rapid eye movement (NREM) sleep stages, we found a relatively larger fraction of Local SOs in slow wave sleep (SWS) compared to stage 2, and larger fraction of Global SOs in stage 2 compared to SWS. The probability of SO detection in time between two electrodes showed that SO troughs of all types co-occurred at some nearby electrodes, but only Global troughs had traveling wave profiles, moving anteriorly to posteriorly. Global SOs also had larger amplitudes at frontal electrodes and stronger coupling with fast spindles (12.5-16 Hz). Indeed, SO-spindle complexes were more likely to be detected following a Global SO trough compared to SOs in other clusters. Also, the phase-amplitude modulation of SOs over spindles (modulation vector) was higher for Global SOs across the electrode manifold. Given the recent evidence of a link between thalamocortical coupling and cognition, our findings suggest stronger cognitive relevance of Global SOs as compared to other SO types in sleep memory processing. Clinical Trials No clinical trial is related to this study.
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Affiliation(s)
- Paola Malerba
- Department of Cognitive Sciences, University of California Irvine, Irvine, CA
| | | | | | - Sara C Mednick
- Department of Cognitive Sciences, University of California Irvine, Irvine, CA
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Gunnarsdottir KM, Gamaldo CE, Salas RME, Ewen JB, Allen RP, Sarma SV. A Novel Sleep Stage Scoring System: Combining Expert-Based Rules with a Decision Tree Classifier. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:3240-3243. [PMID: 30441082 DOI: 10.1109/embc.2018.8513039] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Overnight polysomnography (PSG) is the gold standard tool used to characterize sleep and for diagnosing sleep disorders. PSG is a non-invasive procedure that collects various physiological data which is then scored by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules. In this study, we aimed to automate the process of sleep stage scoring of overnight PSG data while adhering to expert-based rules. We developed an algorithm utilizing a likelihood ratio decision tree classifier and extracted features from EEG, EMG and EOG signals based on predefined rules of the American Academy of Sleep Medicine Manual. Specifically, features were computed in 30-second epochs in the time and the frequency domains of the signals and used as inputs to the classifier which assigned each epoch to one of five possible stages: N3, N2, N1, REM or Wake. The algorithm was trained and tested on PSG data from 38 healthy individuals with no reported sleep disturbances. The overall scoring accuracy was 80.70% on the test set, which was comparable to the training set. Our results imply that the automatic classification is highly robust, fast, consistent with visual scoring and is highly interpretable.
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Akella S, Jose Principe C. Quantitative Analysis of a Marked Point Process based Sleep Spindle Detector (MPP-SSD). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1464-1467. [PMID: 30440669 DOI: 10.1109/embc.2018.8512508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Sleep spindles result from interactions between the thalamic and cortical neurons during the NREM2 stage. Studies show that these waxing and waning episodes of field potentials may have an implied role in memory consolidation, cellular plasticity and neuronal development besides serving as important markers in several neuronal pathologies. For these reasons, accurate spindle scoring of polysomnographic signals is important and has garnered interest in automating the tedious process of scoring via visual inspection. In this paper, we employ a transient model for automatic sleep spindle detection designed as a Marked Point Process (MPP). Further, in order to simplify the model development, the determination of the atoms was done independently for each of the EEG bands. However, this brings the problem of quantifying the effect of the required bandpass filtering, which was not done in previous work. Here we change the Q- factor of the filters and evaluate the effect on the detections provided by the model, when compared with two sleep experts. Several statistics are utilized, and we conclude that the design of the bandpass filters affects the performance. Low Q filters were thought necessary, but the results show that the optimal Q - factor is around 2.
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LaRocco J, Franaszczuk PJ, Kerick S, Robbins K. Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles. J Neural Eng 2018; 15:066015. [PMID: 30132445 DOI: 10.1088/1741-2552/aadc1c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE EEG spindles, narrow-band oscillatory signal bursts, are widely-studied biomarkers of subject state and neurological function. Most existing methods for spindle detection select algorithm parameters by optimizing agreement with expert labels. We propose a new framework for selecting algorithm parameters based on stability of spindle properties and elucidate the dependence of these properties on parameter selection for several algorithms. APPROACH To demonstrate this approach we developed a new algorithm (Spindler) that decomposes the signal using matching pursuit with Gabor atoms and computes the spindles for each point in a fine grid of parameter values. After computing characteristic surfaces as a function of parameters, Spindler selects algorithm parameters based on the stability of characteristic surface geometry. MAIN RESULTS Spindler performs well relative to several common supervised and unsupervised EEG sleep spindle detection methods. Spindler is available as an open-source MATLAB toolbox (https://github.com/VisLab/EEG-Spindles). In addition to Spindler, the toolbox provides implementations of several other spindle detection algorithms as well as standardized methods for matching ground truth to predictions and a framework for understanding algorithm parameter surfaces. SIGNIFICANCE This work demonstrates that parameter selection based on physical constraints rather than labelled data can provide effective, fully-automated, unsupervised spindle detection. This work also exposes the dangers of applying cross-validation without considering the dependence of spindle properties on parameters. Parameters selected to optimize one performance metric or matching method are not optimized for others. Furthermore, elucidation of the stability of predicted indicators with respect to algorithm parameter selection is critical to practical application of these algorithms.
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Affiliation(s)
- J LaRocco
- University of Texas, Department of Computer Science, San Antonio, Texas 78249, United States of America. US Army Research Laboratory, Human Research and Engineering Directorate, Aberdeen Proving Ground, Maryland 21287, United States of America
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11
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Liu MY, Huang A, Huang NE. Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms. Front Hum Neurosci 2017; 11:261. [PMID: 28572762 PMCID: PMC5435763 DOI: 10.3389/fnhum.2017.00261] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 05/02/2017] [Indexed: 11/13/2022] Open
Abstract
Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.
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Affiliation(s)
- Min-Yin Liu
- Department of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central UniversityTaoyuan, Taiwan
| | - Adam Huang
- Research Center for Adaptive Data Analysis, National Central UniversityTaoyuan, Taiwan
| | - Norden E Huang
- Department of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central UniversityTaoyuan, Taiwan.,Research Center for Adaptive Data Analysis, National Central UniversityTaoyuan, Taiwan
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Zhuang X, Li Y, Peng N. Enhanced automatic sleep spindle detection: a sliding window-based wavelet analysis and comparison using a proposal assessment method. ACTA ACUST UNITED AC 2016. [DOI: 10.1186/s40535-016-0027-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Younes M, Hanly PJ. Minimizing Interrater Variability in Staging Sleep by Use of Computer-Derived Features. J Clin Sleep Med 2016; 12:1347-1356. [PMID: 27448418 DOI: 10.5664/jcsm.6186] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 06/06/2016] [Indexed: 01/16/2023]
Abstract
STUDY OBJECTIVES Inter-scorer variability in sleep staging of polysomnograms (PSGs) results primarily from difficulty in determining whether: (1) an electroencephalogram pattern of wakefulness spans > 15 sec in transitional epochs, (2) spindles or K complexes are present, and (3) duration of delta waves exceeds 6 sec in a 30-sec epoch. We hypothesized that providing digitally derived information about these variables to PSG scorers may reduce inter-scorer variability. METHODS Fifty-six PSGs were scored (five-stage) by two experienced technologists, (first manual, M1). Months later, the technologists edited their own scoring (second manual, M2). PSGs were then scored with an automatic system and the same two technologists and an additional experienced technologist edited them, epoch-by-epoch (Edited-Auto). This resulted in seven manual scores for each PSG. The two M2 scores were then independently modified using digitally obtained values for sleep depth and delta duration and digitally identified spindles and K complexes. RESULTS Percent agreement between scorers in M2 was 78.9 ± 9.0% before modification and 96.5 ± 2.6% after. Errors of this approach were defined as a change in a manual score to a stage that was not assigned by any scorer during the seven manual scoring sessions. Total errors averaged 7.1 ± 3.7% and 6.9 ± 3.8% of epochs for scorers 1 and 2, respectively, and there was excellent agreement between the modified score and the initial manual score of each technologist. CONCLUSIONS Providing digitally obtained information about sleep depth, delta duration, spindles and K complexes during manual scoring can greatly reduce interrater variability in sleep staging by eliminating the guesswork in scoring epochs with equivocal features.
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Affiliation(s)
- Magdy Younes
- YRT Ltd, Winnipeg, MB, Canada.,Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada.,Sleep Disorders Centre, Winnipeg, Manitoba, Canada
| | - Patrick J Hanly
- Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada
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Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods. Neural Plast 2016; 2016:6783812. [PMID: 27478649 PMCID: PMC4958487 DOI: 10.1155/2016/6783812] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 04/27/2016] [Indexed: 12/16/2022] Open
Abstract
Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.
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Tsanas A, Clifford GD. Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing. Front Hum Neurosci 2015; 9:181. [PMID: 25926784 PMCID: PMC4396195 DOI: 10.3389/fnhum.2015.00181] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Accepted: 03/17/2015] [Indexed: 12/05/2022] Open
Abstract
Sleep spindles are critical in characterizing sleep and have been associated with cognitive function and pathophysiological assessment. Typically, their detection relies on the subjective and time-consuming visual examination of electroencephalogram (EEG) signal(s) by experts, and has led to large inter-rater variability as a result of poor definition of sleep spindle characteristics. Hitherto, many algorithmic spindle detectors inherently make signal stationarity assumptions (e.g., Fourier transform-based approaches) which are inappropriate for EEG signals, and frequently rely on additional information which may not be readily available in many practical settings (e.g., more than one EEG channels, or prior hypnogram assessment). This study proposes a novel signal processing methodology relying solely on a single EEG channel, and provides objective, accurate means toward probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT) with a Morlet basis function, identifying regions of interest where the power of the CWT coefficients corresponding to the frequencies of spindles (11-16 Hz) is large. The potential for assessing the signal segment as a spindle is refined using local weighted smoothing techniques. We evaluate our findings on two databases: the MASS database comprising 19 healthy controls and the DREAMS sleep spindle database comprising eight participants diagnosed with various sleep pathologies. We demonstrate that we can replicate the experts' sleep spindles assessment accurately in both databases (MASS database: sensitivity: 84%, specificity: 90%, false discovery rate 83%, DREAMS database: sensitivity: 76%, specificity: 92%, false discovery rate: 67%), outperforming six competing automatic sleep spindle detection algorithms in terms of correctly replicating the experts' assessment of detected spindles.
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Affiliation(s)
- Athanasios Tsanas
- Department of Engineering Science, Institute of Biomedical Engineering, University of OxfordOxford, UK
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of OxfordOxford, UK
- Nuffield Department of Medicine, Sleep and Circadian Neuroscience Institute, University of OxfordUK
| | - Gari D. Clifford
- Nuffield Department of Medicine, Sleep and Circadian Neuroscience Institute, University of OxfordUK
- Department of Biomedical Informatics, Emory UniversityAtlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of TechnologyAtlanta, GA, USA
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Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nat Methods 2014; 11:385-92. [PMID: 24562424 PMCID: PMC3972193 DOI: 10.1038/nmeth.2855] [Citation(s) in RCA: 224] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Accepted: 01/31/2014] [Indexed: 11/19/2022]
Abstract
Sleep spindles are discrete, intermittent patterns of brain activity that arise as a result of interactions of several circuits in the brain. Increasingly, these oscillations are of biological and clinical interest because of their role in development, learning, and neurological disorders. We used an internet interface to ‘crowdsource’ spindle identification from human experts and non-experts, and compared performance with 6 automated detection algorithms in middle-to-older aged subjects from the general population. We also developed a method for forming group consensus, and refined methods of evaluating the performance of event detectors in physiological data such as polysomnography. Compared to the gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. Crowdsourcing the scoring of sleep data is an efficient method to collect large datasets, even for difficult tasks such as spindle identification. Further refinements to automated sleep spindle algorithms are needed for middle-to-older aged subjects.
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