The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Sleep algorithm
研究概览
简要总结
To evaluate whether it is able to perform sleep staging with EEG data recorded from 2 electrodes behind each ear.
详细描述
The Sensor Dot wearable device measures electroencephalography (EEG). It records from 2 electrodes behind each ear. The device was designed as a wearable for seizure detection in epilepsy patients. The purpose of this study is to test its ability to capture the information necessary for sleep monitoring in elderly patients. Trained electrophysiologists are unable to stage sleep on data from novel wearable devices, since AASM sleep scoring rules are only defined for standardized recording positions on the head. Therefore, we need an automated algorithm to perform sleep staging with data from the Sensor Dot device. We will train this algorithm using manual annotations made with the polysomnography simultaneously acquired with the wearable EEG.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 60 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Subjects planned to undergo a diagnostic polysomnography
- •> 60y old
排除标准
- •Patients unable to provide informed consent
研究组 & 干预措施
EEG evaluation
All patients will be evaluated during 1 night by standard polysomnography and additionally EEG will be evaluated by 2 electrodes behind each ear connected to a recording device (Sensor Dot)
干预措施: EEG behind the ear (Diagnostic Test)
结局指标
主要结局
Sleep algorithm
时间窗: 1 night
To develop an algorithm to characterize sleep architecture based on EEG measurement by 2 electrodes behind each ear. To classify the sleep stages, a deep learning algorithm will be used. The algorithm will learn a complex function, transforming an input to an output, based on several examples. In this specific case, the input are 30s EEG epochs and the output are sleep stages. To classify the measured signal in the correct sleep stage, the deep learning algorithm will learn to extract useful features from the data.
次要结局
未报告次要终点