荷兰癫痫诊所基金会自动检测和诊断癫痫发作

诊断和治疗癫痫需要训练有素的专家才能准确识别癫痫发作的物理迹象。这通常是通过通过同步视频观察来审查脑电图结果来完成的。

"Detecting seizures in this way is challenging," says Kalitzin. "Human observers are susceptible to fatigue, which can cause them to miss seizures. Filtering out irrelevant video data is tedious. The process is also costly because we dedicate a trained technician to monitor each patient and three neurology specialists to review EEG and video data offline."

Sein试图开发一个可以根据患者视频中的运动自动识别癫痫发作的系统。研究人员还希望将连续的实时流视频作为输入，从而使系统能够在癫痫发作开始时提醒护理人员。最后，该小组需要一个集成的开发环境，该环境支持从各种来源，视频处理，算法开发和统计验证的图像获取。万博1manbetx

SEIN researchers used MATLAB, Simulink, and Computer Vision Toolbox™ to build a system that automatically detects epileptic seizures by analyzing the movements of epilepsy patients using video data.

Dr. Kalitzin began by partitioning the project into three segments: image acquisition, processing and analysis, and system output. This facilitated a modular system design, enabling the researchers to focus on algorithm development and switch between various input formats and output options.

该团队使用Simulink和万博1manbetx计算机视觉工具箱从现有的AVI和MPEG文件中获取视频数据，从而使他们能够从数百个患者视频中测试其算法。

Epileptic seizures are characterized by specific kinds of patient movement: myoclonic seizures are distinguished by single jerks, tonic seizures by stiffening, and clonic seizures by repetitive, rhythmic jerks. SEIN researchers used Computer Vision Toolbox to detect this motion in video sequences using optical flow techniques.

They estimated velocity fields using the Optical Flow block from Computer Vision Toolbox and then averaged the velocity fields over multiple frames to reduce the amount of data to be processed. They also isolated positive and negative velocity elements to avoid mutual cancellation between pixels. The team then refined this algorithm by processing thousands of image streams of patients in various positions.

在开发用于检测癫痫发作的算法之后，该团队使用MATLAB，统计和机器学习工具箱™，图像处理工具箱™和信号处理工具箱™来通过将结果与依赖肌电图，EEG和视频进行比较来验证结果。

然后使用验证结果使用Simulink模型来调整算法的灵敏度。万博1manbetx根据该应用程序是否用于诊断或实时患者监测，可以增加模型以检测所有类似癫痫发作的事件或减少以降低误报数量。

Kalitzin计划通过自动选择感兴趣的区域来增强算法，该区域将最大程度地减少由进入框架的护理人员引起的误报。SEIN研究人员还使用图像采集Toolbox™与其他医院一起对患者进行自动实时监控，以从网络摄像头获取视频数据。