A comparison among different techniques for human ERG signals processing and classification |
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| Affiliation: | 1. Dipartimento di Fisica e Chimica, Università di Palermo and CNISM, Viale delle Scienze, Ed. 18, I-90128 Palermo, Italy;2. Laboratorio di Fisica e Tecnologie Relative – UniNetLab, Università di Palermo, Viale delle Scienze, Ed. 18, I-90128 Palermo, Italy;2. Department of Ophthalmology, Faculty of Medicine, Pamukkale University, Denizli, Turkey;1. Medical Physics Unit, Veneto Institute of Oncology IOV – IRCCS, Padua, Italy;2. Radiotherapy and Nuclear Medicine Unit, Veneto Institute of Oncology IOV – IRCCS, Padua, Italy;3. Melanoma and Sarcoma Unit, Veneto Institute of Oncology IOV – IRCCS, Padua, Italy;1. Service d’ophtalmogie CHR Tsevié, faculté des sciences de la santé de l’université de Lomé, BP 13648, Lomé, Togo;2. Service d’ophtalmologie CHU Sylvanus-Olympio, Lomé, Togo;3. Cabinet d’ophtalmologie AFIA, route de Kpalimé, Adidogomé, 07 BP 13648, Lomé, Togo;4. Service d’ophtalmologie, CHU Campus, Lomé, Togo;1. University Eye Center, Medical Center – University of Freiburg, Germany;2. Faculty of Medicine, University of Freiburg, Germany;3. Visual Processing Laboratory, Universitäts-Augenklinik, Magdeburg, Germany;4. Center for Behavioural Brain Sciences, Magdeburg, Germany;2. Department of Biostatistics, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India;1. SAGA Light Source, 8-7 Yayoigaoka, Tosu, Saga 841-0005, Japan;2. National Research Tomsk State University, Lenin Ave. 30, 634050 Tomsk, Russia;3. Institute of High Current Electronics, SB RAS, Academychesky Ave. 2/3, 634055 Tomsk, Russia;4. National Research Tomsk Polytechnic University, Lenin Ave. 36, 634050 Tomsk, Russia |
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| Abstract: | Feature detection in biomedical signals is crucial for deepening our knowledge about the involved physiological processes. To achieve this aim, many analytic approaches can be applied but only few are able to deal with signals whose time dependent features provide useful clinical information. Among the biomedical signals, the electroretinogram (ERG), that records the retinal response to a light flash, can improve our comprehension of the complex photoreceptoral activities.The present study is focused on the analysis of the early response of the photoreceptoral human system, known as a-wave ERG-component. This wave reflects the functional integrity of the photoreceptors, rods and cones, whose activation dynamics are not yet completely understood. Moreover, since in incipient photoreceptoral pathologies eventual anomalies in a-wave are not always detectable with a “naked eye” analysis of the traces, the possibility to discriminate pathologic from healthy traces, by means of appropriate analytical techniques, could help in clinical diagnosis.In the present paper, we discuss and compare the efficiency of various techniques of signal processing, such as Fourier analysis (FA), Principal Component Analysis (PCA), Wavelet Analysis (WA) in recognising pathological traces from the healthy ones. The investigated retinal pathologies are Achromatopsia, a cone disease and Congenital Stationary Night Blindness, affecting the photoreceptoral signal transmission. Our findings prove that both PCA and FA of conventional ERGs, don't add clinical information useful for the diagnosis of ocular pathologies, whereas the use of a more sophisticated analysis, based on the wavelet transform, provides a powerful tool for routine clinical examinations of patients. |
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| Keywords: | ERG signals Wavelet analysis Principal component analysis Retinal pathologies |
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