Literature DB >> 31751280

An Investigation of Neural Stimulation Efficiency with Gaussian Waveforms.

Steffen Eickhoff, Jonathan C Jarvis.   

Abstract

OBJECTIVE: Previous computational studies predict that Gaussian shaped waveforms use the least energy to activate nerves. The primary goal of this study was to examine the claimed potential of up to 60% energy savings with these waveforms over a range of phase widths (50-200µs) in an animal model.
METHODS: The common peroneal nerve of anaesthetized rats was stimulated via monopolar and bipolar electrodes with single stimuli. The isometric peak twitch force of the extensor digitorum longus muscle was recorded to indicate the extent of neural activation. The energy consumption, charge injection and maximum instantaneous power values required to reach 50% neural activation were compared between Gaussian pulses and standard rectangular stimuli.
RESULTS: Energy savings in the 50-200µs range of phase widths did not exceed 17% and were accompanied by significant increases in maximum instantaneous power of 110-200%. Charge efficiency was found to be increased over the whole range of tested phase widths with Gaussian compared to rectangular pulses and reached up to 55% at 1ms phase width.
CONCLUSION: These findings challenge the claims of up to 60% energy savings with Gaussian like stimulation waveforms. The moderate energy savings achieved with the novel waveform are accompanied with considerable increases in maximal instantaneous power. Larger power sources would therefore be required, and this opposes the trend for implant miniaturization. SIGNIFICANCE: This is the first study to comprehensively investigate stimulation efficiency of Gaussian waveforms. It sheds new light on the practical potential of such stimulation waveforms.

Entities:  

Year:  2019        PMID: 31751280     DOI: 10.1109/TNSRE.2019.2954004

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  1 in total

1.  Sports Energy Consumption Evaluation Based on Improved Adaptive Weighted Data Fusion Energy-Saving Algorithm.

Authors:  Ling Han; Yanping Jiang
Journal:  Comput Intell Neurosci       Date:  2022-04-22
  1 in total

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