Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network

Abstract

Deep learning has a huge potential in healthcare for uncovering the hidden patterns from large volume of clinical data to diagnose different diseases. This paper presents a novel deep learning architecture based long short term memory (LSTM) network for severity rating of Parkinson’s disease (PD) using gait pattern. Unlike machine learning (ML) algorithms, the LSTM network avoids the need for hand crafted features and learns the long-term temporal dependencies in the gait cycle for robust diagnosis of PD. The primary advantage of the LSTM network is that it solves the vanishing gradient problem by introducing the memory blocks in place of self-connected hidden units, thereby deciding when to learn new information. Three distinct gait datasets containing vertical ground reaction force (VGRF) recordings for different walking scenarios are used for training the LSTM network. To avoid data overfitting, the proposed approach utilizes dropout and L2 regularization techniques. For solving the cost function, Adam, a stochastic gradient-based optimizer, is employed and the severity of PD is categorized based on unified Parkinson’s disease rating scale (UPDRS) and Hoehn and Yahr (H&Y) scale. The experimental results reveal that Adam optimized LSTM network can effectively learn the gait kinematic features and offer an average accuracy of 98.6% for binary classification and 96.6% for multi-class classification, with an accuracy improvement of 3.4% in comparison with the related techniques.