常用來診斷睡眠問題的第 1 級之 PSG 需要耗費數月的時間安排檢查並取得睡眠報告，此段時間未接受治療的空窗期，將延誤治療，而再度惡化。現在科技廠商開發支援睡眠檢測的智慧手錶雖然可在家自行使用且可快速檢測，但是由於測量的訊號種類比起第 1 和第 2 級的 PSG 少了許多，容易忽略非呼吸問題造成的睡眠障礙，例如腦波中的覺醒，這也同樣發生在第三級居家使用的呼吸偵測器上。儘管第 2 級睡眠篩檢涵蓋了腦波，但目前欠缺一個可以將診斷，治療、診斷及後續追蹤的生理訊號整合的系統，使臨床醫師和病患須自行整理報告才可追蹤。基於以上困境，我們開發了一套平台系統讓病患在家同時戴上我們各種測量不同訊號的居家睡眠篩檢裝置，並將其訊號、特徵自動傳送到平台伺服器上進行特徵運算及資料呈現。使用的居家睡眠篩檢及治療裝置包含了眼動波、腦波、活動量、睡眠階段、血氧、脈搏等數值，其測量種類數更接近 PSG，意味著其結果可以整合判斷來增加精準度。此平台的其他優勢在於不只整合多種測量更為精準之裝置的指標數值，也整合了同一類型之歷史指標至單一頁面，相較於其他系統，我們增加整合了治療用機器之數值，成功簡化了複雜的睡眠指標報告，也簡化了臨床醫師擷取歷史資料的流程。由於平台是以網路伺服器方式服務，我們有防範常見的程式碼注入、偽造請求、漏洞開採等攻擊，並進行自動化測試以確保計算特徵之功能正確運作。

Though the level I PSG is a common tool to diagnose sleep disorders due to its sensitivity and precision, it usually takes several months to get the PSG check outcome. The above time plus the time until treatment would be much longer, worsening the condition due to delayed treatment. In modern we have home sleep testing devices to perform sleep screening rapidly at home, however, those devices only measure limited types of signals, compared to PSG. This would miss out non-apnea sleep disorders like frequent sleep arousal only observable via EEG and EOG, and the Home Apnea Sleep Testing devices thus also suffers from this. Due to the predicament, we developed a web platform system to allow patients to simultaneously wear multiple HST devices at home to measure various kinds of signals. The measured data would be uploaded to the platform server for feature analysis and historical data display. The data includes but is not limited to EEG, EOG, movement, sleep stages, sleep efficiency indices, SaO2, and pulse. Compared to the brand smart watches and an HSAT, we measured the most closet number of signals to PSG at home, meaning that the reliability is improved. Another advantage of the platform is that it also provisioned the historical index values from different sleep screening devices, covering data from diagnosis to treatment stages, allowing observation on treatment efficiency for long term. It also simplified clinical III workflows and the tedious sleep index reports. Since our platform is served as web service, we protected our system against common web attacks such as malicious code injection, forged requests, security exploit, and automated tests to ensure the feature analysis function expectedly.

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