Miguel-Jiménez J M, Blanco R, De-Santiago L, Fernández A, Rodríguez-Ascariz J M, Barea R, Martín-Sánchez J L, Amo C, Sánchez-Morla E, Boquete L
Department of Electronics, University of Alcalá, Alcalá de Henares, Spain.
Med Biol Eng Comput. 2015 Sep;53(9):771-80. doi: 10.1007/s11517-015-1287-6. Epub 2015 Apr 8.
The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals' amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.
绝大多数用于检测青光眼的多焦视网膜电图（mfERG）信号分析都研究信号的振幅和潜伏期。本文的目的是研究mfERG信号的小波分析在青光眼诊断中的应用。该分析方法将连续小波变换（CWT）应用于信号，使用实Morlet小波。最大相关性尺度下得到的CWT系数用作神经网络的输入，该神经网络作为分类器。mfERG记录取自47名被诊断为慢性开角型青光眼的受试者以及24名健康受试者的眼睛。分类中的高灵敏度（0.894）为青光眼区域提供了可靠的检测，而所达到的特异性（0.844）反映了对健康区域的准确检测。本文获得的结果比作者使用相同视觉刺激和数据库之前报告的结果有所改进。
