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临床试验/NCT03657329
NCT03657329已完成不适用

Performance of a Wireless Dry-EEG Device for Sleep Monitoring Compared to a Gold Standard Polysomnography in Patients With Suspected Sleep-Disordered Breathing

Dreem2 个研究点 分布在 1 个国家目标入组 67 人开始时间: 2018年5月7日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
已完成
发起方
入组人数
67
试验地点
2
主要终点
Apnea-Hypopnea Index (AHI) severity agreement

研究概览

简要总结

This study aims to evaluate the accuracy of apnea detection and automated sleep analysis by the Dreem dry-EEG headband and deep learning algorithm in comparison to the consensus of 5 sleep technologists' manual scoring of a gold-standard clinical polysomnogram (PSG) record in adults during a physician-referred overnight sleep study due to suspicion of sleep-disordered breathing.

详细描述

The study will enroll up to 70 adults who are referred to the Stanford Sleep Medicine Center by their physician for an overnight polysomnographic sleep study due to suspicion of sleep-disordered breathing, with the aim of collecting 60 usable data sets (i.e., eligible subjects with high-quality PSG and Dreem recordings). Upon arrival to the clinic, patients provide informed consent, are interviewed to determine eligibility, and complete a detailed demographic, medical, health, sleep, and lifestyle questionnaire (Alliance Sleep Questionnaire; ASQ). After the ASQ, participants are fitted with the PSG and the Dreem headband by the sleep technologist. During the PSG sleep study, the Dreem headband records EEG, pulse, oxygen saturation (SO2), movement, and respiratory rate. Many participants may undergo a split-night study with a continuous positive airway pressure (CPAP) device during their participation, as deemed necessary by the clinical staff pursuant to the sleep study.

The PSG data from the first 30 eligible participants will be manually scored by 5 sleep technologists. These manually-scored PSG data files (referred to as the training dataset) will be synchronized with Dreem data files from the same night and the synchronized files will be used to train Dreem's deep learning algorithms. Following training, the algorithms will be deployed to automatically score the final 30 participants' Dreem datasets (testing dataset). Finally, PSG records for the second 30 participants will be provided to the sponsor and manually scored by 5 sleep technologists. The manual scoring results will be compared to the Dreem automatic analysis to determine the accuracy of Dreem's apnea-hypopnea index (AHI) severity detection and sleep staging algorithms.

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
18 Years 至 70 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • 18-70 years of age
  • Capable of providing informed consent
  • Suspicion of sleep breathing disorder (both diagnostic and split-night studies)

排除标准

  • Concomitant diagnosis of a sleep disorder other than sleep apnea syndrome or insomnia
  • Morbid obesity (BMI > 39)
  • Use of benzodiazepines, nonbenzodiazepine (Z-drugs), or Gammahydroxybutyrate (GHB) the day/night of the study
  • Concomitant diagnosis of cardiopulmonary or neurological comorbidities (such as heart failure, COPD, neurodegenerative conditions)

研究组 & 干预措施

Suspicion of sleep-disordered breathing

Experimental

Dreem

干预措施: Dreem (Diagnostic Test)

结局指标

主要结局

Apnea-Hypopnea Index (AHI) severity agreement

时间窗: Day 1

AHI severity (normal \[\<5\], mild \[5-14\], moderate \[15-29\], severe \[\>29\]) as automatically determined by the Dreem headband compared to the AHI severity determined by the consensus of 5 sleep technologists' scoring of the subject's PSG record from the same night.

次要结局

  • Total Sleep Time (TST) agreement(Day 1)
  • EEG Virtual Channel signal quality agreement(Day 1)
  • Wake After Sleep Onset (WASO) time agreement(Day 1)
  • Time in N1 sleep stage agreement(Day 1)
  • Time in N2 sleep stage agreement(Day 1)
  • Time in N3 sleep stage agreement(Day 1)
  • Time in REM sleep stage agreement(Day 1)

研究者

发起方
Dreem
申办方类型
Industry
责任方
Sponsor

研究点 (2)

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