Department of Information Engineering, Universita' degli Studi di Firenze, Firenze, Italy; Department of Medical Biotechnologies, Universita' degli Studi di Siena, Siena, Italy.
School of Engineering, Universita' degli Studi di Firenze, Firenze, Italy.
J Neurosci Methods. 2021 Jan 15;348:109003. doi: 10.1016/j.jneumeth.2020.109003. Epub 2020 Nov 27.
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 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.
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.
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.
儿科神经学中最具挑战性的问题之一是新生儿癫痫发作的诊断，其治疗的延迟可能会影响新生儿的神经发育。正确诊断的制定受到大量感知或自动检测到的假阳性的影响。
提出了新的方法来评估新生儿癫痫发作随时间的趋势。我们的方法基于分析脑网络的两个属性的标准化趋势：同步性（S）和由圆形 Omega 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癫痫发作事件与发作间期和无癫痫发作患者。
该方法在标记有新生儿癫痫发作的公共数据集上进行了测试。COC 显示在癫痫发作和非癫痫发作事件之间存在显著差异（p 值<0.001，Cohen's d 0.86）。在标准化时间点上结合 S 和 COC 提供了对脑网络生理行为的可靠描述，在新生儿癫痫发作期间。
现有的少数网络方法提出了一种将其分析方法应用于新生儿癫痫诊断过程的可行方法。我们的方法提供了脑网络动力学的简单表示形式，易于实现和理解，即使是经验较少的人员也可以理解。
我们的发现证实了评估脑网络动力学随时间的变化对于更好地理解和解释新生儿癫痫背后的复杂机制的有用性。所提出的方法还可以作为可疑病例的后处理步骤，可靠地支持现有的癫痫发作检测器。
