Automatic nystagmus detection and quantification in long-term continuous eye-movement data. (November 2019)

• Record Type: Journal Article
• Authors: Newman, Jacob L.; Phillips, John S.; Cox, Stephen J.; FitzGerald, John; Bath, Andrew
• Is Part Of: Computers in biology and medicine. Volume 114(2019)
• Publication Date: 2019-11
• Subjects: Nystagmus -- Dizziness -- Vestibular diseases -- Event detection -- Biomedical signal processing -- Electronystagmography -- Time series classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103448
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: CAVA is a new system for detecting eye-movements associated with dizziness. Our algorithms detected artificially-induced nystagmus with 98.77% accuracy. The direction and speed of nystagmus can be determined with high accuracy. Nystagmus induced through caloric testing can be detected and quantified using CAVA.