Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research comm...Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.展开更多
在分析传统侦收船舶自动识别系统(AIS)信号严重依赖全球定位系统(GPS)不足的基础上,提出了一种改进的侦收算法,利用同步序列的实时相关运算实现AIS信号的检测并达到位同步,进而完成解调侦收。改进后的算法不再需要GPS系统的辅助,简化了...在分析传统侦收船舶自动识别系统(AIS)信号严重依赖全球定位系统(GPS)不足的基础上,提出了一种改进的侦收算法,利用同步序列的实时相关运算实现AIS信号的检测并达到位同步,进而完成解调侦收。改进后的算法不再需要GPS系统的辅助,简化了设计降低了成本,在13 d B的信噪比下误包率可以达到5以下。展开更多
基金supported by the Auckland Medical Research Foundation,No.1117017(to CPU)
文摘Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
文摘在分析传统侦收船舶自动识别系统(AIS)信号严重依赖全球定位系统(GPS)不足的基础上,提出了一种改进的侦收算法,利用同步序列的实时相关运算实现AIS信号的检测并达到位同步,进而完成解调侦收。改进后的算法不再需要GPS系统的辅助,简化了设计降低了成本,在13 d B的信噪比下误包率可以达到5以下。
