Video-EEG Data Compression

长时间同时记录脑电图（EEG）波形和视频通常在评估癫痫发作患者时进行。近年来，基于MPEG视频压缩标准的数字视频-脑电图系统应运而生。这些系统可以提供快速访问任何感兴趣的视频片段，并支持计算机屏幕上的各种显示选项。然而，由于现有的基于mpeg的软件包主要针对电影和数字电视等应用，不能很好地适应长时间癫痫视频监控的情况，因此它们的数据压缩性能不理想。因此，由于数据量过大，数据存档和管理、通过互联网访问数据、远程诊断和家庭癫痫监测等重要应用受到阻碍。我们建议对视频压缩进行研究，以帮助癫痫的诊断。我们将根据癫痫视频的特点和MPEG-4视频压缩标准开发新的视频对象分割算法。利用这些算法，我们将设计一个最先进的高分辨率、低输出率癫痫数据采集系统，用于脑电图和视频，以支持快速的互联网数据传输和高效的数据存档。最后，我们将在农村地区的偏远医院进行一系列实地试验，以评估我们的系统。

Prolonged simultaneous recording of both electroencephalogram (EEG) waveforms and video is often conducted during the evaluation of patients with seizures. Recently, digital video-EEG systems based on MPEG video compression standards have emerged. These systems can provide quick access to any video segment of interest, and support various display options on computer screens. However, they have sub-optimal data compression performance because the existing MPEG-based software packages, which mainly target applications such as films and digital TV, do not adapt well to the case of epilepsy video monitoring over extended periods of time. As a result, important applications such as data archiving and management, data access through the Internet, remote diagnosis, and home epilepsy monitoring have been hampered due to the excessive data size. We propose an investigation on video compression to be applied specifically to aid in epilepsy diagnosis. We will develop new algorithms for video object segmentation based on special characteristics of epilepsy video and the MPEG-4 video compression standard. Using these algorithms we will design a state-of-the-art high-resolution, low output rate epilepsy data acquisition system for both EEG and video to support rapid Internet data transmission and efficient data archiving. Finally, we will conduct a series of field-tests at remote hospital sites in rural regions to evaluate our system.