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Status unknownNCT05443373Updated Jul 5, 2022

A Multi-Signal Based Monitoring System for CNS Hypersomnias

An observational study in Hypersomnia, sponsored by Chang Gung Memorial Hospital. Status unknown at 2 sites in Taiwan. Open to participants aged 9 Years to 45 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2022-07-05.

Sponsored by Chang Gung Memorial Hospital · Observational

The sponsor has not verified this record recently (last verified May 2022), so the status shown — last known as Recruiting — may be out of date.
Study type
Observational
Model
Cohort
Time perspective
Cross-sectional
Enrollment
600
Ages
9 Years to 45 Years
Sex
All
01

Study summary

This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected.The purposes of this study are as follows:(1) The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms of these CNS hypersomnias.(2) Use the Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to further screen out these clinically significant biomarkers for CNS hypersomnias, and to find ideal and accurate physiological biomarkers that can monitor the course of the disease.(3) Utilize these precisely monitored biomarkers to track changes in the biomarkers and the long-term course of these CNS hypersomnias, and evaluate the treatment effect and prognosis.(4) Use computer machine learning and other algorithms to analyze and construct a variety of faster and more accurate prediction models for these CNS hypersomnias, thereby achieving the goal of preventive medicine.

Read the detailed description

Excessive daytime sleepiness (EDS) is a common symptom in the general population. The prevalence ranges from 5% to 30%. And daytime drowsiness often brings negative effects, and even the daily function and the quality of life is impaired due to these hypersomnias. In some severe cases, many accidents can occur and endanger life. The current third edition of the International Classification of Sleep Disorders (ICSD 3) specifically classified "Central nervous system disorders of hypersomnolence" as Narcolepsy type 1 and type 2 ; idiopathic hypersomnia(IH), and Kleine-Levin syndrome (KLS). However, so far, except for Narcolepsy type 1, which has a relatively clear pathological mechanism that is related to the reduced secretion of hypocretin, other hypersomnia disorders such as Narcolepsy type 2, IH and KLS, that is no clear neurophysiological diagnosis standard, and the mechanism of these diseases is still not clear. Therefore, the diagnosis can only rely on the clinical symptoms and the clinical experience physicians. That is why the diagnosis of these diseases still has great difficulties and challenges. Therefore, in order to make the diagnosis more accurate, the investigators have to find out the "Biologic and neurophysiologic biomarkers" for these diseases. And let patients receive the correct treatment quickly.

The purposes of this study are as follows:

  1. The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms of these CNS hypersomnias.
  2. Use the Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to further screen out these clinically significant biomarkers for CNS hypersomnias, and to find ideal and accurate physiological biomarkers that can monitor the course of the disease.
  3. Utilize these precisely monitored biomarkers to track changes in the biomarkers and the long-term course of these CNS hypersomnias, and evaluate the treatment effect and prognosis.
  4. Use computer machine learning and other algorithms to analyze and construct a variety of faster and more accurate prediction models for these CNS hypersomnias, thereby achieving the goal of preventive medicine.

Research method:

This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected. These subjects will be divided into the five groups: (1) experimental group (narcolepsy Type 1, 300 subjects); (2) experimental group (narcolepsy Type 2, 100 subjects); and (3) experimental group (KLS, 100 subjects); and (4) experimental group (IH,50 subjects); and (5) healthy control group (age and gender matched healthy subjects,50 subjects). The investigators will collect all the clinical data for each subject, including clinical characteristics, sleep examination data, actigraphy, HLA typing, and brain imaging data.

Data analysis method:

Use multiple physiological signals to generate real-time quantitative algorithms and find physiological biomarkers related to hypersomnias. Use the aforementioned data were categorized and grouped through data analysis based on computer machine learning, neural network, and other algorithms. Then the investigators will build a predictive model based on the results and write a medical report and publish it.

02

Conditions studied

  • Hypersomnia

Keywords

  • Sleep
03

In context

Disorders of Excessive Somnolence

173 studies on the registry are indexed under Disorders of Excessive Somnolence; 34 are open to participants now.

This study's planned enrollment of 600 is above the median of 110 across 32 observational studies indexed under Disorders of Excessive Somnolence.

Browse Disorders of Excessive Somnolence studies →

Lead sponsor

Chang Gung Memorial Hospital is the lead sponsor of 1,064 studies on the registry; 235 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
9 Years to 45 Years
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Probability sample

Study population

9-45 years old subjects with narcolepsy , Kleine-Levin syndrome or Idiopathic Hypersomnia

Inclusion criteria

  1. Patients with narcolepsy , Kleine-Levin syndrome(KLS) or Idiopathic Hypersomnia (IH) diagnosed by a physician who meet the ICSD-3 diagnostic criteria
  2. Age: 9-45 years old
  3. Those who agree to participate in this research and can sign the consent form.

Exclusion criteria

Exclusion Criteria:

  1. Patients with epilepsy, head trauma and severe organic brain disease.
  2. Patients with severe Obstructive Sleep Apnea (OSA) and severe Periodic Limb Movement Disorder (PLMD) who have not received treatment.
  3. People with narcolepsy due to other physical and brain diseases.
  4. Those who cannot cooperate with the brain imaging examination and neurocognitive function test.
  5. Exclude those who have had brain surgery for brain tumor hemangioma, or those who have cerebral blood vessel metal clips.
  6. Exclude current pacemakers.
  7. Excluded those who had implanted artificial heart metal valve.
  8. Those who underwent surgery within the last 3 months were excluded.
  9. rule out claustrophobia
  10. Those who are unwilling to participate in this research or are unwilling to fill in the consent form.
05

Study design

Observational model
Cohort
Time perspective
Cross-sectional
Enrollment
600 participants (estimated)
Patient registry
No

Groups and cohorts

  • experimental group (narcolepsy Type 1)

    experimental group (narcolepsy Type 1, 300 subjects)

  • experimental group (narcolepsy Type 2)

    experimental group (narcolepsy Type 2, 100 subjects)

  • experimental group (KLS)

    experimental group (KLS, 100 subjects)

  • experimental group (IH)

    experimental group (IH,50 subjects)

  • healthy control group

    healthy control group (age and gender matched healthy subjects,50 subjects)

06

What researchers measure

Primary outcomes

  1. Polysomnography (PSG)

    Change in sleep latency (SL, mins) based on PSG during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  2. Multiple sleep latency test (MSLT)

    Change in Change in sleep latency (SL, mins) based on MSLT during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  3. HLA TYPING

    The investigators will use sequence-specific primer - polymerase chain reaction (SSP-PCR) to detect HLA-DQB1 and reverse sequence-specific oligonucleotide probes (SSOPs) to detect HLA-DQA1,and also use Sequencing Based Typing (SBT) and reverse sequence specific oligonucleotide (rSSO) to detect HLA-DRB and HLA-DQB in the lab.

    Time frame: baseline

  4. Actigraphy

    Change in sleep latency (mins) based on actigraphy during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  5. PET/MRI

    Positron Emission Tomography is a fusion of PET and MRI imaging techniques that can show the spread of diseased cells in soft tissue. The PET/MRI system can scan various parts of the patient and collect PET and MRI images separately for early diagnosis.

    Time frame: through study completion, an average of 1 year

Secondary outcomes

  1. Conners' Continuous Performance Test (CPT)

    The Conners Continuous Performance Test is a computer administered test that is designed to assess problems with attention.Many statistics are computed including omission errors , commission errors, hit reaction time, hit reaction time standard error, detectability, response style, perseverations , hit reaction time by block, standard error by block, reaction time by ISI , and standard error by ISI. These statistics are converted to T-scores and can be interpreted in terms of various aspects of attention including inattention, impulsivity, and vigilance.Higher rates of correct detections indicate better attentional capacity.

    Time frame: Once a year until the study is completed (up to 3 years)

  2. Wisconsin Card Sorting Test (WCST)

    The Wisconsin Card Sorting Test (WCST) is a neuropsychological test that is frequently used to measure such higher-level cognitive processes as attention, perseverance,working memory, abstract thinking and set shifting.

    Time frame: Once a year until the study is completed (up to 3 years)

  3. Epworth Sleepoiness Scale (ESS)

    Epworth Sleepoiness Scale (ESS) assesses the responder's propensity to doze or fall asleep during 8 common daily activities, such as: sitting and reading; sitting inactive in a public place; sitting and talking to someone; sitting quietly after a lunch without alcohol; or in a car, while stopped for a few minutes in traffic. An ESS score \>10 suggests excessive daytime sleepiness (EDS); ESS score ≥16 suggests a high level of EDS.

    Time frame: Once a year until the study is completed (up to 3 years)

  4. Pediatric Daytime Sleepiness Scale (PDSS)

    The pediatric daytime sleepiness questionnaire is a 5 points Likert scale (0-4) for 8 questions concerning to sleepiness. Scores ranged from 0 to 32.Higher scores on PDSS were associated with reduced total sleep time, poorer school achievement, poorer anger control, and frequent illness.

    Time frame: Once a year until the study is completed (up to 3 years)

  5. Short Form-36 (SF-36)

    36-Item Short-Form Health Survey (SF-36) includes 11 major questions that evaluate eight components (0-100), with higher scores indicating better outcome.These components include physical functioning, role limitations due to physical health, role limitations due to emotional problems, energy/fatigue, emotional wellbeing, social functioning, pain, and general health.

    Time frame: Once a year until the study is completed (up to 3 years)

  6. Polysomnography (PSG)-SE

    Change in sleep efficiency (SE, %)based on PSG during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  7. Polysomnography (PSG)-TST

    Change in total sleep time (TST, mins) based on PSG during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  8. Polysomnography (PSG)-WASO

    Change in slow wave sleep (SWS, %) based on PSG during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  9. Polysomnography (PSG)-REM

    Change in REM sleep (%) based on PSG during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  10. Polysomnography (PSG)-SWS

    Change in slow wave sleep (SWS, %) based on PSG during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  11. Actigraphy-TST

    Total sleep time (TST, mins) based on actigraphy during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  12. Actigraphy-SE

    Sleep efficiency (SE, %) based on actigraphy during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

  13. Actigraphy-WASO

    Wake after sleep onset (WASO) based on actigraphy during the study.

    Time frame: Once a year until the study is completed (up to 3 years)

07

Study locations

2 of 2 sites recruiting
  • Chang Gung Memorial Hospital, Linkou
    Taoyuan City, 333423, Taiwan
    Recruiting
  • Chang Gung Memorial Hospital
    Taoyuan, 333423, Taiwan
    Recruiting
08

References and documents

Publications

  • Abe K. Lithium prophylaxis of periodic hypersomnia. Br J Psychiatry. 1977 Mar;130:312-3. doi: 10.1192/bjp.130.3.312. No abstract available. PubMed 843779 ↗
  • Anderson KN, Pilsworth S, Sharples LD, Smith IE, Shneerson JM. Idiopathic hypersomnia: a study of 77 cases. Sleep. 2007 Oct;30(10):1274-81. doi: 10.1093/sleep/30.10.1274. PubMed 17969461 ↗
  • Arnulf I, Rico TJ, Mignot E. Diagnosis, disease course, and management of patients with Kleine-Levin syndrome. Lancet Neurol. 2012 Oct;11(10):918-28. doi: 10.1016/S1474-4422(12)70187-4. PubMed 22995695 ↗
  • Ali M, Auger RR, Slocumb NL, Morgenthaler TI. Idiopathic hypersomnia: clinical features and response to treatment. J Clin Sleep Med. 2009 Dec 15;5(6):562-8. PubMed 20465024 ↗
  • Bassetti C, Aldrich MS. Idiopathic hypersomnia. A series of 42 patients. Brain. 1997 Aug;120 ( Pt 8):1423-35. doi: 10.1093/brain/120.8.1423. PubMed 9278632 ↗
  • Brankack J, Kukushka VI, Vyssotski AL, Draguhn A. EEG gamma frequency and sleep-wake scoring in mice: comparing two types of supervised classifiers. Brain Res. 2010 Mar 31;1322:59-71. doi: 10.1016/j.brainres.2010.01.069. Epub 2010 Feb 1. PubMed 20123089 ↗
  • CRITCHLEY M. Periodic hypersomnia and megaphagia in adolescent males. Brain. 1962 Dec;85:627-56. doi: 10.1093/brain/85.4.627. No abstract available. PubMed 14023898 ↗
  • Engstrom M, Hallbook T, Szakacs A, Karlsson T, Landtblom AM. Functional magnetic resonance imaging in narcolepsy and the kleine-levin syndrome. Front Neurol. 2014 Jun 25;5:105. doi: 10.3389/fneur.2014.00105. eCollection 2014. PubMed 25009530 ↗
  • Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed. 2012 Oct;108(1):10-9. doi: 10.1016/j.cmpb.2011.11.005. Epub 2011 Dec 16. PubMed 22178068 ↗
  • Frenette E, Kushida CA. Primary hypersomnias of central origin. Semin Neurol. 2009 Sep;29(4):354-67. doi: 10.1055/s-0029-1237114. Epub 2009 Sep 9. PubMed 19742411 ↗
  • Grimaldi D, Pierangeli G, Barletta G, Terlizzi R, Plazzi G, Cevoli S, Franceschini C, Montagna P, Cortelli P. Spectral analysis of heart rate variability reveals an enhanced sympathetic activity in narcolepsy with cataplexy. Clin Neurophysiol. 2010 Jul;121(7):1142-7. doi: 10.1016/j.clinph.2010.01.028. Epub 2010 Feb 23. PubMed 20181520 ↗
  • Grosse-Wentrup M, Liefhold C, Gramann K, Buss M. Beamforming in noninvasive brain-computer interfaces. IEEE Trans Biomed Eng. 2009 Apr;56(4):1209-19. doi: 10.1109/TBME.2008.2009768. PubMed 19423426 ↗
  • Guilleminault C, Lopes MC, Hagen CC, da Rosa A. The cyclic alternating pattern demonstrates increased sleep instability and correlates with fatigue and sleepiness in adults with upper airway resistance syndrome. Sleep. 2007 May;30(5):641-7. doi: 10.1093/sleep/30.5.641. PubMed 17552380 ↗
  • Hadjiyannakis K, Ogilvie RD, Alloway CE, Shapiro C. FFT analysis of EEG during stage 2-to-REM transitions in narcoleptic patients and normal sleepers. Electroencephalogr Clin Neurophysiol. 1997 Nov;103(5):543-53. doi: 10.1016/s0013-4694(97)00064-3. PubMed 9402885 ↗
  • Jaussent I, Morin CM, Ivers H, Dauvilliers Y. Incidence, worsening and risk factors of daytime sleepiness in a population-based 5-year longitudinal study. Sci Rep. 2017 May 2;7(1):1372. doi: 10.1038/s41598-017-01547-0. PubMed 28465612 ↗
  • Kanbayashi T, Kodama T, Kondo H, Satoh S, Inoue Y, Chiba S, Shimizu T, Nishino S. CSF histamine contents in narcolepsy, idiopathic hypersomnia and obstructive sleep apnea syndrome. Sleep. 2009 Feb;32(2):181-7. doi: 10.1093/sleep/32.2.181. PubMed 19238805 ↗
  • Pike M, Stores G. Kleine-Levin syndrome: a cause of diagnostic confusion. Arch Dis Child. 1994 Oct;71(4):355-7. doi: 10.1136/adc.71.4.355. PubMed 7979534 ↗

Individual participant data

Plan to share: Undecided

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Jul 5, 2022, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT05443373
Lead sponsor
Chang Gung Memorial Hospital
Responsible party
Sponsor
First posted
Jul 5, 2022
Start date
Jun 4, 2020
Primary completion
Jul 31, 2023 (estimated)
Completion
Jul 31, 2023 (estimated)
Last update
Jul 5, 2022

Study contacts

Yu-Shu Huang, PhD
Contact
yushuhuang1212@gmail.com
+886 3 3281200 ext. 3836
Yu-Shu Huang, PhD
study director · Principal Investigator

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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