An Affordable EOG-based Application for Eye Dystonia Evaluation

This paper evaluates the risk of ocular dystonia—a condition marked by excessive blinking—using electrooculography. A commercial bioamplifier is employed to capture the electrical activity of the eyes using dispensed surface electrodes. The continuous wavelet transform of the electrooculogram was estimated to identify the features related to involuntary eye-blinking behavior and make the classification. The signal processing is integrated into a novel application with a simple graphical user interface oriented to be used by physicians. The performance is evaluated using multiple evaluation measures. Results show that the proposed method succeeded in identifying an abnormal frequency of blinks with respective accuracy, precision, sensitivity, and specificity scores of 98.46%, 96.51%, 99.13%, and 96.41%.