Dataset Information

PD-ResNet for Classification of Parkinson's Disease From Gait.

ABSTRACT:

Objective

To develop an objective and efficient method to automatically identify Parkinson's disease (PD) and healthy control (HC).

Methods

We design a novel model based on residual network (ResNet) architecture, named PD-ResNet, to learn the gait differences between PD and HC and between PD with different severity levels. Specifically, a polynomial elevated dimensions technique is applied to increase the dimensions of the input gait features; then, the processed data is transformed into a 3-dimensional picture as the input of PD-ResNet. The synthetic minority over-sampling technique (SMOTE), data augmentation, and early stopping technologies are adopted to improve the generalization ability. To further enhance the classification performance, a new loss function, named improved focal loss function, is developed to focus on the train of PD-ResNet on the hard samples and to discard the abnormal samples.

Results

The experiments on the clinical gait dataset show that our proposed model achieves excellent performance with an accuracy of 95.51%, a precision of 94.44%, a recall of 96.59%, a specificity of 94.44%, and an F1-score of 95.50%. Moreover, the accuracy, precision, recall, specificity, and F1-score for the classification of early PD and HC are 92.03%, 94.20%, 90.28%, 93.94%, and 92.20%, respectively. Furthermore, the accuracy, precision, recall, specificity, and F1-score for the classification of PD with different severity levels are 92.03%, 94.29%, 90.41%, 93.85%, and 92.31%, respectively.

Conclusion

Our proposed method shows better performance than the traditional machine learning and deep learning methods.

Clinical impact

The experimental results show that the proposed method is clinically meaningful for the objective assessment of gait motor impairment for PD patients.

SUBMITTER: Yang X

PROVIDER: S-EPMC9252336 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.MethodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.ResultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.ConclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.