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

利用时频信号处理技术设计新生儿惊厥诊断方法

• 批准号: LP0562317
• 负责人: Prof Paul Colditz
• 金额: $ 13.32万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2006
• 资助国家: 澳大利亚
• 起止时间: 2006-09-18 至 2009-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP0562317
• 关键词: Design; Neonatal; Seizure; Diagnosis; Methods

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.

癫痫发作约占所有新生儿的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。它们的发生引起了人们对潜在原因、对大脑的影响以及适当治疗的担忧。新生儿癫痫发作大多是亚临床的，只有通过脑电才能发现。为了进行有效的诊断，提出了基于视觉观察的癫痫发作分类系统。该项目提出了一种新的方法，利用基于癫痫发作的多通道特征的时频(TF)信号处理技术来实现分类过程的自动化，即：A)TF特征B)起源和C)传播行为。