本論文主旨在於開發自動睡眠階層判讀及睡眠效率估測之演算法，演算法利用心電及活動訊號進行訊號前處理、雜訊濾除後，從時域、頻域及非線性分析三方面產生多達30個特徵值，為消除每人之生理訊號特徵值單位的不同，在進入辨識分類器前進行正規化處理，之後再以循序向前選擇法搜尋重要之特徵值以當作辨識分類器之輸入參數，本論文中所使用的辨識分類器有最小平方支持向量機(基於多類分類之一對一方法和決策樹式方法)以及機率類神經網路分類器，應用在正常人及睡眠呼吸中止症族群上，首先，在睡眠階層的判讀方面與多重睡眠生理記錄儀的比較下，我們獲得平均81.31%(正常人)和75.32%(患者)的準確率；其次，在睡眠效率估測方面，我們在正常人方面僅有4.90%的誤差，而在患者方面有15.03%的誤差。結果顯示，此系統適合結合心電及活動訊號於居家環境下作使用，免除在醫院所作的多重睡眠生理記錄儀檢查之不適感與高成本。

This thesis proposes an algorithm for sleep stage classification and sleep efficiency estimation using reduced features of ECG and activity signals. Initially, signals were preprocessed in order to remove artifacts from the ECG and activity signals. Secondly, 30 features were generated from the time-frequency domain and nonlinear analysis. Subsequently, the Z-score method was utilized to normalize all features in order to minimize the effect of differences in the ranges of values among different parameters. This study presents some tools for classifying the sleep stages of healthy and obstructive sleep apnea (OSA) subjects consisting of a least squares support vector machine (One-against-one method and Decision-tree based SVM) and a probabilistic neural network classifier. Comparing the resulting estimates with actual polysomnography data, average accuracies of 81.31% for healthy subjects and 75.32% for OSA patients were reported. In terms of sleep efficiency, the results demonstrate a bias of 4.9% and 15.03% error on sleep efficiency compared with PSG for healthy and OSA participants, respectively. The proposed strategy, being successfully validated by experimental data, may serve as a reasonably accurate tool for conveniently assessing sleep quality for long term monitoring in home-based environments.

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