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Towards On-Demand Deep Brain Stimulation Using Online Parkinson’s Disease Prediction Driven by Dynamic Detection

Mohammed, A; Zamani, M; Bayford, R; Demosthenous, A; (2017) Towards On-Demand Deep Brain Stimulation Using Online Parkinson’s Disease Prediction Driven by Dynamic Detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering , 25 (15) pp. 2441-2452. 10.1109/TNSRE.2017.2722986. Green open access

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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%.

Type: Article
Title: Towards On-Demand Deep Brain Stimulation Using Online Parkinson’s Disease Prediction Driven by Dynamic Detection
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TNSRE.2017.2722986
Publisher version: http://doi.org/10.1109/TNSRE.2017.2722986
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Satellite broadcasting, Heuristic algorithms, Autoregressive processes, Classification algorithms, Feature extraction, Parkinson's disease, Brain stimulation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1568293
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