Validation Study of an Artificial Intelligence-based Sleep Stage Classification for a Home Sleep Tracking Device
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 305
- 试验地点
- 1
- 主要终点
- Sleep Stages Classification Accuracy
研究概览
简要总结
In this study, a two-part recursive convolutional neural networks model was developed, extracting features for each epoch window independently from before and after sleep onset (epoch encoder), and then trained in the context of long-term relationships in the sleep process (sequence encoder), using an approach similar to human expert classification based on information from single-channel forehead EEG and PPG (IR, Green, Red). The classification is based on guidelines from the American Academy of Sleep Medicine and calculated six parameters: total sleep duration (TST), wake (W), N1, N2, N3, and REM.
The validation study of the developed model and the device was conducted at the Sleep Disorders Centre of the Istanbul Medical Faculty using concurrent polysomnographic data from 305 male and female patients aged 18 to 65 years.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Participants suffering from sleep disorders
- •Participants sent to PSG test by neurologists, pulmonologists, psychiatrists, and otolaryngologists
排除标准
- •Anyone who has been diagnosed as having a contagious skin disease
- •Participants who do not have consent to have an additional device in their forehead area
- •Incomplete of sleep measurement
结局指标
主要结局
Sleep Stages Classification Accuracy
时间窗: 4-5 months
The collected EEG data were classified according to Cohen's kappa (\>85), which is considered successful in the literature. Initially the open source codes YASA, tinysleepnet and attentionsleep have been implemented. These codes yielded kappa 0.64, accuracy 0.80, kappa 0.69, accuracy 0.79 and kappa 0.65, accuracy 0.78 respectively. The values obtained do not correspond to those reported in the classification articles. Subsequently, 29 participants from our own dataset were tested in these classifications as a preliminary test, with poor results. On an individual basis, the highest cappa score was 0.51. Development of our own classification system is in progress.
Interoception analysis from PPG data collected from facial skin
时间窗: 4-5 months
According to our preliminary analyses, we found that the intermediary rhythm (0.12-0.18 Hz) associated with interoception is also present in sleep patients. In one participant, for example, a value of 0.19 was obtained as a ratio of total sleep time. In addition, an intermediary rhythm is observed in all stages of sleep, including wakefulness, light sleep, deep sleep and REM.
次要结局
未报告次要终点