School of Mathematics, Physics and Computing, University of Southern Queensland, Darling Heights, Australia.
School of Engineering, University of Southern Queensland, Toowoomba, Australia.
Sleep. 2024 Jan 11;47(1). doi: 10.1093/sleep/zsad159.
Sleep spindles are isolated transient surges of oscillatory neural activity present during sleep stages 2 and 3 in the nonrapid eye movement (NREM). They can indicate the mechanisms of memory consolidation and plasticity in the brain. Spindles can be identified across cortical areas and classified as either slow or fast. There are spindle transients across different frequencies and power, yet most of their functions remain a mystery. Using several electroencephalogram (EEG) databases, this study presents a new method, called the "spindles across multiple channels" (SAMC) method, for identifying and categorizing sleep spindles in EEGs during the NREM sleep. The SAMC method uses a multitapers and convolution (MT&C) approach to extract the spectral estimation of different frequencies present in sleep EEGs and graphically identify spindles across multiple channels. The characteristics of spindles, such as duration, power, and event areas, are also extracted by the SAMC method. Comparison with other state-of-the-art spindle identification methods demonstrated the superiority of the proposed method with an agreement rate, average positive predictive value, and sensitivity of over 90% for spindle classification across the three databases used in this paper. The computing cost was found to be, on average, 0.004 seconds per epoch. The proposed method can potentially improve the understanding of the behavior of spindles across the scalp and accurately identify and categories sleep spindles.
睡眠纺锤波是在非快速眼动 (NREM) 睡眠的 2 期和 3 期出现的短暂、振荡性神经活动的孤立突发放电。它们可以指示大脑中记忆巩固和可塑性的机制。纺锤波可以在皮质区域之间进行识别，并分为慢波或快波。存在跨越不同频率和功率的纺锤波瞬变，但它们的大多数功能仍然是个谜。本研究使用多个脑电图 (EEG) 数据库，提出了一种新的方法，称为“跨多个通道的纺锤波”（SAMC）方法，用于识别和分类 NREM 睡眠期间 EEG 中的睡眠纺锤波。SAMC 方法使用多谱估计和卷积 (MT&C) 方法来提取睡眠 EEG 中存在的不同频率的频谱估计，并以图形方式识别跨多个通道的纺锤波。SAMC 方法还提取了纺锤波的特征，如持续时间、功率和事件区域。与其他最先进的纺锤波识别方法的比较表明，该方法的一致性、平均正预测值和敏感性超过 90%，在本文使用的三个数据库中对纺锤波进行分类具有优越性。计算成本平均为每个时段 0.004 秒。该方法有可能改善对头皮上纺锤波行为的理解，并准确识别和分类睡眠纺锤波。
