Mendez M O, Matteucci M, Cerutti S, Aletti F, Bianchi A M
Politecnico di Milano, Milano, IT 20133 Italia.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:9-12. doi: 10.1109/IEMBS.2009.5332624.
An algorithm to evaluate the sleep macrostructure based on heart rate fluctuations from ECG signal is presented. This algorithm is an attempt to evaluate the sleep quality out of sleep centers. The algorithm is made up by a) a time-variant autoregressive model used as feature extractor and b) a hidden Markov model used as classifier. Characteristics coming from the joint probability of HRV features were used to fed the HMM. 17 full polysomnography recordings from healthy subjects were used in the current analysis. When compared to Wake-NREM-REM given by experts, the automatic classifier achieved a total accuracy of 78.21+/-6.44% and a kappa index of 0.41+/-.1085 using two features and a total accuracy of 79.43+/-8.83% and kappa index of 0.42+/-.1493 using three features.
提出了一种基于心电图信号心率波动评估睡眠宏观结构的算法。该算法旨在从睡眠中心之外评估睡眠质量。该算法由a）用作特征提取器的时变自回归模型和b）用作分类器的隐马尔可夫模型组成。来自心率变异性特征联合概率的特征用于输入隐马尔可夫模型。当前分析使用了17名健康受试者的完整多导睡眠图记录。与专家给出的清醒-非快速眼动-快速眼动状态相比，使用两个特征时，自动分类器的总准确率为78.21±6.44%，kappa指数为0.41±0.1085；使用三个特征时，总准确率为79.43±8.83%，kappa指数为0.42±0.1493。
