Visual function alterations in essential tremor: A case report

Our purpose is to report alterations in contrast sensitivity function (CSF) and in the magno, parvo and koniocellular visual pathways by means of a multichannel perimeter in case of an essential tremor (ET). A complete evaluation of the visual function was performed in a 69-year old patient, including the analysis of the chromatic discrimination by the Fansworth–Munsell 100 hue test, the measurement of the CSF by the CSV-1000E test, and the detection of potential alteration patterns in the magno, parvo and koniocellular visual pathways by means of a multichannel perimeter. Visual acuity and intraocular pressure (IOP) were within the ranges of normality in both eyes. No abnormalities were detected in the fundoscopic examination and in the optical coherence tomography (OCT) exam. The results of the color vision examination were also within the ranges of normality. A significant decrease in the achromatic CSFs for right eye (RE) and left eye (LE) was detected for all spatial frequencies. The statistical global values provided by the multichannel perimeter confirms that there were significant absolute sensitivity losses compared to the normal pattern in RE. In the LE, only a statistically significant decrease in sensitivity was detected for the blue-yellow (BY) channel. The pattern standard deviation (PSD) values obtained in our patient indicated that there were significant localized losses compared to the normality pattern in the achromatic channel of the RE and in the red-green (RG) channel of the LE. Some color vision alterations may be present in ET that cannot be detected with conventional color vision tests, such as the FM 100 Hue.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.