Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

Abstract

Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs. control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia.