Sleep Level Evaluation by Feature Fusion and ARMA for Nap

According to the characteristic of nap,this work proposes a sleep level estimation method based on ARMA model for analyzing the sleep status varying in nap.By using the sleep data during day nap,3 relevant parameters are calculated from Electroencephalogram(EEG),which are further fused into one parameter via the conditional probability for describing different sleep levels.And then,Auto Regressive and Moving Average (ARMA) model is adopted to analyze the sleep tendency.Finally,Support Vector Machine(SVM) is utilized to classify the sleep progress automatically.Compared with the visual inspection,the proposed estimation method can raise the sleep level recognition up to the average 88.7% of all 7 subjects.On one hand,feature fusion can improve the calculation speed significantly and provide an effective method for real-time sleep level detection.On the other hand,the prediction feature of ARMA model can be utilized to analyze the sleep tendency and provide an objective evaluation for further adjusting and controlling the sleep duration.