Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany.
Leibniz Institute for Resilience Research, Mainz, Germany.
J Sleep Res. 2022 Dec;31(6):e13733. doi: 10.1111/jsr.13733. Epub 2022 Sep 21.
Sleep spindles are a hallmark electroencephalographic feature of non-rapid eye movement sleep, and are believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain-stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real-time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector, a robust multi-channel electroencephalographic signal-processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the real-time spindle detection method by streaming pre-recorded sleep electroencephalographic datasets to a real-time computer system running a Simulink® Real-Time™ implementation of the algorithm. Sleep spindles were detected with high levels of Sensitivity (83%), Precision (78%) and a convincing F1-Score (~81%) in reference to state-of-the-art offline algorithms (which reached similar or lower levels when compared with each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude and symmetry, and showed the typical time-frequency characteristics as well as a centroparietal topography. Spindles were detected close to their centre and reliably at the predefined target phase. The real-time spindle detection algorithm therefore empowers researchers to target spindles during human sleep, and apply the stimulation method and experimental paradigm of their choice.
睡眠纺锤波是非快速眼动睡眠的标志性脑电图特征，被认为对睡眠依赖性记忆再激活和巩固至关重要。然而，很难获得其因果相关性的直接证据，我们对其即时神经生理后果的理解也有限。为了研究它们的因果作用，需要使用感觉或非侵入性脑刺激技术实时靶向纺锤波。虽然全自动离线检测算法已经很成熟，但由于纺锤波的自发性和瞬时性，实时检测纺锤波极具挑战性。在这里，我们提出了实时纺锤波检测器，这是一种稳健的多通道脑电图信号处理算法，能够以特定相位的方式自动触发睡眠纺锤波期间的刺激。我们通过将预先录制的睡眠脑电图数据集流式传输到实时计算机系统来验证实时纺锤波检测方法，该系统运行的是算法的 Simulink® Real-Time™ 实现。与最先进的离线算法相比（相互比较时达到类似或更低的水平），该实时检测方法在参考睡眠评分信息的情况下，在小睡和整夜睡眠中都能以高灵敏度（83%）、高精度（78%）和令人信服的 F1 分数（~81%）检测到睡眠纺锤波，并且在很大程度上独立于睡眠评分信息。检测到的纺锤波在频率、持续时间、幅度和对称性上具有可比性，并显示出典型的时频特征和中央顶叶拓扑结构。纺锤波在接近中心的位置被可靠地检测到，并在预定的目标相位上进行检测。因此，实时纺锤波检测算法使研究人员能够在人类睡眠期间靶向纺锤波，并应用他们选择的刺激方法和实验范式。
