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Dive into the research topics where Eileen B. Leary is active.

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Featured researches published by Eileen B. Leary.


Sleep | 2012

Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES).

Clete A. Kushida; Deborah A. Nichols; Tyson H. Holmes; Stuart F. Quan; James K. Walsh; Daniel J. Gottlieb; Richard D. Simon; Christian Guilleminault; David P. White; James L. Goodwin; Paula K. Schweitzer; Eileen B. Leary; Pamela R. Hyde; Max Hirshkowitz; Sylvan B. Green; Linda K. McEvoy; Cynthia S. Chan; Alan Gevins; Gary G. Kay; Daniel A. Bloch; Tami Crabtree; William C. Dement

STUDY OBJECTIVE To determine the neurocognitive effects of continuous positive airway pressure (CPAP) therapy on patients with obstructive sleep apnea (OSA). DESIGN, SETTING, AND PARTICIPANTS The Apnea Positive Pressure Long-term Efficacy Study (APPLES) was a 6-month, randomized, double-blind, 2-arm, sham-controlled, multicenter trial conducted at 5 U.S. university, hospital, or private practices. Of 1,516 participants enrolled, 1,105 were randomized, and 1,098 participants diagnosed with OSA contributed to the analysis of the primary outcome measures. INTERVENTION Active or sham CPAP MEASUREMENTS: THREE NEUROCOGNITIVE VARIABLES, EACH REPRESENTING A NEUROCOGNITIVE DOMAIN: Pathfinder Number Test-Total Time (attention and psychomotor function [A/P]), Buschke Selective Reminding Test-Sum Recall (learning and memory [L/M]), and Sustained Working Memory Test-Overall Mid-Day Score (executive and frontal-lobe function [E/F]) RESULTS The primary neurocognitive analyses showed a difference between groups for only the E/F variable at the 2 month CPAP visit, but no difference at the 6 month CPAP visit or for the A/P or L/M variables at either the 2 or 6 month visits. When stratified by measures of OSA severity (AHI or oxygen saturation parameters), the primary E/F variable and one secondary E/F neurocognitive variable revealed transient differences between study arms for those with the most severe OSA. Participants in the active CPAP group had a significantly greater ability to remain awake whether measured subjectively by the Epworth Sleepiness Scale or objectively by the maintenance of wakefulness test. CONCLUSIONS CPAP treatment improved both subjectively and objectively measured sleepiness, especially in individuals with severe OSA (AHI > 30). CPAP use resulted in mild, transient improvement in the most sensitive measures of executive and frontal-lobe function for those with severe disease, which suggests the existence of a complex OSA-neurocognitive relationship. CLINICAL TRIAL INFORMATION Registered at clinicaltrials.gov. Identifier: NCT00051363. CITATION Kushida CA; Nichols DA; Holmes TH; Quan SF; Walsh JK; Gottlieb DJ; Simon RD; Guilleminault C; White DP; Goodwin JL; Schweitzer PK; Leary EB; Hyde PR; Hirshkowitz M; Green S; McEvoy LK; Chan C; Gevins A; Kay GG; Bloch DA; Crabtree T; Demen WC. Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: the Apnea Positive Pressure Long-term Efficacy Study (APPLES). SLEEP 2012;35(12):1593-1602.


PLOS ONE | 2014

Design and Validation of a Periodic Leg Movement Detector

Hyatt Moore; Eileen B. Leary; Seo-Young Lee; Oscar Carrillo; Robin Stubbs; Paul E. Peppard; Terry Young; Bernard Widrow; Emmanuel Mignot

Periodic Limb Movements (PLMs) are episodic, involuntary movements caused by fairly specific muscle contractions that occur during sleep and can be scored during nocturnal polysomnography (NPSG). Because leg movements (LM) may be accompanied by an arousal or sleep fragmentation, a high PLM index (i.e. average number of PLMs per hour) may have an effect on an individual’s overall health and wellbeing. This study presents the design and validation of the Stanford PLM automatic detector (S-PLMAD), a robust, automated leg movement detector to score PLM. NPSG studies from adult participants of the Wisconsin Sleep Cohort (WSC, n = 1,073, 2000–2004) and successive Stanford Sleep Cohort (SSC) patients (n = 760, 1999–2007) undergoing baseline NPSG were used in the design and validation of this study. The scoring algorithm of the S-PLMAD was initially based on the 2007 American Association of Sleep Medicine clinical scoring rules. It was first tested against other published algorithms using manually scored LM in the WSC. Rules were then modified to accommodate baseline noise and electrocardiography interference and to better exclude LM adjacent to respiratory events. The S-PLMAD incorporates adaptive noise cancelling of cardiac interference and noise-floor adjustable detection thresholds, removes LM secondary to sleep disordered breathing within 5 sec of respiratory events, and is robust to transient artifacts. Furthermore, it provides PLM indices for sleep (PLMS) and wake plus periodicity index and other metrics. To validate the final S-PLMAD, experts visually scored 78 studies in normal sleepers and patients with restless legs syndrome, sleep disordered breathing, rapid eye movement sleep behavior disorder, narcolepsy-cataplexy, insomnia, and delayed sleep phase syndrome. PLM indices were highly correlated between expert, visually scored PLMS and automatic scorings (r2 = 0.94 in WSC and r2 = 0.94 in SSC). In conclusion, The S-PLMAD is a robust and high throughput PLM detector that functions well in controls and sleep disorder patients.


Sleep Medicine | 2015

Sleep-stage transitions during polysomnographic recordings as diagnostic features of type 1 narcolepsy

Julie Anja Engelhard Christensen; Oscar Carrillo; Eileen B. Leary; Paul E. Peppard; Terry Young; Helge Bjarrup Dissing Sorensen; Poul Jennum; Emmanuel Mignot

OBJECTIVE Type 1 narcolepsy/hypocretin deficiency is characterized by excessive daytime sleepiness, sleep fragmentation, and cataplexy. Short rapid eye movement (REM) latency (≤15 min) during nocturnal polysomnography (PSG) or during naps of the multiple sleep latency test (MSLT) defines a sleep-onset REM sleep period (SOREMP), a diagnostic hallmark. We hypothesized that abnormal sleep transitions other than SOREMPs can be identified in type 1 narcolepsy. METHODS Sleep-stage transitions (one to 10 epochs to one to five epochs of any other stage) and bout length features (one to 10 epochs) were extracted from PSGs. The first 15 min of sleep were excluded when a nocturnal SOREMP was recorded. F(0.1) measures and receiver operating characteristic curves were used to identify specific (≥98%) features. A data set of 136 patients and 510 sex- and age-matched controls was used for the training. A data set of 19 cases and 708 sleep-clinic patients was used for the validation. RESULTS (1) ≥5 transitions from ≥5 epochs of stage N1 or W to ≥2 epochs of REM sleep, (2) ≥22 transitions from ≥3 epochs of stage N2 or N3 to ≥2 epochs of N1 or W, and (3) ≥16 bouts of ≥6 epochs of N1 or W were found to be highly specific (≥98%). Sensitivity ranged from 16% to 30%, and it did not vary substantially with and without medication or a nocturnal SOREMP. In patients taking antidepressants, nocturnal SOREMPs occurred much less frequently (16% vs. 36%, p < 0.001). CONCLUSIONS Increased sleep-stage transitions notably from ≥2.5 min of W/N1 into REM are specifically diagnostic for narcolepsy independent of a nocturnal SOREMP.


Journal of Neuroscience Methods | 2017

Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy

Anders Vinther Olsen; Jens Stephansen; Eileen B. Leary; Paul E. Peppard; Hong Sheungshul; Poul Jennum; Helge Bjarup Dissing Sørensen; Emmanuel Mignot

BACKGROUND Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy. NEW METHOD A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane. COMPARISON WITH EXISTING METHODS This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods. RESULTS Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%. CONCLUSION Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result.


Journal of Sleep Research | 2018

Sleep disorders, depression and anxiety are associated with adverse safety outcomes in healthcare workers: A prospective cohort study

Matthew D. Weaver; Céline Vetter; Shantha M. W. Rajaratnam; Conor S. O’Brien; S Qadri; Ruth M. Benca; Ann E. Rogers; Eileen B. Leary; James K. Walsh; Charles A. Czeisler; Laura K. Barger

The objective of the study was to determine if sleep disorder, depression or anxiety screening status was associated with safety outcomes in a diverse population of hospital workers. A sample of shift workers at four hospitals participated in a prospective cohort study. Participants were screened for five sleep disorders, depression and anxiety at baseline, then completed prospective monthly surveys for the next 6 months to capture motor vehicle crashes, near‐miss crashes, occupational exposures and medical errors. We tested the associations between sleep disorders, depression and anxiety and adverse safety outcomes using incidence rate ratios adjusted for potentially confounding factors in a multivariable negative binomial regression model. Of the 416 hospital workers who participated, two in five (40.9%) screened positive for a sleep disorder and 21.6% screened positive for depression or anxiety. After multivariable adjustment, screening positive for a sleep disorder was associated with 83% increased incidence of adverse safety outcomes. Screening positive for depression or anxiety increased the risk by 63%. Sleep disorders and mood disorders were independently associated with adverse outcomes and contributed additively to risk. Our findings suggest that screening for sleep disorders and mental health screening can help identify individuals who are vulnerable to adverse safety outcomes. Future research should evaluate sleep and mental health screening, evaluation and treatment programmes that may improve safety.


Clinical Neurophysiology | 2018

Periodic limb movements in sleep: Prevalence and associated sleepiness in the Wisconsin Sleep Cohort

Eileen B. Leary; Hyatt Moore; Logan Schneider; Laurel Finn; Paul E. Peppard; Emmanuel Mignot

OBJECTIVES Periodic limb movements in sleep (PLMS) are thought to be prevalent in elderly populations, but their impact on quality of life remains unclear. We examined the prevalence of PLMS, impact of age on prevalence, and association between PLMS and sleepiness. METHODS We identified limb movements in 2335 Wisconsin Sleep Cohort polysomnograms collected over 12 years. Prevalence of periodic limb movement index (PLMI) ≥15 was calculated at baseline (n = 1084). McNemars test assessed changes in prevalence over time. Association of sleepiness and PLMS evaluated using linear mixed modeling and generalized estimating equations. Models adjusted for confounders. RESULTS Prevalence of PLMI ≥15 at baseline was 25.3%. Longitudinal prevalence increased significantly with age (p = 2.97 × 10-14). Sleepiness did not differ significantly between PLMI groups unless stratified by restless legs syndrome (RLS) symptoms. The RLS+/PLM+ group was sleepier than the RLS+/PLM- group. Multiple Sleep Latency Test trended towards increased alertness in the RLS-/PLM+ group compared to RLS-/PLM-. CONCLUSIONS A significant number of adults have PLMS and prevalence increased with age. No noteworthy association between PLMI category and sleepiness unless stratified by RLS symptoms. SIGNIFICANCE Our results indicate that RLS and PLMS may have distinct clinical consequences and interactions that can help guide treatment approach.


Sleep | 2017

Breathing Disturbances Without Hypoxia Are Associated With Objective Sleepiness in Sleep Apnea

Henriette Koch; Logan Schneider; Laurel Finn; Eileen B. Leary; Paul E. Peppard; Erika W. Hagen; Helge Bjarup Dissing Sørensen; Poul Jennum; Emmanuel Mignot

Study Objectives To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness. Methods A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ≥ 140/90 or antihypertensive use. Results H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ≥ 0.89, p ≤ .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (β = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; β = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (β = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (β = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity. Conclusions In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.


Sleep | 2011

The Association between Obstructive Sleep Apnea and Neurocognitive Performance—The Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Stuart F. Quan; Cynthia S. Chan; William C. Dement; Alan Gevins; James L. Goodwin; Daniel J. Gottlieb; Sylvan B. Green; Christian Guilleminault; Max Hirshkowitz; Pamela R. Hyde; Gary G. Kay; Eileen B. Leary; Deborah A. Nichols; Paula K. Schweitzer; Richard D. Simon; James K. Walsh; Clete A. Kushida


Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine | 2006

The Apnea Positive Pressure Long-term Efficacy Study (APPLES): rationale, design, methods, and procedures.

Clete A. Kushida; Deborah A. Nichols; Stuart F. Quan; James L. Goodwin; David P. White; Daniel J. Gottlieb; James K. Walsh; Paula K. Schweitzer; Christian Guilleminault; Richard D. Simon; Eileen B. Leary; Pamela R. Hyde; Tyson H. Holmes; Daniel A. Bloch; Sylvan B. Green; Linda K. McEvoy; Alan Gevins; William C. Dement


arXiv: Neural and Evolutionary Computing | 2017

The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy.

Jens Stephansen; Aditya Ambati; Eileen B. Leary; Hyatt Moore; Oscar Carrillo; Ling Lin; Birgit Högl; Ambra Stefani; Seung Chul Hong; Tae Won Kim; Fabio Pizza; Giuseppe Plazzi; Stefano Vandi; Elena Antelmi; Dimitri Perrin; Samuel T. Kuna; Paula K. Schweitzer; Clete A. Kushida; Paul E. Peppard; Poul Jennum; Helge Bjarup Dissing Sørensen; Emmanuel Mignot

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Paul E. Peppard

University of Wisconsin-Madison

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Poul Jennum

University of Copenhagen

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Alan Gevins

Michigan State University

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