2000 Fiscal Year Final Research Report Summary
Detection and classification of neural information from peripheral nerves
| Project/Area Number |
10450156
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Tohoku University |
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Principal Investigator |
HOSHIMIYA Nozomu Graduate School of Engineering, Tohoku University, Professor, 大学院・工学研究科, 教授 (50005394)
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| Co-Investigator(Kenkyū-buntansha) |
KANOH Shin'ichiro Graduate School of Engineering, Tohoku University, Research Associate, 大学院・工学研究科, 助手 (00282103)
WATANABE Takashi Graduate School of Engineering, Tohoku University, Assistant Professor, 大学院・工学研究科, 講師 (90250696)
FUTAMI Ryoko Graduate School of Engineering, Tohoku University, Associate Professor, 大学院・工学研究科, 助教授 (20156938)
HANDA Yasunobu New Industry Creation Hatchery Center, Tohoku University, Professor, 未来科学技術共同研究センター, 教授 (00111790)
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| Project Period (FY) |
1998 – 2000
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| Keywords | functional electrical stimulation / cuff electrode / electrode noise / detection of nerve action potentials / classification of nerve action potentials / multivariate analysis / hierarchical cluster analysis |
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| Research Abstract |
The functional electrical stimulation (FES) is used to restore the loss of voluntary motor function caused by the spinal cord injury or the cerebral apoplexy. Less effective for restoration in the presence of the disturbance and muscle fatigue because of open-loop control system are problems of the current FES system. For developing closedloop FES system with natural sensors, we studied biocompatible and low noise electrodes, and method extraction of neural information from peripheral nerves.
Fatigue properties and noise characteristic of stainless steel SUS316L, Co-Cr based alloy NAS604PH and newly developed high nitrogen high manganese austenitic stainless steel NAS 106N were investigated. We found that each electrode did not generate excess noise, and NAS106N had higher fatigue life than SUS316L and NAS604PH in both air and saline solution.
A model of peripheral nerve was developed. We found that the distance between Ranvier's node and the recording electrode influenced on the recorded waveforms of nerve action potentials and it was possible to identify activity of each nerve fiber based on the recorded waveforms even if diameter and property of membrane of each fiber were similar and each fiber was close in nerve trunk. For clinical application of closed-loop FES system with natural sensors, the recording device must be placed stably and give minor damage to nerve fiber. Cuff electrode satisfies these requirements. However, it sometimes provides recordings whose signal-to-noise ratio is too low to detect action potentials. We used wavelet transform to detect action potentials under low signal-to-noise ratio condition. Our method provided reliable result. Moreover, we proposed the method for classification of action potentials to estimate single-unit activity with cluster analysis and the criterion for deciding the number of groups that action potentials were classified into. It provided reliable result.
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