Mendez Martin Oswaldo, Chouvarda Ioanna, Alba Alfonso, Bianchi Anna Maria, Grassi Andrea, Arce-Santana Edgar, Milioli Guilia, Terzano Mario Giovanni, Parrino Liborio
Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Lateral Av. Salvador Nava s/n, 78290, San Luis Potosí (SLP), Mexico.
Lab of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Med Biol Eng Comput. 2016 Jan;54(1):133-48. doi: 10.1007/s11517-015-1349-9. Epub 2015 Aug 8.
An analysis of the EEG signal during the B-phase and A-phases transitions of the cyclic alternating pattern (CAP) during sleep is presented. CAP is a sleep phenomenon composed by consecutive sequences of A-phases (each A-phase could belong to a possible group A1, A2 or A3) observed during the non-REM sleep. Each A-phase is separated by a B-phase which has the basal frequency of the EEG during a specific sleep stage. The patterns formed by these sequences reflect the sleep instability and consequently help to understand the sleep process. Ten recordings from healthy good sleepers were included in this study. The current study investigates complexity, statistical and frequency signal properties of electroencephalography (EEG) recordings at the transitions: B-phase--A-phase. In addition, classification between the onset-offset of the A-phases and B-phase was carried out with a kNN classifier. The results showed that EEG signal presents significant differences (p < 0.05) between A-phases and B-phase for the standard deviation, energy, sample entropy, Tsallis entropy and frequency band indices. The A-phase onset showed values of energy three times higher than B-phase at all the sleep stages. The statistical analysis of variance shows that more than 80% of the A-phase onset and offset is significantly different from the B-phase. The classification performance between onset or offset of A-phases and background showed classification values over 80% for specificity and accuracy and 70% for sensitivity. Only during the A3-phase, the classification was lower. The results suggest that neural assembles that generate the basal EEG oscillations during sleep present an over-imposed coordination for a few seconds due to the A-phases. The main characteristics for automatic separation between the onset-offset A-phase and the B-phase are the energy at the different frequency bands.
本文对睡眠期间周期性交替模式（CAP）的B相和A相转换过程中的脑电图（EEG）信号进行了分析。CAP是一种睡眠现象，由非快速眼动睡眠期间观察到的连续A相序列（每个A相可能属于A1、A2或A3组）组成。每个A相由一个B相分隔，B相具有特定睡眠阶段EEG的基础频率。这些序列形成的模式反映了睡眠不稳定性，从而有助于理解睡眠过程。本研究纳入了10名健康睡眠良好者的记录。当前研究调查了脑电图（EEG）记录在B相-A相转换时的复杂性、统计和频率信号特性。此外，使用kNN分类器对A相和B相的起始-结束进行了分类。结果表明，EEG信号在A相和B相之间的标准差、能量、样本熵、Tsallis熵和频带指数存在显著差异（p<0.05）。在所有睡眠阶段，A相起始时的能量值比B相高两倍。方差统计分析表明，超过80%的A相起始和结束与B相有显著差异。A相起始或结束与背景之间的分类性能显示，特异性和准确性的分类值超过80%，敏感性的分类值为70%。仅在A3期，分类较低。结果表明，睡眠期间产生基础EEG振荡的神经集合由于A相而在几秒钟内呈现出过度的协调。A相起始-结束与B相自动分离的主要特征是不同频带的能量。
