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Lee WL, Ward N, Petoe M, Moorhead A, Lawson K, Xu SS, Bulluss K, Thevathasan W, McDermott H, Perera T. Detection of evoked resonant neural activity in Parkinson's disease. J Neural Eng 2024; 21:016031. [PMID: 38364279 DOI: 10.1088/1741-2552/ad2a36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/16/2024] [Indexed: 02/18/2024]
Abstract
Objective. This study investigated a machine-learning approach to detect the presence of evoked resonant neural activity (ERNA) recorded during deep brain stimulation (DBS) of the subthalamic nucleus (STN) in people with Parkinson's disease.Approach. Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features.Main results. Nested cross-validation revealed a strong classification performance of 99.1% accuracy, with 99.6% specificity and 98.7% sensitivity to detect ERNA. Using a semi-simulated ERNA dataset, the results show that a signal-to-noise ratio of 15 dB is required to maintain a 90% classifier sensitivity. ERNA detection is feasible with an appropriate combination of signal processing, feature extraction and classifier. Future work should consider reducing the computational complexity for use in real-time applications.Significance. The presence of ERNA can be used to indicate the location of a DBS electrode array during implantation surgery. The confidence score of the detector could be useful for assisting clinicians to adjust the position of the DBS electrode array inside/outside the STN.
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Affiliation(s)
- Wee-Lih Lee
- Bionics Institute, East Melbourne, Australia
- Medical Bionics Department, University of Melbourne, Parkville, Australia
| | - Nicole Ward
- School of Biomedical Engineering, University of Sydney, Camperdown, Australia
| | - Matthew Petoe
- Bionics Institute, East Melbourne, Australia
- Medical Bionics Department, University of Melbourne, Parkville, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
| | - Ashton Moorhead
- Bionics Institute, East Melbourne, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
| | - Kiaran Lawson
- Bionics Institute, East Melbourne, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
| | - San San Xu
- Bionics Institute, East Melbourne, Australia
- Medical Bionics Department, University of Melbourne, Parkville, Australia
- National Hospital for Neurology and Neurosurgery, Queen Square, United Kingdom
| | - Kristian Bulluss
- Bionics Institute, East Melbourne, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
- Department of Neurosurgery, Austin Hospital, Heidelberg, Australia
- Department of Neurosurgery, Cabrini Hospital, Malvern, Australia
- Department of Neurosurgery, St. Vincent's Hospital, Fitzroy, Australia
- Department of Surgery, University of Melbourne, Parkville, Australia
| | - Wesley Thevathasan
- Bionics Institute, East Melbourne, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Australia
- Department of Medicine, University of Melbourne, Parkville, Australia
| | - Hugh McDermott
- Medical Bionics Department, University of Melbourne, Parkville, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
- Department of Medicine, University of Melbourne, Parkville, Australia
| | - Thushara Perera
- Bionics Institute, East Melbourne, Australia
- Medical Bionics Department, University of Melbourne, Parkville, Australia
- DBS Technologies Pty Ltd, East Melbourne, Australia
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Gülke E, Juárez Paz L, Scholtes H, Gerloff C, Kühn AA, Pötter-Nerger M. Multiple input algorithm-guided Deep Brain stimulation-programming for Parkinson's disease patients. NPJ Parkinsons Dis 2022; 8:144. [PMID: 36309508 PMCID: PMC9617933 DOI: 10.1038/s41531-022-00396-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/14/2022] [Indexed: 12/04/2022] Open
Abstract
Technological advances of Deep Brain Stimulation (DBS) within the subthalamic nucleus (STN) for Parkinson's disease (PD) provide increased programming options with higher programming burden. Reducing the effort of DBS optimization requires novel programming strategies. The objective of this study was to evaluate the feasibility of a semi-automatic algorithm-guided-programming (AgP) approach to obtain beneficial stimulation settings for PD patients with directional DBS systems. The AgP evaluates iteratively the weighted combination of sensor and clinician assessed responses of multiple PD symptoms to suggested DBS settings until it converges to a final solution. Acute clinical effectiveness of AgP DBS settings and DBS settings that were found following a standard of care (SoC) procedure were compared in a randomized, crossover and double-blind fashion in 10 PD subjects from a single center. Compared to therapy absence, AgP and SoC DBS settings significantly improved (p = 0.002) total Unified Parkinson's Disease Rating Scale III scores (median 69.8 interquartile range (IQR) 64.6|71.9% and 66.2 IQR 58.1|68.2%, respectively). Despite their similar clinical results, AgP and SoC DBS settings differed substantially. Per subject, AgP tested 37.0 IQR 34.0|37 settings before convergence, resulting in 1.7 IQR 1.6|2.0 h, which is comparable to previous reports. Although AgP long-term clinical results still need to be investigated, this approach constitutes an alternative for DBS programming and represents an important step for future closed-loop DBS optimization systems.
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Affiliation(s)
- Eileen Gülke
- grid.13648.380000 0001 2180 3484Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - León Juárez Paz
- grid.418905.10000 0004 0437 5539Boston Scientific, Valencia, CA Spain
| | - Heleen Scholtes
- grid.418905.10000 0004 0437 5539Boston Scientific, Valencia, CA Spain
| | - Christian Gerloff
- grid.13648.380000 0001 2180 3484Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andrea A. Kühn
- grid.6363.00000 0001 2218 4662Department of Neurology, Movement disorders & Neuromodulation section, Charité – University Medicine Berlin, Berlin, Germany
| | - Monika Pötter-Nerger
- grid.13648.380000 0001 2180 3484Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Xu SS, Lee WL, Perera T, Sinclair NC, Bulluss KJ, McDermott HJ, Thevathasan W. Can brain signals and anatomy refine contact choice for deep brain stimulation in Parkinson's disease? J Neurol Neurosurg Psychiatry 2022:jnnp-2021-327708. [PMID: 35589375 DOI: 10.1136/jnnp-2021-327708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 04/25/2022] [Indexed: 11/03/2022]
Abstract
INTRODUCTION Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson's disease is time-consuming and reliant on clinical expertise. The aim of this cohort study was to assess whether neuronal signals (beta oscillations and evoked resonant neural activity (ERNA)), and the anatomical location of electrodes, can predict the contacts selected by long-term, expert-clinician programming of STN-DBS. METHODS We evaluated 92 hemispheres of 47 patients with Parkinson's disease receiving chronic monopolar and bipolar STN-DBS. At each contact, beta oscillations and ERNA were recorded intraoperatively, and anatomical locations were assessed. How these factors, alone and in combination, predicted the contacts clinically selected for chronic deep brain stimulation at 6 months postoperatively was evaluated using a simple-ranking method and machine learning algorithms. RESULTS The probability that each factor individually predicted the clinician-chosen contact was as follows: ERNA 80%, anatomy 67%, beta oscillations 50%. ERNA performed significantly better than anatomy and beta oscillations. Combining neuronal signal and anatomical data did not improve predictive performance. CONCLUSION This work supports the development of probability-based algorithms using neuronal signals and anatomical data to assist programming of deep brain stimulation.
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Affiliation(s)
- San San Xu
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, Melbourne, Victoria, Australia
| | - Wee-Lih Lee
- Bionics Institute, East Melbourne, Victoria, Australia
| | - Thushara Perera
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, Melbourne, Victoria, Australia
| | - Nicholas C Sinclair
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kristian J Bulluss
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurosurgery, St Vincent's Hospital, Fitzroy, Victoria, Australia
- Department of Neurosurgery, Austin Hospital, Heidelberg, Victoria, Australia
- Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia
| | - Hugh J McDermott
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, Melbourne, Victoria, Australia
| | - Wesley Thevathasan
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
- Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
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