A wavelet approach for unsupervised nystagmus analysis on ENG and VOG recordings

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.