A Multi-Signal Based Monitoring System for CNS Hypersomnias

Chang Gung Medical Foundation

Status

Unknown

Conditions

Hypersomnia

Study type

Observational

Funder types

Other

Identifiers

NCT05443373

201902163A3

Details and patient eligibility

About

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.

Full 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:

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.
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.
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.
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.

Enrollment

600 estimated patients

Sex

All

Ages

9 to 45 years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

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