Background

One of the most challenging issues in paediatric neurology is the diagnosis of neonatal seizures, whose delayed treatment may affect the neurodevelopment of the newborn. Formulation of the correct diagnosis is conditioned by the high number of perceptually or automatically detected false positives.

New Method

New methodologies are proposed to assess neonatal seizures trend over time. Our approach is based on the analysis of standardized trends of two properties of the brain network: the Synchronizabilty (S) and the degree of phase synchronicity given by the Circular Omega Complexity (COC). Qualitative and quantitative methods based on network dynamics allow differentiating seizure events from interictal periods and seizure-free patients.

Results

The methods were tested on a public dataset of labelled neonatal seizures. COC shows significant differences among seizure and non-seizure events (p-value <0.001, Cohen’s d 0.86). Combining S and COC in standardized temporal instants provided a reliable description of the physiological behaviour of the brain’s network during neonatal seizures.

Comparison with Existing Method(s)

Few of the existing network methods propose an operative way for carrying their analytical approach into the diagnostic process of neonatal seizures. Our methods offer a simple representation of brain network dynamics easily implementable and understandable also by less experienced staff.

Conclusions

Our findings confirm the usefulness of the evaluation of brain network dynamics over time for a better understanding and interpretation of the complex mechanisms behind neonatal seizures. The proposed methods could also reliably support existing seizure detectors as a post-processing step in doubtful cases.

中文翻译：

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新生儿癫痫发作期间脑网络动力学的多参数脑电图分析

背景

小儿神经病学中最具挑战性的问题之一是新生儿癫痫的诊断，其延迟治疗可能会影响新生儿的神经发育。正确诊断的制定取决于大量感知或自动检测到的假阳性。

新方法

提出了新的方法来评估新生儿癫痫发作随时间的变化趋势。我们的方法基于对大脑网络的两个属性的标准化趋势的分析：同步性（S）和由圆形欧米茄复杂度（COC）给出的相位同步程度。基于网络动力学的定性和定量方法可以区分发作期和发作期和无发作的患者。

结果

在标记为新生儿癫痫发作的公共数据集上测试了这些方法。COC显示癫痫发作和非癫痫发作之间存在显着差异（p值<0.001，Cohen's d 0.86）。在标准化的瞬间结合S和COC，为新生儿惊厥时大脑网络的生理行为提供了可靠的描述。

与现有方法的比较

现有的网络方法很少提出将其分析方法用于新生儿癫痫诊断过程的有效方法。我们的方法提供了大脑网络动力学的简单表示，即使经验不足的员工也可以轻松实现和理解。

结论

我们的发现证实了随着时间的推移对大脑网络动力学进行评估的有效性，可以更好地理解和解释新生儿惊厥背后的复杂机制。所提出的方法还可以可靠地支持现有的癫痫发作检测器，作为可疑情况下的后处理步骤。