Automatic nystagmus detection and quantification in long-term continuous eye-movement data

Newman, Jacob L., Phillips, John S., Cox, Stephen J., Fitzgerald, John and Bath, Andrew (2019) Automatic nystagmus detection and quantification in long-term continuous eye-movement data. Computers in Biology and Medicine, 114. ISSN 0010-4825

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Symptoms of dizziness or imbalance are frequently reported by people over 65. Dizziness is usually episodic and can have many causes, making diagnosis problematic. When it is due to inner-ear malfunctions, it is usually accompanied by abnormal eye-movements called nystagmus. The CAVA (Continuous Ambulatory Vestibular Assessment) device has been developed to provide continuous monitoring of eye-movements to gain insight into the physiological parameters present during a dizziness attack. In this paper, we describe novel algorithms for detecting short periods of artificially induced nystagmus from the long-term eye movement data collected by the CAVA device. In a blinded trial involving 17 healthy subjects, each participant induced nystagmus artificially on up to eight occasions by watching a short video on a VR headset. Our algorithms detected these short periods with an accuracy of 98.77%. Additionally, data relating to vestibular induced nystagmus was collected, analysed and then compared to a conventional technique for assessing nystagmus during caloric testing. The results show that a range of nystagmus can be identified and quantified using computational methods applied to long-term eye-movement data captured by the CAVA device.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
Faculty of Medicine and Health Sciences > School of Health Sciences
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Depositing User: LivePure Connector
Date Deposited: 18 Sep 2019 11:30
Last Modified: 11 Jun 2020 01:15
DOI: 10.1016/j.compbiomed.2019.103448

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