Modeling and quality assessment of nystagmus eye movements recorded using an eye-tracker

• Published in: Behavior research methods Vol. 52; no. 4; pp. 1729 - 1743
• Main Authors: Rosengren, William, Nyström, Marcus, Hammar, Björn, Rahne, Markus, Sjödahl, Linnea, Stridh, Martin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2020; Springer Nature B.V
• Subjects: Acuity; Behavioral Science and Psychology; Bioinformatics (Computational Biology); Bioinformatik (Beräkningsbiologi); Cognitive Psychology; Computer and Information Sciences; Data- och informationsvetenskap (Datateknik); Engineering and Technology; Eye Movement Measurements; Eye Movements; Humans; Mathematical models; Medical Engineering; Medical Laboratory Technologies; Medicinsk laboratorieteknik; Medicinteknik; Natural Sciences; Naturvetenskap; Nystagmus; Nystagmus, Pathologic - diagnosis; Oscillations; Psychology; Quality control; Teknik; Eye tracking; Modelling; Nystagmus
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-020-01346-y

Mathematical modeling of nystagmus oscillations is a technique with applications in diagnostics, treatment evaluation, and acuity testing. Modeling is a powerful tool for the analysis of nystagmus oscillations but quality assessment of the input data is needed in order to avoid misinterpretation of the modeling results. In this work, we propose a signal quality metric for nystagmus waveforms, the normalized segment error (NSE). The NSE is based on the energy in the error signal between the observed oscillations and a reconstruction from a harmonic sinusoidal model called the normalized waveform model (NWM). A threshold for discrimination between nystagmus oscillations and disturbances is estimated using simulated signals and receiver operator characteristics (ROC). The ROC is optimized to find noisy segments and abrupt waveform and frequency changes in the simulated data that disturb the modeling. The discrimination threshold, 𝜖 , obtained from the ROC analysis, is applied to real recordings of nystagmus data in order to determine whether a segment is of high quality or not. The NWM parameters from both the simulated dataset and the nystagmus recordings are analyzed for the two classes suggested by the threshold. The optimized 𝜖 yielded a true-positive rate and a false-positive rate of 0.97 and 0.07, respectively, for the simulated data. The results from the NWM parameter analysis show that they are consistent with the known values of the simulated signals, and that the method estimates similar model parameters when performing analysis of repeated recordings from one subject.