TY - GEN
T1 - DyNeuMo Mk-2
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
AU - Toth, Robert
AU - Zamora, Mayela
AU - Ottaway, Jon
AU - Gillbe, Tom
AU - Martin, Sean
AU - Benjaber, Moaad
AU - Lamb, Guy
AU - Noone, Tara
AU - Taylor, Barry
AU - Deli, Alceste
AU - Kremen, Vaclav
AU - Worrell, Gregory
AU - Constandinou, Timothy G.
AU - De Wachter, Ivor Gillbe Stefan
AU - Knowles, Charles
AU - Sharott, Andrew
AU - Valentin, Antonio
AU - Green, Alexander L.
AU - Denison, Timothy
N1 - Funding Information:
This work was supported by the John Fell Fund, the UK Medical Research Council and the Royal Academy of Engineering. Robert Toth, Mayela Zamora, Moaad Benjaber, Andrew Sharott and Tim Denison are with the MRC Brain Network Dynamics Unit, and the Department of Engineering Science, University of Oxford, Oxford OX2 7DQ, UK. Jon Ottaway, Tom Gillbe, Guy Lamb, Tara Noone, Barry Taylor and Ivor Gillbe are with Bioinduction Ltd, Bristol BS8 4RP, UK. Timothy G. Constandinou is with the Department of Electrical and Electronic Engineering and the UK Dementia Research Institute (Care Research and Technology Centre), Imperial College London, London SW7 2AZ, UK. Sean Martin, Alceste Deli and Alexander L. Green are with the Department of Neurosurgery, John Radcliffe Hospital, Oxford OX3 9DU, UK. Vaclav Kremen and Gregory A. Worrell are with the Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN, US. Stefan De Wachter is with the Department of Urology, University of Antwerp Hospital, 2650 Edegem, Belgium. Charles H. Knowles is with the Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK. Antonio Valentin is with the Department of Basic and Clinical Neuroscience, King’s College London, London SE5 9RT, UK. Correspondence: robert.toth@bndu.ox.ac.uk, timothy.denison@eng.ox.ac.uk
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - Deep brain stimulation (DBS) for Parkinson's disease, essential tremor and epilepsy is an established palliative treatment. DBS uses electrical neuromodulation to suppress symptoms. Most current systems provide a continuous pattern of fixed stimulation, with clinical follow-ups to refine settings constrained to normal office hours. An issue with this management strategy is that the impact of stimulation on circadian, i.e. sleep-wake, rhythms is not fully considered; either in the device design or in the clinical follow-up. Since devices can be implanted in brain targets that couple into the reticular activating network, impact on wakefulness and sleep can be significant. This issue will likely grow as new targets are explored, with the potential to create entraining signals that are uncoupled from environmental influences. To address this issue, we have designed a new brain-machine-interface for DBS that combines a slow-adaptive circadian-based stimulation pattern with a fast-acting pathway for responsive stimulation, demonstrated here for seizure management. In preparation for first-in-human research trials to explore the utility of multi-timescale automated adaptive algorithms, design and prototyping was carried out in line with ISO risk management standards, ensuring patient safety. The ultimate aim is to account for chronobiology within the algorithms embedded in brain-machine-interfaces and in neuromodulation technology more broadly.
AB - Deep brain stimulation (DBS) for Parkinson's disease, essential tremor and epilepsy is an established palliative treatment. DBS uses electrical neuromodulation to suppress symptoms. Most current systems provide a continuous pattern of fixed stimulation, with clinical follow-ups to refine settings constrained to normal office hours. An issue with this management strategy is that the impact of stimulation on circadian, i.e. sleep-wake, rhythms is not fully considered; either in the device design or in the clinical follow-up. Since devices can be implanted in brain targets that couple into the reticular activating network, impact on wakefulness and sleep can be significant. This issue will likely grow as new targets are explored, with the potential to create entraining signals that are uncoupled from environmental influences. To address this issue, we have designed a new brain-machine-interface for DBS that combines a slow-adaptive circadian-based stimulation pattern with a fast-acting pathway for responsive stimulation, demonstrated here for seizure management. In preparation for first-in-human research trials to explore the utility of multi-timescale automated adaptive algorithms, design and prototyping was carried out in line with ISO risk management standards, ensuring patient safety. The ultimate aim is to account for chronobiology within the algorithms embedded in brain-machine-interfaces and in neuromodulation technology more broadly.
KW - Activity recognition
KW - Adaptive control
KW - Brain stimulation
KW - Chronobiology
KW - Circadian rhythm
KW - Closed loop systems
KW - Digital filters
KW - Neural implants
KW - Safety management
UR - http://www.scopus.com/inward/record.url?scp=85098874753&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098874753&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9283187
DO - 10.1109/SMC42975.2020.9283187
M3 - Conference contribution
AN - SCOPUS:85098874753
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3433
EP - 3440
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 October 2020 through 14 October 2020
ER -