PMID- 31946497
OWN - NLM
STAT- MEDLINE
DCOM- 20200511
LR  - 20200928
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2019
DP  - 2019 Jul
TI  - A simulated environment for early development stages of reinforcement learning 
      algorithms for closed-loop deep brain stimulation.
PG  - 2900-2904
LID - 10.1109/EMBC.2019.8857533 [doi]
AB  - In recent years, closed-loop adaptive deep brain stimulation (aDBS) for 
      Parkinson's disease (PD) has gained focus in the research community, due to 
      promising proof-of-concept studies showing its suitability for improving DBS 
      therapy and ameliorating related side effects.The main challenges faced in the 
      aDBS control problem is the presence of non-stationary/non-linear dynamics and 
      the heterogeneity of PD's phenotype, making the exploration of data-driven 
      dynamics-aware control algorithms a promising research direction. However, due to 
      the severe safety constraints related to working with patients, aDBS is a 
      sensitive research field that requires surrogate development platforms with 
      growing complexity, as novel control algorithms are validated.With our current 
      contribution, we propose the characterization and categorization of 
      non-stationary dynamics found in the aDBS problem. We show how knowledge about 
      these dynamics can be embedded in a surrogate simulation environment, which has 
      been designed to support early development stages of aDBS control strategies, 
      specifically those based on reinforcement learning (RL) algorithms. Finally, we 
      present a comparison of representative RL methods designed to cope with the type 
      of non-stationary dynamics found in aDBS.To allow reproducibility and encourage 
      adoption of our approach, the source code of the developed methods and simulation 
      environment are made available online.
FAU - Castano-Candamil, Sebastian
AU  - Castano-Candamil S
FAU - Vaihinger, Mara
AU  - Vaihinger M
FAU - Tangermann, Michael
AU  - Tangermann M
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - *Algorithms
MH  - *Deep Brain Stimulation
MH  - Humans
MH  - Nonlinear Dynamics
MH  - *Parkinson Disease/therapy
MH  - Reproducibility of Results
EDAT- 2020/01/18 06:00
MHDA- 2020/05/12 06:00
CRDT- 2020/01/18 06:00
PHST- 2020/01/18 06:00 [entrez]
PHST- 2020/01/18 06:00 [pubmed]
PHST- 2020/05/12 06:00 [medline]
AID - 10.1109/EMBC.2019.8857533 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2900-2904. doi: 
      10.1109/EMBC.2019.8857533.