兒童睡眠障礙中阻塞型睡眠呼吸疾病是最常見的疾病，兒童、青少年族群中盛行率約2%到5%間，而其臨床症狀並不明顯，多半是睡眠打鼾、張嘴呼吸等情形，尤其在台灣接近半數兒童會有過敏性鼻炎，父母往往會忽略睡眠呼吸中止的症狀，對亞洲孩童而言，由於基因緣故顱底空間生來較狹窄，相較於西方人種更容易出現嚴重的睡眠呼吸中止，若未獲得早期治療，會引發許多併發症如過動症、言語能力發展遲緩，學齡期的兒童更會出現社交障礙、情緒控制障礙、學習障礙及智力發展的問題，因此早期診斷與治療睡眠呼吸中止是近期逐漸受到重視的臨床問題，但在台灣睡眠治療中心的床位嚴重不足，醫學中心的「候位」時間往往長達半年，且並無兒童青少年專門的睡眠中心，因此對已接受治療或是需要已確診需觀察的兒童青少年睡眠呼吸中止症患者無法提供有效的策略長期追蹤。因此本計畫的目的希望能應用時頻分析方法於臨床睡眠檢查多重生理紀錄資料庫，尋找兒童睡眠呼吸中止症之可能替代指標，並將發展之生物指標能應用於微型化儀器或穿戴式裝置結合雲端平台，使睡眠呼吸中止症能早期檢測與追蹤，同時也將納入兒童時期易發與過度嗜睡相關疾病，最終協助醫療人員達到個人化的照護。 ;Obstructive sleep apnea (OSA)is the most common diagnosis for pediatric sleep disorders. OSA occurs in 2-5% of children and the clinical symptoms are often very subtle-snoring or mouth breathing. In Taiwan, the parents can mistake the symptom as rhinitis due to its high prevalence (over 50% of children) and delay the diagnosis of OSA. The narrow craniofacial structure of Asian children predisposes them to more severe apnea compared to the western children. If the pediatric OSA cannot diagnosis promptly, the children may suffer from many adverse health consequences such as Attention-Deficit/Hyperactivity Disorder or delayed speech or language development. For children at school age, OSA can lead to social disturbance, emotional or behavior disorders, and learning/cognitive problems. Early diagnosis of pediatric OSA gains more and more attention. However, the sleep center in Taiwan is already overwhelmed by adult sleep patients and each patient has to wait for around six months for the sleep test and the insufficient resource for pediatric patients make it difficult for the early diagnosis or treatment follow-up. The aims of this proposal are therefore to apply the time-frequency analysis on the pediatric polysomnography database recorded in sleep center and to probe the alternative biomarkers for pediatric sleep apnea. The surrogates developed from the database will be implemented into the miniaturized or wearable devices and integrated with the cloud system for early diagnosing and continuously following up for pediatric patients with OSA. Furthermore, the biomarkers for different disorders related to hypersomnolence will also be explored from the database. Finally, the data uploaded to the cloud system can be assessed by the clinical staff anywhere and anytime which helps the treatment of those patients or subjects can be tailored accordingly to accomplish personalized medicine.