Comput Methods Programs Biomed - An approach based on wavelet analysis for feature extraction in the a-wave of the electroretinogram.

Tópicos

{ signal(2180) analysi(812) frequenc(800) }
{ take(945) account(800) differ(722) }
{ case(1353) use(1143) diagnosi(1136) }
{ use(1733) differ(960) four(931) }
{ estim(2440) model(1874) function(577) }
{ data(1737) use(1416) pattern(1282) }
{ featur(3375) classif(2383) classifi(1994) }
{ concept(1167) ontolog(924) domain(897) }
{ studi(1410) differ(1259) use(1210) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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 pathological case, shift toward lower values and change their times of occurrence. The present findings are a first step toward a deeper understanding of the features of the a-wave and possible applications to diagnostic procedures in order to recognise incipient photoreceptoral pathologies.

Resumo Limpo

biomed signal nonstationari knowledg frequenc content tempor distribut use clinic context wavelet analysi appropri achiev task present paper use method reveal hidden characterist anomali human awav import compon electroretinogram sinc measur function integr photoreceptor analys timefrequ featur awav normal subject patient affect achromatopsia patholog disturb function cone result indic presenc two three stabl frequenc patholog case shift toward lower valu chang time occurr present find first step toward deeper understand featur awav possibl applic diagnost procedur order recognis incipi photoreceptor patholog

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