Evaluation of the Ear-EEG System for Sleep Monitoring in Healthy Subjects
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 20
- 试验地点
- 2
- 主要终点
- Cohens kappa
研究概览
简要总结
Subjects sleep multiple nights in their own home, wearing actigraph, PSG (PolySomnoGraphy) and ear-EEG sensors. The object of the study is to determine the applicability of ear-EEG for sleep monitoring.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Single Group
- 主要目的
- Device Feasibility
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 50 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Informed consent obtained and letter of authority signed before any study related activities
- •Age 18-50 years
排除标准
- •BMI (body mass index) > 30
- •Previous stroke or cerebral haemorrhage and any other structural cerebral disease
- •Known or suspected abuse of alcohol or any other neuro-active substance
- •Use of hearing aid or cochlear implants
- •Allergic contact dermatitis caused by metals or generally prone to skin irritation
- •Narrow or malformed ear canals
- •Obstructive sleep apnea
- •History of sleep disorders or neurological diseases
- •Chronic pain
- •People judged incapable, by the investigator, of understanding the participant instruction or who are not capable of carrying through the investigation.
- •Use of medication known to influence the user's sleep (antidepressants, sedatives, antipsychotic-, and pain relieving medication)
- •Teeth grinding (bruxism)
结局指标
主要结局
Cohens kappa
时间窗: At study completion (average of 6 months)
The test outcome is a set of matched polysomnography and ear-EEG sleep measurements. From this will be generated an algorithm for automatic sleep scoring based on ear-EEG (using leave-one-subject-out cross validation). The primary outcome measure of the test is the correlation between the automatically generated hypnograms and those generated manually from the scalp recordings. The accuracy is quantified using Cohen's kappa, which is a number between -1 and 1. An average (across all recordings) above 0.4 would be a success for the test. As the training of the sleep scoring algorithm requires large amounts of data, it is necessary to use a large number of subjects (20) to estimate the viability of automatic sleep scoring from ear-EEG recordings. This also means that kappa values are calculated for all recordings at once when the measurements are done. This method for creating sleep scoring algorithms and quantifying their success is in line with standard procedure in this field.
次要结局
未报告次要终点