A Novel Binary Classification Algorithm for Carpal Tunnel Syndrome Detection Using LSTM

Carpal tunnel syndrome (CTS) is one of the common neurological disorders caused by prolonged compression of the median nerve. Thus, CTS patients’ daily tasks are significantly affected. Traditional diagnostic methods are invasive or subjective, causing pain or inaccuracy. Therefore, a more accurate machine/deep learning classifier is needed to provide an accessible assessment approach that can help screen out early-stage patients to prevent further deterioration. Behavioral biomechanics has shown great potential to be used for CTS and its severity classification. The biomechanical parameters are collected when identified patients and healthy individuals perform daily life activities, such as grasping and lifting in a controlled manner. Facing the challenges of time series biomechanical data with small sample sizes and high dimensions, we propose a novel classification algorithm to create an ensemble model for CTS detection using Long Short-Term Memory (LSTM). The proposed algorithm achieves 93% accuracy on average for CTS detection using biomechanical data of daily life activities.