An improved algorithm for classification of nystagmus was designed allowing the sorting of response segments even in severely non-linear patients and subjects with abnormally large phase shifts. The algorithm employs a model-based approach that was developed by Rey and Galiana (1991). The improved classification algorithm consists of two essential stages. In the first stage the eye velocity response is classified to obtain best possible estimates of the slow phase eye velocity intervals. In the second stage, the slow phase estimates are used to identify a response phase shift and non-linearity, and compensate for their effects. Multiple tests on simulated data and experimental data obtained from clinical subjects are presented. The results of the tests demonstrate that the algorithm is able to analyze the patient data with a high accuracy even in the presence of noise, eye-blinks and other artifacts.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.81561 |
| Date | January 2004 |
| Creators | Radinsky, Iliya |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Engineering (Department of Biomedical Engineering) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 002166429, proquestno: AAIMR06578, Theses scanned by UMI/ProQuest. |
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