当大脑没有接受足够的氧气和血液时,会发生继发于围产期窒息的缺氧缺血性脑病(HIE)。“完整生存”的替代标记对于HIE的成功管理是必要的。HIE的严重程度可以根据临床表现进行分类,包括癫痫发作的存在,使用称为Sarnat分期的临床分类量表;然而,Sarnat分期是主观的,分数会随着时间的推移而变化。此外,癫痫发作很难在临床上发现,并且与不良预后相关。因此,在婴儿床侧连续监测的工具是必要的,例如,脑电图(EEG),非侵入性地从头皮测量大脑的电活动。然后,多模态脑成像,当与功能近红外光谱(fNIRS)结合使用时,可以捕捉神经血管耦合(NVC)状态。在这项研究中,我们首先测试了低成本EEG-fNIRS成像系统的可行性,缺氧,围产期绵羊缺氧模型的发病状态。这里,目的是评估便携式床侧装置,并进行额外输入自回归(ARX)建模,以捕获模拟HIE损伤期间的围产期绵羊脑状态.所以,ARX参数用线性分类器使用单差分通道EEG进行测试,使用fNIRS检测到不同的组织氧合状态,在绵羊模型中标记模拟的HIE状态。然后,我们展示了低成本EEG-fNIRS设备和ARX建模与支持向量机分类的技术可行性,用于有和没有败血症的人类HIE病例系列。用绵羊缺氧数据训练的分类器将10例严重HIE人类病例(有和没有败血症)标记为“缺氧”组,将4例中度HIE人类病例标记为“对照”组。此外,我们证明了基于ARX模型的实验模态分析(EMA)可利用EEG-fNIRS联合成像数据研究NVC动力学,该数据可将6例无脓毒症的重度HIE患者与4例脓毒症的重度HIE患者区分开来.总之,我们的研究显示了EEG-fNIRS成像的技术可行性,用于HIE分类的NVC的ARX建模,和EMA可能提供脓毒症对HIE中NVC影响的生物标志物。
Hypoxic-ischemic encephalopathy (HIE) secondary to perinatal asphyxia occurs when the brain does not receive enough oxygen and blood. A surrogate marker for \"intact survival\" is necessary for the successful management of HIE. The severity of HIE can be classified based on clinical presentation, including the presence of seizures, using a clinical classification scale called Sarnat staging; however, Sarnat staging is subjective, and the score changes over time. Furthermore, seizures are difficult to detect clinically and are associated with a poor prognosis. Therefore, a tool for continuous monitoring on the cot side is necessary, for example, an electroencephalogram (EEG) that noninvasively measures the electrical activity of the brain from the scalp. Then, multimodal brain imaging, when combined with functional near-infrared spectroscopy (fNIRS), can capture the neurovascular coupling (NVC) status. In this study, we first tested the feasibility of a low-cost EEG-fNIRS imaging system to differentiate between normal, hypoxic, and ictal states in a perinatal ovine hypoxia model. Here, the objective was to evaluate a portable cot-side device and perform autoregressive with extra input (ARX) modeling to capture the perinatal ovine brain states during a simulated HIE injury. So, ARX parameters were tested with a linear classifier using a single differential channel EEG, with varying states of tissue oxygenation detected using fNIRS, to label simulated HIE states in the ovine model. Then, we showed the technical feasibility of the low-cost EEG-fNIRS device and ARX modeling with support vector machine classification for a human HIE
case series with and without sepsis. The classifier trained with the ovine hypoxia data labeled ten severe HIE human cases (with and without sepsis) as the \"hypoxia\" group and the four moderate HIE human cases as the \"control\" group. Furthermore, we showed the feasibility of experimental modal analysis (EMA) based on the ARX model to investigate the NVC dynamics using EEG-fNIRS joint-imaging data that differentiated six severe HIE human cases without sepsis from four severe HIE human cases with sepsis. In conclusion, our study showed the technical feasibility of EEG-fNIRS imaging, ARX modeling of NVC for HIE classification, and EMA that may provide a biomarker of sepsis effects on the NVC in HIE.