Neda Sefandarmaz1, Soroor Behbahani2, Alireza Ramezani3. 1. Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran. 2. Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran. Sor.behbahani@gmail.com. 3. Ophthalmic Epidemiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract
PURPOSE: Central retinal vein occlusion (CRVO) is the second most common retinal vascular disorder after diabetic retinopathy that affects the eyes. We propose a method for distinction of normal and central CRVO eyes based on electroretinogram (ERG). METHODS: Seventeen patients with CRVO in one eye were analyzed. Their ERG signals were collected in six different stimuli, including four records in the darkness (dark-adapted 0.01, dark-adapted 3.0, dark-adapted oscillatory potentials, and dark-adapted 10) and two records in brightness (light-adapted 3.0 and light-adapted 30 Hz flicker). Nonlinear features such as Hurst exponent (HE) and approximate entropy (ApEn) were extracted from healthy and CRVO eyes. Finally, a parabolic mapping and two criteria (theta angle and the density of points) were proposed to distinguish the groups. RESULTS: For ApEn, the P values of dark-adapted 3.0 oscillatory (P = 0.0433) and flicker (P = 0.0425) confirmed significant differences between the groups. For HE, the P values of dark-adapted 3.0 oscillatory (P = 0.0421) and flicker 30 Hz (P = 0.0402) confirmed differences between the healthy and CRVO groups. The P values of theta angle for dark-adapted 3.0 (P = 0.0199), dark-adapted oscillatory (P = 0.0265), dark-adapted 10.0 (P = 0.0166), light-adapted 3.0 (P = 0.0411), and flicker (P = 0.0361) showed significant differences. Using the density criterion, the statistical test demonstrated a significant difference between the groups in dark-adapted 3 (P = 0.0038), dark-adapted oscillatory (P = 0.0102), dark-adapted 10.0 (P = 0.0071), light-adapted 3.0 (P = 0.0319), and flicker 30 Hz (P = 0.0076). CONCLUSION: The proposed features have made it possible to distinguish between healthy and CRVO eyes. This method could be helpful in some cases with no definite diagnosis or to estimate the severity of CRVO.
PURPOSE: Central retinal vein occlusion (CRVO) is the second most common retinal vascular disorder after diabetic retinopathy that affects the eyes. We propose a method for distinction of normal and central CRVO eyes based on electroretinogram (ERG). METHODS: Seventeen patients with CRVO in one eye were analyzed. Their ERG signals were collected in six different stimuli, including four records in the darkness (dark-adapted 0.01, dark-adapted 3.0, dark-adapted oscillatory potentials, and dark-adapted 10) and two records in brightness (light-adapted 3.0 and light-adapted 30 Hz flicker). Nonlinear features such as Hurst exponent (HE) and approximate entropy (ApEn) were extracted from healthy and CRVO eyes. Finally, a parabolic mapping and two criteria (theta angle and the density of points) were proposed to distinguish the groups. RESULTS: For ApEn, the P values of dark-adapted 3.0 oscillatory (P = 0.0433) and flicker (P = 0.0425) confirmed significant differences between the groups. For HE, the P values of dark-adapted 3.0 oscillatory (P = 0.0421) and flicker 30 Hz (P = 0.0402) confirmed differences between the healthy and CRVO groups. The P values of theta angle for dark-adapted 3.0 (P = 0.0199), dark-adapted oscillatory (P = 0.0265), dark-adapted 10.0 (P = 0.0166), light-adapted 3.0 (P = 0.0411), and flicker (P = 0.0361) showed significant differences. Using the density criterion, the statistical test demonstrated a significant difference between the groups in dark-adapted 3 (P = 0.0038), dark-adapted oscillatory (P = 0.0102), dark-adapted 10.0 (P = 0.0071), light-adapted 3.0 (P = 0.0319), and flicker 30 Hz (P = 0.0076). CONCLUSION: The proposed features have made it possible to distinguish between healthy and CRVO eyes. This method could be helpful in some cases with no definite diagnosis or to estimate the severity of CRVO.
Entities:
Keywords:
Central retinal vein occlusion; Electroretinogram; Feature extraction; Parabolic mapping