用于癫痫发作检测和高分辨率微皮层脑电图（μECoG）数据波前估计的棘波模式的无监督学习
Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic (μECoG) Data
摘要：
在过去的几年里，我们已经开发出灵活的，主动的，多路复用的记录设备，用于高分辨率记录大脑中大的，临床相关的区域。虽然这项技术已经能够更高分辨率地观察大脑的电活动，但尚未开发出处理、分类和响应这些设备产生的大量癫痫发作数据的分析方法。在这项工作中，我们提出了一个无监督的学习框架的尖峰分析，它本身揭示了尖峰模式。通过应用先进的视频处理技术将多通道记录分离成单个尖峰段，展开尖峰段流形并识别尖峰模式的自然簇，我们能够找到共同的尖峰运动模式。并进一步探讨了将这些模式用于更有趣和实际的问题，如癫痫发作预测和棘波波前预测。这些方法已被应用于在体内猫癫痫发作记录，并取得了可喜的成果。
英文摘要：
For the past few years, we have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of seizure data produced by these devices have not yet been developed. In this work we proposed an unsupervised learning framework for spike analysis, which by itself reveals spike pattern. By applying advanced video processing techniques for separating a multi-channel recording into individual spike segments, unfolding the spike segments manifold and identifying natural clusters for spike patterns, we are able to find the common spike motion patterns. And we further explored using these patterns for more interesting and practical problems as seizure prediction and spike wavefront prediction. These methods have been applied to in-vivo feline seizure recordings and yielded promising results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
