Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection

Mohammed, Ameer, Zamani, Majid, Bayford, Richard and Demosthenous, Andreas (2017) Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (12), 2441-2452. (doi:10.1109/TNSRE.2017.2722986).

Abstract

In Parkinson’s disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain–machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

This record has no associated files available for download.

Contributors

Author: Majid Zamani

Download statistics