Dutch Epilepsy Clinics Foundation Automates the Detection and Diagnosis of Epileptic Seizures

Diagnosing and treating epilepsy requires trained specialists to accurately identify the physical signs of epileptic seizures. This is typically accomplished by reviewing EEG results with synchronized video observation.

卡利津说：“以这种方式检测癫痫发作是具有挑战性的。”“人类观察者容易受到疲劳的影响，这可能会导致他们错过癫痫发作。滤除无关的视频数据很乏味。这一过程也很昂贵，因为我们专门介绍了一名受过训练的技术人员来监视每个患者和三位神经病学专家，以查看脑电图和视频数据离线数据离线。”

SEIN sought to develop a system that could automatically identify seizures based on movements in patient video. Researchers also wanted to use continuous real-time streaming video as input, enabling the system to alert caregivers at the onset of a seizure. Finally, the group needed an integrated development environment that supported image acquisition from a variety of sources, video processing, algorithm development, and statistical validation.

SEIN研究人员使用MATLAB，SIMULINK和计算机视万博1manbetx觉Toolbox™来构建一个系统，该系统通过使用视频数据分析癫痫患者的运动来自动检测癫痫发作。

Kalitzin博士首先将项目分为三个部分：图像采集，处理和分析以及系统输出。这促进了模块化系统设计，使研究人员能够专注于算法开发，并在各种输入格式和输出选项之间进行切换。

The team used Simulink and Computer Vision Toolbox to acquire video data from existing AVI and MPEG files, enabling them to test their algorithms from hundreds of patient videos.

癫痫发作的特征是特定的患者运动：肌阵挛性癫痫发作以单个混蛋，通过僵硬而通过浓缩性癫痫发作和通过重复的节奏性抽搐来区分。SEIN研究人员使用计算机视觉工具箱使用光流技术在视频序列中检测此运动。

他们使用计算机视觉工具箱中的光流块估算了速度字段，然后在多个帧上平均速度字段，以减少要处理的数据量。他们还隔离了正速度和负速度元素，以避免像素之间相互取消。然后，团队通过处理各种位置的成千上万的患者图像流来完善该算法。

After developing the algorithm for detecting seizures, the team used MATLAB, Statistics and Machine Learning Toolbox™, Image Processing Toolbox™, and Signal Processing Toolbox™ to validate the results by comparing them to methods that rely on electromyography, EEG, and video.

Validation results were then used to adjust the sensitivity of the algorithm using a Simulink model. Depending on whether the application will be used for diagnosis or real-time patient monitoring, the model can be increased to detect all seizure-like events or reduced to lower the number of false positives.

Kalitzin plans to enhance the algorithm by automatically selecting a region of interest, which will minimize false positives caused by caregivers that enter the frame. SEIN researchers are also working with other hospitals on automated real-time monitoring of patients using Image Acquisition Toolbox™ to acquire video data from a web camera.