Increase the accuracy in classifying subjects with or without early diabetic retinopathy, by analyzing the multifocal electroretinogram (mfERG) responses.Method:
The mfERG was recorded for 14 control subjects (Normal) and 26 non-insulin dependent diabetic patients, 16 of the patients had no apparent retinopathy (NDR), the other 10 had mild to moderate non-proliferative retinopathy (NPDR). The first order kernel (K1) and the first slice of second order kernel (K21) of the mfERG were summed across the field and exported as trace arrays. The feature subset was selected from the kernel arrays by criterion of inter-intra distance and sequential forward searching strategy. Based on the selected features, Fisher's linear classifiers were trained and the classification error rates were given.Result:
With one feature selected, the error rates of the classifiers were bigger than 23% between the NDR and NPDR subjects. When six features were selected to train the classifiers, the classification error rate decreased to 6.5% between Normal and NDR, 0 between Normal and NPDR, and 9.6% between NDR and NPDR. The criterion function of inter-intra distance was calculated for each feature in the K1 and K21 arrays. According to rank of the function value, during early DR, deformations should be easier to find at the front limb of peaks N2 and P1 on the K1 trace, and at the front limb of peak N2 on the K21 trace. As a conclusion, the multifocal ERG responses have great potentials in exploring diabetic retinopathy, assisted with the pattern classification methods.