Mariani Sara, Bianchi Anna M, Manfredini Elena, Rosso Valentina, Mendez Martin O, Parrino Liborio, Matteucci Matteo, Grassi Andrea, Cerutti Sergio, Terzano Mario G
Politecnico di Milano, Dept. of Biomedical Engineering, P.zza Leonardo da Vinci 32, 20133, Milan, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5085-8. doi: 10.1109/IEMBS.2010.5626211.
This study aimed to develop an automatic algorithm to detect the activation phases (A phases) of the Cyclic Alternating Pattern. The sleep EEG microstructure of 4 adult, healthy subjects was scored by a sleep medicine expert. Features were calculated from each of the six EEG bands (low delta, high delta, theta, alpha, sigma and beta), and three additional characteristics were computed: the Hjorth activity in the low delta and high delta bands, and the differential variance of the raw EEG signal. The correlation between couples of features was analyzed to find redundancies for the automatic analysis. The features were used to train an Artificial Neural Network to automatically find the A phases of CAP. The data were divided into training, validation and testing set, and the visual scoring provided by the clinician was used as the desired output. The statistics on the second by second classification show an average sensitivity equal to 76%, specificity equal to 83% and accuracy equal to 82%. The results obtained are encouraging, since an automatic classification of the A phases could benefit the practice in clinics, preventing the physician from the time-consuming activity of visually scoring the sleep microstructure over the whole eight-hour sleep recordings. Moreover, it would provide an objective criterion capable of overcoming the problems of inter-scorer variability.
本研究旨在开发一种自动算法以检测周期性交替模式的激活期（A期）。由一位睡眠医学专家对4名成年健康受试者的睡眠脑电图微观结构进行评分。从六个脑电图频段（低δ波、高δ波、θ波、α波、σ波和β波）中的每一个频段计算特征，并计算另外三个特征：低δ波和高δ波频段的 Hjorth 活动，以及原始脑电图信号的差分方差。分析特征对之间的相关性以找出自动分析中的冗余信息。使用这些特征训练人工神经网络以自动找出周期性交替模式的A期。数据被分为训练集、验证集和测试集，临床医生提供的视觉评分用作期望输出。逐秒分类的统计结果显示平均灵敏度为76%，特异性为83%，准确率为82%。所获得的结果令人鼓舞，因为A期的自动分类可能有益于临床实践，使医生无需在整个八小时睡眠记录上耗时地对睡眠微观结构进行视觉评分。此外，它将提供一个能够克服评分者间变异性问题的客观标准。
