Chen Ke-Wei, Tseng Chun-Hsien, Lee Hsin-Chien, Liu Wen-Te, Chou Kun-Ta, Wu Hau-Tieng
PranaQ Pte. Ltd., Singapore.
Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Sleep. 2025 May 12;48(5). doi: 10.1093/sleep/zsae317.
This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilizes photoplethysmography (red, infrared, and green channels) and accelerometer sensors to assess sleep apnea by the AI system.
We prospectively enrolled 240 participants suspected of obstructive sleep apnea (OSA) at a tertiary medical center for internal validation and 112 participants independently at another center for external validation. All participants underwent simultaneous polysomnography and TipTraQ HSAT. We compared TipTraQ-derived total sleep time (TQ-TST) and TipTraQ-derived Respiratory Events Index (TQ-REI) with expert-determined TST and apnea-hypopnea index (AHI), based on AASM standards with the 1B hypopnea rule. Temporal event localization analysis for respiratory event prediction was conducted at both event and hourly levels.
In the external validation, the Spearman correlation coefficients for TQ-TST vs. TST and TQ-REI vs. AHI were 0.81 and 0.95, respectively. The root mean square error were 0.53 hours for TQ-TST vs. TST and 7.53 events/h for TQ-REI vs. AHI. For apnea/hypopnea prediction with a 10-second grace period, the true positive, false positive, and false negative rates in temporal event localization analysis were 0.76, 0.24, and 0.23, respectively. The four-way OSA severity classification achieved a Cohen's kappa of 0.7.
TQ-TST and TQ-REI predict TST and AHI with comparable performance to existing devices of the same type, and respiratory event prediction is validated through temporal event localization analysis.
Development of TipTraQ Home Sleep Test, https://clinicaltrials.gov/study/NCT06474351?term=NCT06474351&rank=1, NCT06474351TipTraQ Home Sleep Test Study, SHH, https://clinicaltrials.gov/study/NCT06633887?term=NCT06633887&rank=1, NCT06633887.
本文验证了TipTraQ,一种紧凑型家庭睡眠呼吸暂停测试(HSAT)系统。TipTraQ包括一个戴在指尖的设备、一个移动应用程序和一个基于云的深度学习人工智能(AI)系统。该设备利用光电容积脉搏波描记法(红色、红外线和绿色通道)和加速度计传感器,通过AI系统评估睡眠呼吸暂停。
我们前瞻性地招募了240名在三级医疗中心疑似阻塞性睡眠呼吸暂停(OSA)的参与者进行内部验证,并在另一个中心独立招募了112名参与者进行外部验证。所有参与者同时接受多导睡眠监测和TipTraQ HSAT。我们根据AASM标准和1B型呼吸暂停规则,将TipTraQ得出的总睡眠时间(TQ-TST)和TipTraQ得出的呼吸事件指数(TQ-REI)与专家确定的TST和呼吸暂停低通气指数(AHI)进行比较。在事件和小时级别进行呼吸事件预测的时间事件定位分析。
在外部验证中,TQ-TST与TST以及TQ-REI与AHI的Spearman相关系数分别为0.81和0.95。TQ-TST与TST的均方根误差为0.53小时,TQ-REI与AHI的均方根误差为7.53次事件/小时。对于有10秒宽限期的呼吸暂停/低通气预测,时间事件定位分析中的真阳性、假阳性和假阴性率分别为0.76、0.24和0.23。四向OSA严重程度分类的Cohen's kappa为0.7。
TQ-TST和TQ-REI预测TST和AHI的性能与同类现有设备相当,并且通过时间事件定位分析验证了呼吸事件预测。