Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the inner-ear, the eyes flicker and this is called nystagmus. In this article we describe the first use of Deep Neural Network architectures applied to detecting nystagmus. The data used in these experiments was gathered during a clinical investigation of a novel medical device for recording head and eye movements. We describe methods for training networks using very limited amounts of training data, with an average of 11 mins of nystagmus across four subjects, and less than 24 hours of data in total, per subject. Our methods work by replicating and modifying existing samples to generate new data. In a cross-fold validation experiment, we achieve an average F1 score of 0.59 (SD = 0.24) across all four folds, showing that the methods employed are capable of identifying periods of nystagmus with a modest degree of accuracy. Notably, we were also able to identify periods of pathological nystagmus produced by a patient during an acute attack of Ménière's Disease, despite training the network on nystagmus that was induced by different means.

中文翻译：

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用于检测眼球震颤的一维卷积神经网络

眩晕是一种头晕，其特征是静止时有主观感觉。社区中四分之一的人在任何特定时间都会出现头晕症状，临床医生诊断根本原因可能具有挑战性。当头晕是内耳功能障碍的结果时，眼睛会闪烁，这被称为眼球震颤。在本文中，我们描述了首次使用深度神经网络架构来检测眼球震颤。这些实验中使用的数据是在对一种用于记录头部和眼球运动的新型医疗设备的临床研究期间收集的。我们描述了使用非常有限的训练数据来训练网络的方法，每个受试者平均有 11 分钟的眼球震颤，总共不到 24 小时的数据。我们的方法通过复制和修改现有样本来生成新数据。在交叉验证实验中，我们在所有四个折叠中实现了 0.59 (SD = 0.24) 的平均 F1 分数，表明所采用的方法能够以适度的准确度识别眼球震颤的时期。值得注意的是，我们还能够识别出患者在梅尼埃病急性发作期间产生的病理性眼球震颤的时期，尽管对通过不同方式诱发的眼球震颤进行了网络训练。