Parkinson’s disease diagnosis and stage prediction based on gait signal analysis using EMD and CNN–LSTM network - B. Vidya; Sasikumar P. (Engineering applications of artificial intelligence, 2022)
Parkinson’s disease (PD) diagnosis is a complex and challenging task which needs the assessment of various motor and non-motor symptoms. As gait impairment is one of the early and important symptoms of PD, in a clinical setting, physicians generally evaluate the gait abnormality based on visual observations along with other numerous manifestations to assess the severity of PD. As such kind of assessment majorly depends on the experience and expertise of the physicians, there is scope for bias in assessment, leading to misdiagnosis. In this context, to assist the physicians to diagnose PD effectively, this study aims to design and investigate a gait analysis based classifier model using a hybrid convolutional neural network-long short term memory (CNN–LSTM) network to predict the severity rating of PD. For evaluation, we utilize the openly available gait dataset from Physionet that consists of vertical ground reaction force (VGRF) signals from three different walking tests. Firstly, the prominent VGRF signals obtained using the variability analysis are decomposed using the empirical mode decomposition (EMD) technique to extract the significant intrinsic mode functions (IMFs) that contain the vital gait features. Secondly, through the power spectral analysis the dominant IMFs of the selected VGRF signals are extracted to train the CNN–LSTM classifier model. To address the data overfitting problem in the classifier model, the proposed approach employs L2 regularization along with dropout techniques. Moreover, to solve the stochastic cost function, CNN–LSTM network utilizes the Adam optimizer for its minimal memory requirement and tuning. Finally, the experiments conducted using the gait patterns from 93 PD subjects and 73 healthy controls substantiate that the proposed CNN–LSTM​ classifier model can achieve a maximum multi-class classification accuracy of 98.32% and offer superior performance compared to several other similar methods which have used gait pattern to diagnose PD. [Display omitted] •PD stage prediction based on gait classification using CNN–LSTM network is proposed.•The VGRF signals are decomposed using EMD technique to extract dominant IMFs.•L2 regularization along with dropout layer is employed to address the data overfitting problem.•The multi-class classification accuracy of 98.32% is achieved using Adam optimizer.