Analysing nystagmus waveforms: a computational framework

Electrophysiological analysis of ENG signals in patients with Covid-19

Background

Currently, there is an increasing number of patients reporting dizziness, which has resulted in a positive COVID-19 PCR test. In this paper, we analyzed the ENG signals recorded from patients with a positive COVID-19 PCR test.

Methods

In this paper, both linear and nonlinear analyses of time series were employed to determine the regularity and complexity of a recorded ENG signal.

Results

The Wilcoxon rank-sum test indicated that the COVID-19 and non-COVID groups have significant differences based on different extracted features. Various machine learning methods including Linear Discriminant Analysis (LDA), Naïve Base (NB), K-nearest Neighbours (KNN), and Support Vector Machines (SVM) were used to classify COVID-19 and non-COVID groups. The best accuracy, precision and FCR achieved by SVM are 86%, 91% and 0.13.

Conclusion

In this study, ENG signals were recorded from COVID-19 and control groups. Linear and non-linear features were extracted from the recorded signals to identify significantly different features. Subjects were classified based on SVM and different classifiers. The SVM (polynomial kernel) classifier showed the best result. The proposed method had not been used for the classification of COVID-19 and non-COVID-19 subjects before. This work helps other researchers conduct more research on the development of machine learning methods to diagnose the COVID-19 virus using ENG and other physiological signals.

Eye Tracking in Optometry: A Systematic Review

This systematic review examines the use of eye-tracking devices in optometry, describing their main characteristics, areas of application and metrics used. Using the PRISMA method, a systematic search was performed of three databases. The search strategy identified 141 reports relevant to this topic, indicating the exponential growth over the past ten years of the use of eye trackers in optometry. Eye-tracking technology was applied in at least 12 areas of the field of optometry and rehabilitation, the main ones being optometric device technology, and the assessment, treatment, and analysis of ocular disorders. The main devices reported on were infrared light-based and had an image capture frequency of 60 Hz to 2000 Hz. The main metrics mentioned were fixations, saccadic movements, smooth pursuit, microsaccades, and pupil variables. Study quality was sometimes limited in that incomplete information was provided regarding the devices used, the study design, the methods used, participants' visual function and statistical treatment of data. While there is still a need for more research in this area, eye-tracking devices should be more actively incorporated as a useful tool with both clinical and research applications. This review highlights the robustness this technology offers to obtain objective information about a person's vision in terms of optometry and visual function, with implications for improving visual health services and our understanding of the vision process.