CAJ | 학술논문

目的：根据脑电信号的特征，提出基于条件概率的睡眠状态实时估计方法，为睡眠监测提供反映睡眠状态连续变化的客观评价依据。方法在白天短时睡眠过程中，同步采集了4导与睡眠相关的脑电信号（ C3-A2，C4-A1，O1-A2，O2-A1），对每5秒记录数据进行傅里叶变换，分别计算了8~13 Hz和2~7 Hz 的脑电节律能量占空比特征参数。主要方法包含了学习和测试两个阶段：在学习阶段，根据训练数据获得脑电特征参数的概率密度分布；在测试阶段，根据当前特征，得到各睡眠分期的条件概率，并计算获得睡眠状态的估计值。结果分析和测试了12名受试者的短时睡眠数据。通过与睡眠分期的人工判读结果相比较，睡眠状态估计值呈现了睡眠深度的连续变化。觉醒期的显著性差异为2.94，睡眠一期和二期分别为1.78和1.62，分析结果符合实际规律。结论本文所定义的睡眠状态估计值蕴含了睡眠分期的特征，较好地反映了睡眠阶段在持续和过渡期间的连续变化过程，能够为白天短时睡眠状态分析提供实时监测和分析的客观评价依据。
목적：근거뇌전신호적특정，제출기우조건개솔적수면상태실시고계방법，위수면감측제공반영수면상태련속변화적객관평개의거。방법재백천단시수면과정중，동보채집료4도여수면상관적뇌전신호（ C3-A2，C4-A1，O1-A2，O2-A1），대매5초기록수거진행부리협변환，분별계산료8~13 Hz화2~7 Hz 적뇌전절률능량점공비특정삼수。주요방법포함료학습화측시량개계단：재학습계단，근거훈련수거획득뇌전특정삼수적개솔밀도분포；재측시계단，근거당전특정，득도각수면분기적조건개솔，병계산획득수면상태적고계치。결과분석화측시료12명수시자적단시수면수거。통과여수면분기적인공판독결과상비교，수면상태고계치정현료수면심도적련속변화。각성기적현저성차이위2.94，수면일기화이기분별위1.78화1.62，분석결과부합실제규률。결론본문소정의적수면상태고계치온함료수면분기적특정，교호지반영료수면계단재지속화과도기간적련속변화과정，능구위백천단시수면상태분석제공실시감측화분석적객관평개의거。
Objective According to the characteristics of electroencephalograph( EEG),an automatic sleep level estimation method based on conditional probability is developed. The ultimate purpose is to obtain and realize the real-time sleep level evaluation. Methods There are 4 EEG channels(O2 -A1 ,O1 -A2 ,C4 -A1 , C3 -A2 )recorded during nap. For every 5-second data,two characteristic parameters of ratio of EEG rhythms (8-13 Hz,2-7 Hz)are calculated after fast Fourier transformation(FFT). The main method consists of two models:learning and testing. During the learning stage,the probability density functions of EEG parameters are obtained based on the training data. During the testing stage,the sleep level is estimated based on the conditional probability of sleep stages. Results The nap data of 12 subjects are tested. The significant difference is analyzed. Comparing with the visual inspection of sleep stage,the obtained sleep level reflects the continuous change within or between sleep stages. The significant difference of stage awake is 2. 94,stage 1 is 1. 78 and stage 2 is 1. 62,which fits to the regular patterns. Conclusions The defined sleep level is effective to observe the changes within the sleep stage and the transition process between the sleep stages. This method is usable for real-time nap and sleep level evaluation.