An approach based on wavelet analysis for feature extraction in the electroretinogram

Most biomedical signals are non-stationary. The knowledge of their frequency content andtemporal distribution is then useful in a clinical context. The wavelet analysis is appropriateto achieve this task. The present paper uses this method to reveal hidden characteristicsand anomalies of the human a-wave, an important component of the electroretinogramsince it is a measure of the functional integrity of the photoreceptors. We here analyse thetime–frequency features of the a-wave both in normal subjects and in patients affected byAchromatopsia, a pathology disturbing the functionality of the cones. The results indicatethe presence of two or three stable frequencies that, in the pathological case, shift towardlower values and change their times of occurrence. The present findings are a first steptoward a deeper understanding of the features of the a-wave and possible applications todiagnostic procedures in order to recognise incipient photoreceptoral pathologies.