Sleep · 2025
Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis.
СтатьяНаблюдательное исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chen Ke-Wei, Tseng Chun-Hsien, Lee Hsin-Chien, Liu Wen-Te, Chou Kun-Ta, Wu Hau-Tieng
Аннотация
STUDY OBJECTIVES: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
CLINICAL TRIALS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
CLINICAL TRIALS: 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.