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Energy-efficient waveform shapes for neural stimulation revealed with a genetic algorithm
Open accessAbstract
The energy efficiency of stimulation is an important consideration for battery-powered implantable stimulators. We used a genetic algorithm (GA) to determine the energy-optimal waveform shape for neural stimulation. The GA was coupled to a computational model of extracellular stimulation of a mammalian myelinated axon. As the GA progressed, waveforms became increasingly energy efficient and converged upon an energy-optimal shape. The results of the GA were consistent across several trials, and resulting waveforms resembled truncated Gaussian curves. When constrained to monophasic cathodic waveforms, the GA produced waveforms that were symmetric about the peak, which occurred approximately during the middle of the pulse. However, when the cathodic waveforms were coupled to rectangular charge-balancing anodic pulses, the location and sharpness of the peak varied with the duration and timing (i.e., before or after the cathodic phase) of the anodic phase. In a model of a population of mammalian axons and in vivo experiments on a cat sciatic nerve, the GA-optimized waveforms were more energy efficient and charge efficient than several conventional waveform shapes used in neural stimulation. If used in implantable neural stimulators, GA-optimized waveforms could prolong battery life, thereby reducing the frequency of recharge intervals, the volume of implanted pulse generators, and the costs and risks of battery-replacement surgeries.
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“In implanted devices, exponential-shaped waveforms, utilizing minimal energy, can be used to minimize the power requirement for activation of larger and closer axons (Wongsarnpigoon and Grill, 2010; Krouchev et al., 2014).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Wongsarnpigoon and Grill, 2010
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Metadata
- Authors
- Wongsarnpigoon, A., & Grill, W. M.
- Affiliations
- Duke University (US)
- ORCID
- Grill, Warren M
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2010-06-23
- Volume
- 7
- Issue
- 4
- Pages
- 046009
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC2925408
- OpenAlex
- W2094180230
- Cited by
- 166
- References
- 25
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 5 · 2023: 13 · 2022: 12 · 2021: 4 · 2020: 11 · 2019: 10 · 2018: 19 · 2017: 16 · 2016: 16 · 2015: 15 · 2014: 12 · 2013: 9 · 2012: 5
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (R01NS040894, R21NS054048)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Neurology › Vagus Nerve Stimulation Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- neurostimulation, genetic algorithm optimization, implantable neural stimulator, battery life, extracellular stimulation, myelinated axons, charge balancing, sciatic nerve, charging efficiency, wave shape
- MeSH terms
- Action Potentials, Algorithms, Animals, Cats, Computer Simulation, Electric Stimulation, Energy Transfer, Male, Models, Genetic, Models, Neurological, Sciatic Nerve, Muscle, Skeletal
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