Bio

Projects


  • Stanford EEG Viewer, Stanford University (March 2010)

    Signal processing and pattern recognition software for evaluation and automated analysis of polysomnography based sleep studies.

    Location

    Stanford, CA, USA

Professional

Work Experience


  • Naval Officer, U.S. Navy (May 25, 2001 - September 30, 2009)

    Location

    Misawa, Japan

Publications

Journal Articles


  • Exploring medical diagnostic performance using interactive, multi-parameter sourced receiver operating characteristic scatter plots. Computers in biology and medicine Moore, H. E., Andlauer, O., Simon, N., Mignot, E. 2014; 47: 120-129

    Abstract

    Determining diagnostic criteria for specific disorders is often a tedious task that involves determining optimal diagnostic thresholds for symptoms and biomarkers using receiver-operating characteristic (ROC) statistics. To help this endeavor, we developed softROC, a user-friendly graphic-based tool that lets users visually explore possible ROC tradeoffs. The software requires MATLAB installation and an Excel file containing threshold symptoms/biological measures, with corresponding gold standard diagnoses for a set of patients. The software scans the input file for diagnostic and symptom/biomarkers columns, and populates the graphical-user-interface (GUI). Users select symptoms/biomarkers of interest using Boolean algebra as potential inputs to create diagnostic criteria outputs. The software evaluates subtests across the user-established range of cut-points and compares them to a gold standard in order to generate ROC and quality ROC scatter plots. These plots can be examined interactively to find optimal cut-points of interest for a given application (e.g. sensitivity versus specificity needs). Split-set validation can also be used to set up criteria and validate these in independent samples. Bootstrapping is used to produce confidence intervals. Additional statistics and measures are provided, such as the area under the ROC curve (AUC). As a testing set, softROC is used to investigate nocturnal polysomnogram measures as diagnostic features for narcolepsy. All measures can be outputted to a text file for offline analysis. The softROC toolbox, with clinical training data and tutorial instruction manual, is provided as supplementary material and can be obtained online at http://www.stanford.edu/~hyatt4/software/softroc or from the open source repository at http://www.github.com/informaton/softroc.

    View details for DOI 10.1016/j.compbiomed.2014.01.012

    View details for PubMedID 24561350

  • Periodic Leg Movements during Sleep Are Associated with Polymorphisms in BTBD9, TOX3/BC034767, MEIS1, MAP2K5/SKOR1, and PTPRD. Sleep Moore, H., Winkelmann, J., Lin, L., Finn, L., Peppard, P., Mignot, E. 2014; 37 (9)

    Abstract

    To examine association between periodic leg movements (PLM) and 13 single nucleotide polymorphisms (SNPs) in 6 loci known to increase risk of restless legs syndrome (RLS).Stanford Center for Sleep Sciences and Medicine and Clinical Research Unit of University of Wisconsin Institute for Clinical and Translational Research.Adult participants (n = 1,090, mean age = 59.7 years) from the Wisconsin Sleep Cohort (2,394 observations, 2000-2012).A previously validated automatic detector was used to measure PLMI. Thirteen SNPs within BTBD9, TOX3/BC034767, MEIS1 (2 unlinked loci), MAP2K5/SKOR1, and PTPRD were tested. Analyses were performed using a linear model and by PLM category using a 15 PLM/h cutoff. Statistical significance for loci was Bonferroni corrected for 6 loci (P < 8.3 × 10(-3)). RLS symptoms were categorized into four groups: likely, possible, no symptoms, and unknown based on a mailed survey response.Prevalence of PLMI ≥ 15 was 33%. Subjects with PLMs were older, more likely to be male, and had more frequent RLS symptoms, a shorter total sleep time, and higher wake after sleep onset. Strong associations were found at all loci except one. Highest associations for PLMI > 15/h were obtained using a multivariate model including age, sex, sleep disturbances, and the best SNPs for each loci, yielding the following odds ratios (OR) and P values: BTBD9 rs3923809(A) OR = 1.65, P = 1.5×10(-8); TOX3/BC034767 rs3104788(T) OR = 1.35, P = 9.0 × 10(-5); MEIS1 rs12469063(G) OR = 1.38, P = 2.0 × 10(-4); MAP2K5/SKOR1 rs6494696(G) OR = 1.24, P = 1.3×10(-2); and PTPRD(A) rs1975197 OR = 1.31, P = 6.3×10(-3). Linear regression models also revealed significant PLM effects for BTBD9, TOX3/BC034767, and MEIS1. Co-varying for RLS symptoms only modestly reduced the genetic associations.Single nucleotide polymorphisms demonstrated to increase risk of RLS are strongly linked to increased PLM as well, although some loci may have more effects on one versus the other phenotype.

    View details for PubMedID 25142570

  • Visualization of EEG activity for stimulating sleep research Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Moore, H. 2013
  • Nocturnal rapid eye movement sleep latency for identifying patients with narcolepsy/hypocretin deficiency. JAMA neurology Andlauer, O., Moore, H., Jouhier, L., Drake, C., Peppard, P. E., Han, F., Hong, S., Poli, F., Plazzi, G., O'Hara, R., Haffen, E., Roth, T., Young, T., Mignot, E. 2013; 70 (7): 891-902

    Abstract

    Narcolepsy, a disorder associated with HLA-DQB1*06:02 and caused by hypocretin (orexin) deficiency, is diagnosed using the Multiple Sleep Latency Test (MSLT) following nocturnal polysomnography (NPSG). In many patients, a short rapid eye movement sleep latency (REML) during the NPSG is also observed but not used diagnostically.To determine diagnostic accuracy and clinical utility of nocturnal REML measures in narcolepsy/hypocretin deficiency.Observational study using receiver operating characteristic curves for NPSG REML and MSLT findings (sleep studies performed between May 1976 and September 2011 at university medical centers in the United States, China, Korea, and Europe) to determine optimal diagnostic cutoffs for narcolepsy/hypocretin deficiency compared with different samples: controls, patients with other sleep disorders, patients with other hypersomnias, and patients with narcolepsy with normal hypocretin levels. Increasingly stringent comparisons were made. In a first comparison, 516 age- and sex-matched patients with narcolepsy/hypocretin deficiency were selected from 1749 patients and compared with 516 controls. In a second comparison, 749 successive patients undergoing sleep evaluation for any sleep disorders (low pretest probability for narcolepsy) were compared within groups by final diagnosis of narcolepsy/hypocretin deficiency. In the third comparison, 254 patients with a high pretest probability of having narcolepsy were compared within group by their final diagnosis. Finally, 118 patients with narcolepsy/hypocretin deficiency were compared with 118 age- and sex-matched patients with a diagnosis of narcolepsy but with normal hypocretin levels.Sensitivity and specificity of NPSG REML and MSLT as diagnostic tests for narcolepsy/hypocretin deficiency. This diagnosis was defined as narcolepsy associated with cataplexy plus HLA-DQB1*06:02 positivity (no cerebrospinal fluid hypocretin-1 results available) or narcolepsy with documented low (≤ 110 pg/mL) cerebrospinal fluid hypocretin-1 level.Short REML (≤15 minutes) during NPSG was highly specific (99.2% [95% CI, 98.5%-100.0%] of 516 and 99.6% [95% CI, 99.1%-100.0%] of 735) but not sensitive (50.6% [95% CI, 46.3%-54.9%] of 516 and 35.7% [95% CI, 10.6%-60.8%] of 14) for patients with narcolepsy/hypocretin deficiency vs population-based controls or all patients with sleep disorders undergoing a nocturnal sleep study (area under the curve, 0.799 [95% CI, 0.771-0.826] and 0.704 [95% CI, 0.524-0.907], respectively). In patients with central hypersomnia and thus a high pretest probability for narcolepsy, short REML remained highly specific (95.4% [95% CI, 90.4%-98.3%] of 132) and similarly sensitive (57.4% [95% CI, 48.1%-66.3%] of 122) for narcolepsy/hypocretin deficiency (area under the curve, 0.765 [95% CI, 0.707-0.831]). Positive predictive value in this high pretest probability sample was 92.1% (95% CI, 83.6%-97.0%).Among patients being evaluated for possible narcolepsy, short REML (≤15 minutes) at NPSG had high specificity and positive predictive value and may be considered diagnostic without the use of an MSLT; absence of short REML, however, requires a subsequent MSLT.

    View details for DOI 10.1001/jamaneurol.2013.1589

    View details for PubMedID 23649748

  • Predictors of Hypocretin (Orexin) Deficiency in Narcolepsy Without Cataplexy SLEEP Andlauer, O., Moore, H., Hong, S., Dauvilliers, Y., Kanbayashi, T., Nishino, S., Han, F., Silber, M. H., Rico, T., Einen, M., Kornum, B. R., Jennum, P., Knudsen, S., Nevsimalova, S., Poli, F., Plazzi, G., Mignot, E. 2012; 35 (9): 1247-1255

    Abstract

    To compare clinical, electrophysiologic, and biologic data in narcolepsy without cataplexy with low (? 110 pg/ml), intermediate (110-200 pg/ml), and normal (> 200 pg/ml) concentrations of cerebrospinal fluid (CSF) hypocretin-1.University-based sleep clinics and laboratories.Narcolepsy without cataplexy (n = 171) and control patients (n = 170), all with available CSF hypocretin-1.Retrospective comparison and receiver operating characteristics curve analysis. Patients were also recontacted to evaluate if they developed cataplexy by survival curve analysis.The optimal cutoff of CSF hypocretin-1 for narcolepsy without cataplexy diagnosis was 200 pg/ml rather than 110 pg/ml (sensitivity 33%, specificity 99%). Forty-one patients (24%), all HLA DQB1*06:02 positive, had low concentrations (? 110 pg/ml) of CSF hypocretin-1. Patients with low concentrations of hypocretin-1 only differed subjectively from other groups by a higher Epworth Sleepiness Scale score and more frequent sleep paralysis. Compared with patients with normal hypocretin-1 concentration (n = 117, 68%), those with low hypocretin-1 concentration had higher HLA DQB1*06:02 frequencies, were more frequently non-Caucasians (notably African Americans), with lower age of onset, and longer duration of illness. They also had more frequently short rapid-eye movement (REM) sleep latency (? 15 min) during polysomnography (64% versus 23%), and shorter sleep latencies (2.7 ± 0.3 versus 4.4 ± 0.2 min) and more sleep-onset REM periods (3.6 ± 0.1 versus 2.9 ± 0.1 min) during the Multiple Sleep Latency Test (MSLT). Patients with intermediate concentrations of CSF hypocretin-1 (n = 13, 8%) had intermediate HLA DQB1*06:02 and polysomnography results, suggesting heterogeneity. Of the 127 patients we were able to recontact, survival analysis showed that almost half (48%) with low concentration of CSF hypocretin-1 had developed typical cataplexy at 26 yr after onset, whereas only 2% had done so when CSF hypocretin-1 concentration was normal. Almost all patients (87%) still complained of daytime sleepiness independent of hypocretin status.Objective (HLA typing, MSLT, and sleep studies) more than subjective (sleepiness and sleep paralysis) features predicted low concentration of CSF hypocretin-1 in patients with narcolepsy without cataplexy.

    View details for DOI 10.5665/sleep.2080

    View details for Web of Science ID 000308360100012

    View details for PubMedID 22942503

  • Self-contained position tracking of human movement using small Inertial/Magnetic sensor modules PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10 Yun, X., Bachmann, E. R., Moore, H., Calusdian, J. 2007: 2526-2533

Presentations


  • High Resolution Detection of Polysomnography Based Phasic Events of REM Sleep in Posttraumatic Stress Disorder Steve Woodward, Emmanuel Mignot

    Two signal processing methods, wavelet denoising and adaptive filtering, improve performance of several previously published rapid eye movement detection algorithms. These methods are applied to nocturnal polysomnography (NPSG) based sleep studies of combat veterans who have been diagnosed with posttraumatic stress disorder (PTSD) and healthy combat veteran controls.

    Time Period

    June 2012

    Presented To

    Association for the Psychophysiological Study of Sleep

    Location

    Boston, Massachusetts, USA

    Collaborators

    For More Information:

Stanford Medicine Resources: