Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection
Article
Mohammed, A., Zamani, M., Bayford, R. and Demosthenous, A. 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. https://doi.org/10.1109/TNSRE.2017.2722986
Type | Article |
---|---|
Title | Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection |
Authors | Mohammed, A., Zamani, M., Bayford, R. and Demosthenous, A. |
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%. |
Research Group | Biophysics and Bioengineering group |
Publisher | IEEE Engineering in Medicine and Biology Society |
Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
ISSN | 1534-4320 |
Publication dates | |
Online | 03 Jul 2017 |
01 Dec 2017 | |
Publication process dates | |
Deposited | 25 Sep 2017 |
Accepted | 23 Jun 2017 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TNSRE.2017.2722986 |
Language | English |
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