Search results for " Achromatopsia"
showing 3 items of 3 documents
Empirical mode decomposition and neural network for the classification of electroretinographic data
2013
The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals' features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behaviour characterized by strong non-linear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose to apply a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyse electroretinograms, i.e. the retinal response to a light flash, with the aim to detect and classify retinal diseases…
An approach based on wavelet analysis for feature extraction in the electroretinogram
2011
Most biomedical signals are non-stationary. The knowledge of their frequency content and temporal distribution is then useful in a clinical context. The wavelet analysis is appropriate to achieve this task. The present paper uses this method to reveal hidden characteristics and anomalies of the human a-wave, an important component of the electroretinogram since it is a measure of the functional integrity of the photoreceptors. We here analyse the time–frequency features of the a-wave both in normal subjects and in patients affected by Achromatopsia, a pathology disturbing the functionality of the cones. The results indicate the presence of two or three stable frequencies that, in the pathol…
Wavelet analysis of human photoreceptoral response
2010
Feature detection of biomedical signals is crucial for deepening our knowledge of the physiological phenomena giving rise to them. To achieve this aim, even if many analytic approaches have been suggested only few are able to deal with signals whose features are time dependent, and to provide useful clinical information. In this work we use the wavelet analysis to extract peculiarities of the early response of the photoreceptoral human system, known as a-wave ERG-component. The analysis of the a-wave features is important since this component reflects the functional integrity of the two populations of photoreceptors, rods and cones whose activation dynamics are not well known. Moreover, in …