Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Medical research confirms that eye movement abnormalities are related to a variety of psychological activities, mental disorders and physical diseases. However, as the specific manifestations of various diseases in terms of eye movement disorders remain unclear, the accurate diagnosis of diseases according to eye movement is difficult. In this paper, a deep neural network (DNN) method is employed to establish a disease discrimination model according to eye movement. First, multiple eye-tracking experiments are designed to obtain eye images. Second, pupil characteristics, including position and size, are extracted, and the feature vectors of eye movement are obtained from the normalized pupil information. Based on a long short-term memory (LSTM) network, a classifier that corresponds to each feature, which is referred to as a weak classifier, is built. The experimental samples are preclassified, and the classification ability of each weak classifier for different diseases is also calculated. Last, a strong classifier is achieved for disease discrimination by synthesizing all the weak classifiers and their classification abilities. By classification testing for three categories of healthy controls, brain injury patients and vertigo patients, the experimental results demonstrated the efficiency of this method. With the deep learning method, more medical information can be excavated from eye movement to improve the values in disease diagnosis.