Azami Hamed, Chang Zhuoqing, Arnold Steven E, Sapiro Guillermo, Gupta Anoopum S
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.
Department of Electrical and Computer Engineering, Duke University, Durham, NC 27707, USA.
IEEE Access. 2022;10:34022-34031. doi: 10.1109/access.2022.3156964. Epub 2022 Mar 4.
Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.e., hypermetric and hypometric saccades) is a common finding in individuals with cerebellar ataxia. In this study, we evaluated a scalable approach for detecting and quantifying oculomotor dysmetria. Eye movement data were extracted from iPhone video recordings of the horizontal saccade task (a standard clinical task in ataxia) and combined with signal processing and machine learning approaches to quantify saccade abnormalities. Entropy-based measures of eye movements during saccades were significantly different in 72 individuals with ataxia with dysmetria compared with 80 ataxia and Parkinson's participants without dysmetria. A template matching-based analysis demonstrated that saccadic eye movements in patients without dysmetria were more similar to the ideal template of saccades. A support vector machine was then used to train and test the ability of multiple signal processing features in combination to distinguish individuals with and without oculomotor dysmetria. The model achieved 78% accuracy (sensitivity= 80% and specificity= 76%). These results show that the combination of signal processing and machine learning approaches applied to iPhone video of saccades, allow for extraction of information pertaining to oculomotor dysmetria in ataxia. Overall, this inexpensive and scalable approach for capturing important oculomotor information may be a useful component of a screening tool for ataxia and could allow frequent at-home assessments of oculomotor function in natural history studies and clinical trials.
眼动评估有助于诊断和跟踪神经系统疾病。小脑性共济失调会导致平稳跟踪、扫视和注视出现严重且具有特征性的异常。动眼神经辨距障碍（即扫视过度和扫视不足）在小脑性共济失调患者中很常见。在本研究中，我们评估了一种用于检测和量化动眼神经辨距障碍的可扩展方法。眼动数据从水平扫视任务（共济失调的一项标准临床任务）的iPhone视频记录中提取，并结合信号处理和机器学习方法来量化扫视异常。与80名无辨距障碍的共济失调和帕金森病参与者相比，72名有辨距障碍的共济失调患者在扫视期间基于熵的眼动测量值有显著差异。基于模板匹配的分析表明，无辨距障碍患者的扫视眼动与理想扫视模板更相似。然后使用支持向量机来训练和测试多种信号处理特征组合区分有无动眼神经辨距障碍个体的能力。该模型的准确率达到78%（灵敏度 = 80%，特异性 = 76%）。这些结果表明，将信号处理和机器学习方法应用于iPhone扫视视频，能够提取与共济失调中动眼神经辨距障碍相关的信息。总体而言，这种用于获取重要眼动信息的低成本且可扩展的方法可能是共济失调筛查工具的一个有用组成部分，并可在自然史研究和临床试验中对眼动功能进行频繁的家庭评估。
