Early Detection of Retinitis Pigmentosa from Pattern Electroretinography Signals Using Time–Frequency Analysis and Machine Learning

Retinitis pigmentosa (RP) is a hereditary retinal disorder characterized by progressive degeneration of photoreceptor cells, ultimately leading to severe vision loss. Early detection of functional retinal abnormalities is essential for timely clinical intervention and effective disease management. Pattern electroretinography (PERG) provides an objective assessment of retinal ganglion cell activity and has been widely used for evaluating retinal function. This study proposes a machine learning–based framework for the early detection of RP using time-, frequency-, and time–frequency analyses of PERG signals. Temporal features describing amplitude and latency characteristics were first extracted from the time domain. Frequency-domain characteristics were then obtained using Fast Fourier Transform (FFT). To capture localized spectral–temporal variations in the signals, discrete wavelet transform (DWT) and continuous wavelet transform (CWT) were further employed for time–frequency feature extraction. Three machine learning classifiers—support vector machine (SVM), K-nearest neighbors (KNN), and quadratic discriminant analysis (QDA)—were evaluated to determine the most effective model for distinguishing RP patients from healthy subjects. Experimental results demonstrate that time–frequency features classified using QDA achieved the best performance, with an accuracy of 98.2%, outperforming models based solely on time-domain (94.5%) and frequency-domain (78.5%) features. These findings indicate that integrating temporal and spectral information significantly improves diagnostic performance and provides a reliable computational tool for early RP detection using PERG signals.

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