Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation

Abstract

Abstract-Oculomotor tests (OMT) are administered to quantify symptoms in neurological and mental diseases. Eye movements in response to displayed visual stimuli are registered by an digital video-based eye tracker and processed. Stimuli of simple signal form, e.g. sine waves, are traditionally used in medical practice to test the performance of the oculomotor system in smooth pursuit (SP). The calculated SP gain and the phase shift at the frequency in question are then presented as the test outcome. This paper revisits the problem of quantifying the SP dynamics from eye-tracking data by means of nonlinear system identification. First, a sparse Volterra-Laguerre (VL) model is estimated from an OMT with sufficiently exciting (in frequency and amplitude) stimuli. Then the structure and initial parameter estimates of a polynomial Wiener model (WM) are obtained from the kernel estimates of the VL model. Finally, the parameter distributions of the WM are inferred by a particle filter (PF). In the proposed approach, the performance of the PF is improved by the individualized sparse model structure. Experimental data show that the latter captures the alternations in the SP dynamics due to aging and in Parkinson's disease.

Identification of the Oculo-Motor System in the Form Volterra Model Based on Eye-Tracking Data

“…The developed software enables support of the following tasks: − The relationship study of mental states and cognitive processes in educational activities, a post-traumatic stress disorder, the diagnosis of the Parkinson's and Alzheimer's disease stage, checking the psychophysiological state of pilots and drivers, the professional suitability, the fatigue syndrome [10][11][12][13][14][15][16].…”

Data-driven models of pelvic floor muscles dynamics subject to psychological and physiological stimuli

“…How uniformly do different people physiologically respond to the same movie? Given our vision of providing tools for designing personalized vaginal dilation patterns, we seek to answer the questions above by focusing specifically on models with control-oriented structures such as Hammerstein-Wiener and Nonlinear autoregressive exogenous (NARX) models, which have been shown to be suitable in other biomedical applications, see Bro and Medvedev (2017) and Langdon, Docherty, Chiew, Möller, and Chase (2016).…”

“…Their main drawback is though that they are more difficult to be used for automatic control purposes. On the other hand, Hammerstein-Wiener and more control-oriented NARX models have also been proven to be capable of high approximation capabilities for various medical applications (e.g., Bro and Medvedev (2017), Langdon et al (2016)). In the paper we thus explore the approximation capabilities of Hammerstein-Wiener models with different nonlinearities and some NARX strategies for the available dataset.…”

Estimation psychophysiological state via nonlinear dynamic integral models

“…In recent years, the high-tech innovation of eyetracking has received further development and effective application in the construction of a mathematical model of the process of continuous eye movement tracking in order to detect anomalies in the tracking data for the quantitative assessment of motor symptoms of Parkinson's disease [10,11], [12]. At the same time, nonlinear dynamic models of Wiener and Volterra-Laguerre [13] are used, and the identification of the OMS is based on the use of test random effects, which requires the use of correlation analysis methods and obtaining a large amount of experimental data (long duration of experimental studies).…”