A deep explainable artificial intelligent framework for neurological disorders discrimination

Abstract

Abstract Pathological hand tremor (PHT) is a common symptom of Parkinson’s disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven $$\text {NeurDNet}$$ NeurDNet model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. $$\text {NeurDNet}$$ NeurDNet is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The $$\text {NeurDNet}$$ NeurDNet outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of $$95.55\%$$ 95.55 % . In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.