scholarly journals Advanced 56 Channels Stimulation System to Drive Intrafascicular Electrodes

Author(s):  
T. Guiho ◽  
D. Andreu ◽  
V. M. López-Alvarez ◽  
P. Cvancara ◽  
A. Hiairrassary ◽  
...  
2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Milan Djilas ◽  
Christine Azevedo-Coste ◽  
David Guiraud ◽  
Ken Yoshida

Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.


1981 ◽  
Vol 20 (03) ◽  
pp. 169-173
Author(s):  
J. Wagner ◽  
G. Pfurtscheixer

The shape, latency and amplitude of changes in electrical brain activity related to a stimulus (Evoked Potential) depend both on the stimulus parameters and on the background EEG at the time of stimulation. An adaptive, learnable stimulation system is introduced, whereby the subject is stimulated (e.g. with light), whenever the EEG power is subthreshold and minimal. Additionally, the system is conceived in such a way that a certain number of stimuli could be given within a particular time interval. Related to this time criterion, the threshold specific for each subject is calculated at the beginning of the experiment (preprocessing) and adapted to the EEG power during the processing mode because of long-time fluctuations and trends in the EEG. The process of adaptation is directed by a table which contains the necessary correction numbers for the threshold. Experiences of the stimulation system are reflected in an automatic correction of this table. Because the corrected and improved table is stored after each experiment and is used as the starting table for the next experiment, the system >learns<. The system introduced here can be used both for evoked response studies and for alpha-feedback experiments.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Imad Libbus ◽  
Scott R. Stubbs ◽  
Scott T. Mazar ◽  
Scott Mindrebo ◽  
Bruce H. KenKnight ◽  
...  

Abstract Background Vagus Nerve Stimulation (VNS) delivers Autonomic Regulation Therapy (ART) for heart failure (HF), and has been associated with improvement in cardiac function and heart failure symptoms. VNS is delivered using an implantable pulse generator (IPG) and lead with electrodes placed around the cervical vagus nerve. Because HF patients may receive concomitant cardiac defibrillation therapy, testing was conducted to determine the effect of defibrillation (DF) on the VNS system. Methods DF testing was conducted on three ART IPGs (LivaNova USA, Inc.) according to international standard ISO14708-1, which evaluated whether DF had any permanent effects on the system. Each IPG was connected to a defibrillation pulse generator and subjected to a series of high-energy pulses. Results The specified series of pulses were successfully delivered to each of the three devices. All three IPGs passed factory electrical tests, and interrogation confirmed that software and data were unchanged from the pre-programmed values. No shifts in parameters or failures were observed. Conclusions Implantable VNS systems were tested for immunity to defibrillation, and were found to be unaffected by a series of high-energy defibrillation pulses. These results suggest that this VNS system can be used safely and continue to function after patients have been defibrillated.


2018 ◽  
Vol 18 (16) ◽  
pp. 6812-6821 ◽  
Author(s):  
Yu Zhou ◽  
Yinfeng Fang ◽  
Kai Gui ◽  
Kairu Li ◽  
Dingguo Zhang ◽  
...  

2016 ◽  
Vol 38 (11) ◽  
pp. 1232-1243 ◽  
Author(s):  
C. Klauer ◽  
S. Ferrante ◽  
E. Ambrosini ◽  
U. Shiri ◽  
F. Dähne ◽  
...  

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