Robust analysis of dynamical change in scalp electroencephalogram time series

近年来，我们对大脑功能的理解取得了令人兴奋的进展。然而，许多问题仍未解决。其中之一是对大脑的动态分析，这是科学中最大的挑战之一。这种分析有助于理解，预测和识别大脑功能。头皮脑电图（EEG）时间序列是一种无创记录，反映了大脑活动的生理状态。EEG时间序列的动态变化分析在许多研究和诊断应用中是非常有用的，例如检测不同的睡眠阶段和病理状态，包括癫痫发作、精神疾病、阿尔茨海默病、帕金森病和Creutzfeldt-Jakob病。该方法将建立一个措施，以量化的程度复杂的行为的基础上动态变化的成分的EEG。我们的新方法是基于三个步骤：分解，组件表征和融合。我们将调整并应用开发的算法到癫痫患者记录的EEG中，以预测癫痫发作，这是一种导致大脑突然过度活跃的身体状况（神经系统疾病）。据估计，世界人口的1%已经或将在他们的一生中有某种形式的癫痫发作障碍。从患者的角度来看，癫痫最令人致残的方面之一是其不可预测性。它损害患者的生活质量，包括活动限制、药物管理、安全和焦虑。因此，癫痫发作预测可以改善癫痫患者的生活质量。

In recent years, there have been exciting developments in our understanding of brain functions. However, many problems remain unsolved. Among them is dynamical analysis of the brain that is one of the biggest challenges in science. This analysis helps in understanding, predicting and recognizing brain functions. Scalp electroencephalogram (EEG) time series is a non-invasive recording that reflects physiological states of brain activities. Analysis of dynamical change in EEG time series can be extremely useful in many research and diagnosis applications, such as detecting different sleep stages and pathologic states including epileptic seizure, psychiatric disorders, Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases.The objective of this research is to develop a reliable and robust method for EEG analysis in the presence of noise. The method will establish a measure to quantify the degree of complex behavior of the brain based on dynamical change in the components of EEG. Our novel method is based on three steps: decomposition, component characterization and fusion. We will tune and apply the developed algorithms to EEG recorded from patients with epilepsy in order to predict seizure, a physical condition (neurological disorder) which causes sudden bursts of hyperactivity in the brain. It is estimated that 1% of the world population have had, or will have, some form of seizure disorder during their lifetime. From patient's point of view, one of the most disabling aspects of epilepsy is its unpredictability. It impairs patient's quality of life including restrictions on activities, management of medications, safety, and anxiety. Therefore, seizure prediction can improve the quality of life for patients with epilepsy.