Video-oculography (VOG) is a frequently used clinical technique to detect eye movements. In this research, head mounted small video-cameras and IR-illumination are employed to image the eye. Many algorithms have been developed to extract horizontal and vertical eye movements from the video images. Designing a method to determine torsional eye movements is a more complex task. The use of IR-wavelengths required for illumination in certain clinical tests results in a very low image contrast. In such images, iris textures are almost invisible, making them unsuited for direct application of standard matching algorithms, which are used to calculate torsional eye movements. This research presents the design and implementation of a robust torsional eye movement detection algorithm for VOG. This algorithm uses a new approach to measure the torsional eye movement and is suitable for low contrast videos. The algorithm is implemented in a clinical device and its performance is compared to that of alternative techniques.

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Several algorithms are available to quantify nystagmus beats in electro nystagmography (ENG) and videooculography (VOG) recordings. These algorithms use parameterized approaches to detect the fast components of nystagmus beats. This paper proposes a wavelet approach to detect fast components of nystagmus beats. The main advantage of this approach compared to alternatives, is the completely unsupervised automated routine. The algorithm is implemented and validated in different clinical experiments. The results are compared to that of an alternative parameterized technique. Results show that the wavelet approach is suitable for automated nystagmus analysis.

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