The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
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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Cites
The NEURON Simulation Environment
Hines, M. L., & Carnevale, N. T. (1997) · Neural Computation · doi:10.1162/neco.1997.9.6.1179 · Cited by 2,630
Analysis of a Model for Excitation of Myelinated Nerve
McNeal, D. R. (1976) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1976.324593 · Cited by 833
McIntyre, C. C., Richardson, A. G., & Grill, W. M. (2002) · Journal of Neurophysiology · doi:10.1152/jn.00353.2001 · Cited by 764
Self-citationCharge density and charge per phase as cofactors in neural injury induced by electrical stimulation
McCreery, D. B., Agnew, W. F., Yuen, T. G. H., & Bullara, L. (1990) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.102812 · Cited by 708
Axons, but not cell bodies, are activated by electrical stimulation in cortical gray matter
Nowak, L. G., & Bullier, J. (1998) · Experimental Brain Research · doi:10.1007/s002210050304 · Cited by 492
Excitation of Central Nervous System Neurons by Nonuniform Electric Fields
McIntyre, C. C., & Grill, W. M. (1999) · Biophysical Journal · doi:10.1016/s0006-3495(99)77251-6 · Cited by 319
Self-citationExcitation of dorsal root fibers in spinal cord stimulation: a theoretical study
Struijk, J. J., Holsheimer, J., & Boom, H. B. K. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.237693 · Cited by 204
Histological Evaluation of Neural Damage from Electrical Stimulation
Yuen, T. G. H., Agnew, W. F., Bullara, L. A., Jacques, S., & McCreery, D. B. (1981) · Neurosurgery · doi:10.1227/00006123-198109000-00013 · Cited by 160
Non-rectangular waveforms for neural stimulation with practical electrodes
Sahin, M., & Tie, Y. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/3/008 · Cited by 157
Anodal vs cathodal stimulation of motor cortex: A modeling study
Manola, L., Holsheimer, J., Veltink, P. H., & Buitenweg, J. R. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.09.012 · Cited by 154
Research works printed but not linked: 3
- Dimitrova N A, 1992, Gen. Physiol. Biophys., 11, 69
- Sweeney J D Mortimer J D Durand D, 1987, IEEE/9th Annu. Conf. Eng. Med. Biol. Sci.
- Wongsarnpigoon A Grill W M, 2009, IEEE 31st Ann. Intl Conf. Eng. Med. Biol. Soc.
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Mechanisms of deep brain stimulation
Herrington, T. M., Cheng, J. J., & Eskandar, E. N. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00281.2015 · Cited by 551
Neuromodulation: present and emerging methods
Luan, S., Williams, I. A., Nikolić, K., & Constandinou, T. G. (2014) · Frontiers in Neuroengineering · doi:10.3389/fneng.2014.00027 · Cited by 168
Lee, H.-M., Kwon, K. Y., Li, W., & Ghovanloo, M. (2015) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2014.2355814 · Cited by 161
Evaluation of novel stimulus waveforms for deep brain stimulation
Foutz, T. J., & McIntyre, C. C. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/6/066008 · Cited by 167
Principles of electrical stimulation of neural tissue
(2013) · Handbook of Clinical Neurology · doi:10.1016/b978-0-444-53497-2.00001-2 · Cited by 151
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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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