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

Mohammed, Ameer and Zamani, Majid and 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). pp. 2441-2452. ISSN 1534-4320

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Abstract

In Parkinson’s disease (PD), on-demand deep brain stimulation (DBS) 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 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 mean accuracy measures of classification accuracy 99.29%, F1-score of 97.90% and a choice probability of 99.86%.

Item Type: Article
Research Areas: A. > School of Science and Technology > Natural Sciences > Biophysics and Bioengineering group
Item ID: 21775
Notes on copyright: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Richard Bayford
Date Deposited: 25 Sep 2017 14:52
Last Modified: 05 Sep 2018 12:48
URI: http://eprints.mdx.ac.uk/id/eprint/21775

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