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The clinical study aims to develop and fine-tune the TipTraQ system, created by PranaQ, for home sleep apnea screening. The system comprises a wearable device and a cloud-based AI for estimating Total Sleep Time (TST) and Apnea-Hypopnea Index (AHI). This open-label, non-randomized study will recruit 240 subjects aged 20 and older from the Veteran General Hospital, Taipei.
Sleep Breathing Disorders (SBD) is a common disorder in the population. However, a lot of patients are not aware of this disease and do not receive proper treatment, which not only impacts their personal health but also causes public disasters. Thus, a screening tool to find those patients becomes a critical point-of-care issue. To achieve this goal, a wearable device that is suitable for the screening is needed. The goal of this study is developing and fine-tuning a new sleep screening platform, TipTraQ, developed by PranaQ. TipTraQ is composed of a wearable device designed as a convenient screening device based on the photoplethysmography (PPG) technique and an interpretable artificial intelligent (AI) system that takes the PPG signal recorded by the wearable device as input and outputs and outputs estimated total sleep time (TST) and apnea-hypopnea index (AHI).
The study is open-label and non-randomized, planning to recruit 240 subjects. Participants will use the TipTraQ device alongside a companion mobile app, with data analysis supported by AI technology hosted on a cloud server. The TipTraQ device will be worn over fingertip. The primary goal is to assess the robustness of the TipTraQ system and fine-tuned the algorithm by comparing its output against the gold standard PSG annotations made by sleep experts.
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| Measure | Description | Time Frame |
|---|---|---|
| Apnea Hypopnea Index(AHI) | the combined average number of apneas and hypopneas that occur per hour of sleep | From the start of a single polysomnography study to the end of the recording. Usually range from 4 to 10 hours |
| Oxygen Desaturation Index(ODI) | Average desaturation episodes with a decrease in the oxygen saturation of ≥4% or 3% per hour | From the start of a single polysomnography study to the end of the recording. Usually range from 4 to 10 hours |
| Total Sleep Time(TST) | Total sleep time during the polysomnography (PSG) | From the start of a single polysomnography study to the end of the recording. Usually range from 4 to 10 hours |
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Inclusion Criteria:
Subjects from the age of 20 and older that have an indication for an in-lab PSG study. The subject should be able to understand the study and sign the informed consent before being enrolled in the study. During the study period, the subject can withdraw from the study any time.
Exclusion Criteria:
Subjects with the following medical conditions will be excluded from this study:
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Health or people who has indication to conduct polysomnography (PSG).
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Taipei Veterans General Hospital | Taipei | Taipei | 201 | Taiwan |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39821673 | Derived | Chen KW, Tseng CH, Lee HC, Liu WT, Chou KT, Wu HT. Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis. Sleep. 2025 May 12;48(5):zsae317. doi: 10.1093/sleep/zsae317. |
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| ID | Term |
|---|---|
| D020181 | Sleep Apnea, Obstructive |
| ID | Term |
|---|---|
| D012891 | Sleep Apnea Syndromes |
| D001049 | Apnea |
| D012120 | Respiration Disorders |
| D012140 | Respiratory Tract Diseases |
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| D020919 |
| Sleep Disorders, Intrinsic |
| D020920 | Dyssomnias |
| D012893 | Sleep Wake Disorders |
| D009422 | Nervous System Diseases |