Wireless EEG/PSG System with Novel Artifact Removal

描述(申请人提供)：EEG是一种有价值的非侵入性临床工具，在许多应用中，从诊断和治疗脑部疾病到临床监测神经损伤、睡眠障碍和麻醉深度。然而，脑电信号非常容易受到各种伪影的影响，严重阻碍了对脑电的解释，影响了其治疗能力。目前用于从脑电记录中去除伪影的方法在临床上并不有效，也不适用于实时和长期的神经监测。因此，本项目的总体目标是开发一种新的、高保真的伪影识别和去除技术，该技术将特别适用于动态脑电记录和干预。 新的伪影去除技术是基于小波的伪影去除(WBAR)方法，它利用了小波分解提供的良好的时频局部化伪影。WBAR方法在计算上非常有效，并且允许同时、实时地去除各种EEG伪影。它最近由PI开发，并作为新型麻醉深度监测器的一部分，在广泛的临床研究中对单个脑电通道进行测试。 对WBAR方法进行改进，将其与小波神经网络相结合用于伪迹的精确分类，并将递归EEG参数化法用于可靠地估计受污染的EEG分量。这些方法的结合将导致全自动、实时伪影去除技术，最大限度地保留有效的EEG信息。 这一新方法的开发和实施将极大地提高克利夫兰医疗设备公司整个系列可移动无线EEG/PSG系统的功能和利用率。

DESCRIPTION (provided by applicant): EEG is a valuable non-invasive clinical tool in numerous applications, from the diagnosis and treatment of brain diseases to the clinical monitoring of neurological injuries, sleep disorders and depth of anesthesia. However, EEG signals are very susceptible to various artifacts which seriously impede the EEG interpretation and compromise its therapeutic capabilities. Methods currently employed for removing artifacts from EEG recordings are not clinically effective or feasible for real-time and long-term neuro-monitoring. Hence, the overall goal of this project is to develop a novel, high-fidelity artifact identification and removal technique that will be specifically useful for ambulatory EEG recording and intervention. The proposed novel artifact removal technique is based on the Wavelet-Based Artifact Removal (WBAR) method, which exploits the excellent time-frequency localization of artifacts provided by the wavelet decomposition. The WBAR method is computationally very efficient and allows for simultaneous, real-time removal of a variety of EEG artifacts. It has been recently developed by the PI and tested for a single EEG channel in an extensive clinical study as part of a novel depth-of-anesthesia monitor. The WBAR method will be improved by combining it with the Wavelet Neural Networks for the precise artifact classification, and recursive EEG Parameterization methods for the reliable estimation of the corrupted EEG components. The combination of these methods will result in fully automated, real-timeartifact removal technique that maximally preserves valid EEG information. The development and implementation of this novel method will greatly enhance the functionality and utilization of Cleveland Medical Devices' entire line of ambulatory wireless EEG/PSG systems.