一种用于分析非快速眼动睡眠微观结构的通用自动方法。
A general automatic method for the analysis of NREM sleep microstructure.
作者信息
Barcaro Umberto, Bonanni Enrica, Maestri Michelangelo, Murri Luigi, Parrino Liborio, Terzano Mario Giovanni
机构信息
Istituto di Scienza e Tecnologie dell'Informazione, C.N.R., Area della Ricerca, Via Moruzzi 1, I-56124 Pisa, Italy.
出版信息
Sleep Med. 2004 Nov;5(6):567-76. doi: 10.1016/j.sleep.2004.07.012.
OBJECTIVE
To define a unified method for the automatic recognition and quantitative description of EEG phasic events of sleep microstructure occurring during NREM sleep, particularly arousals, phase A subtypes of cyclic alternating pattern and spindles.
METHODS
The NREM sleep EEG of 10 normal young subjects was examined in order to recognize formal phasic events of sleep microstructure. The following 'formal' events (i.e. events defined exclusively on the basis of automatic analysis criteria) were classified: arousals, A1-phases (A-phases not including arousals) and A2- and A3-phases (A-phases including arousals). Spindle bursts, corresponding to visually recognized spindles, were also formally defined. The identification of these events was carried out following a three-step procedure: (1) computation of band-related descriptors derived from the EEG signal, (2) introduction of suitable thresholds and (3) application of simple logical principles, i.e. an exclusion principle and an overlapping principle.
RESULTS
Formal A-phases, arousals and spindle bursts showed spectral characteristics which were consistent with visual inspection. The value of the parameter Correctness for the recognition of the A-phases was 83.5%. In particular, the different physiological distribution of the A-phases in Stage 2 preceding slow wave sleep with respect to Stage 2 preceding REM sleep was confirmed.
CONCLUSIONS
The proposed method provides a unified quantitative approach to the study of sleep microstructure. Visually defined events can be reliably identified by means of automatic recognition.
目的
定义一种统一的方法,用于自动识别和定量描述非快速眼动睡眠期间睡眠微结构的脑电图相位事件,特别是觉醒、周期性交替模式的A亚型和纺锤波。
方法
检查10名正常年轻受试者的非快速眼动睡眠脑电图,以识别睡眠微结构的正式相位事件。对以下“正式”事件(即仅根据自动分析标准定义的事件)进行分类:觉醒、A1期(不包括觉醒的A期)以及A2期和A3期(包括觉醒的A期)。还正式定义了与视觉识别的纺锤波相对应的纺锤波爆发。这些事件的识别按照三步程序进行:(1) 计算从脑电图信号导出的频段相关描述符;(2) 引入合适的阈值;(3) 应用简单的逻辑原则,即排除原则和重叠原则。
结果
正式的A期、觉醒和纺锤波爆发显示出与视觉检查一致的频谱特征。A期识别的正确性参数值为83.5%。特别是,证实了慢波睡眠前第2阶段的A期与快速眼动睡眠前第2阶段的A期在生理分布上的差异。
结论
所提出的方法为睡眠微结构的研究提供了一种统一的定量方法。通过自动识别可以可靠地识别视觉定义的事件。