论文标题
通过卷积稀疏编码从眼线轨道数据中提取眼球震颤模式
Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding
论文作者
论文摘要
对眼睛追踪记录的眼球震颤波形的分析对于这种病理运动的临床解释至关重要。自动化此分析的一个主要问题是存在自然的眼动和眼睛眨眼的人物，它们与感兴趣的信号混合在一起。我们提出了一种基于卷积词典学习的方法，该方法能够自动介绍黑眼形波形，从而将自然运动与病理运动分开。我们在模拟信号上表明，我们的方法确实可以提高模式回收率并提供临床示例，以说明该算法的性能。
The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinicial interpretation of this pathological movement. A major issue to automatize this analysis is the presence of natural eye movements and eye blink artefacts that are mixed with the signal of interest. We propose a method based on Convolutional Dictionary Learning that is able to automaticcaly highlight the Nystagmus waveforms, separating the natural motion from the pathological movements. We show on simulated signals that our method can indeed improve the pattern recovery rate and provide clinical examples to illustrate how this algorithm performs.
