Design of Neonatal Seizure Diagnosis Methods Using Time-Frequency Signal Processing Techniques

癫痫发作在所有新生儿中约占0.5%。它们通常是中枢神经系统（CNS）早期功能障碍的唯一指标。它们的出现引起了人们对其潜在原因、对大脑的影响以及适当治疗的关注。新生儿癫痫大多是亚临床的，只能通过脑电图检测到。为了有效诊断，提出了基于视觉观察的癫痫发作分类系统。该项目提出开发一种新的方法，使用基于癫痫发作的多通道特征的时频（TF）信号处理技术来自动分类过程；即：A) TF签名B)起源C)传播行为。

Seizures occur in approximately 0.5% of all newborns. They are often the only indicator of an early dysfunction in central nervous system (CNS). Their occurrence raises concerns about the underlying cause, its effect on the brain, and the appropriate treatment. Newborn seizures are mostly sub-clinical and only detected through the Electroencephalogram. For an efficient diagnosis, seizure classification systems were proposed based on visual observations. This project proposes developing a novel approach to automate the classification process using time-frequency (TF) signal processing techniques based on the multi-channel characteristics of the seizure; namely: A) TF signature B) origin, and C) propagation behaviour.