High frequency of post-stroke seizures among West Africans

2021 ◽  
Vol 429 ◽  
pp. 119627
Author(s):  
Fred Sarfo ◽  
Mayowa Owolabi ◽  
Bruce Ovbiagele
2014 ◽  
Vol 125 ◽  
pp. S145
Author(s):  
R. Chieffo ◽  
F. Ferrari ◽  
P. Battista ◽  
E. Houdayer ◽  
A. Nuara ◽  
...  

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Wataru Kakuda ◽  
Masahiro Abo ◽  
Ryo Momosaki

Objective: It is difficult to stimulate leg motor areas with magnetic current using a figure-of-eight coil due to the deep anatomical location of the areas. However, a double cone coil is useful for stimulating deep brain regions. We postulated that the use of the same coil may allow repetitive transcranial magnetic stimulation (rTMS) to modulate the neural activity of the same areas. The purpose of this study is to investigate the effect of high-frequency rTMS applied over bilateral leg motor areas with a double cone coil on walking function after stroke. Materials and methods: Eighteen post-stroke hemiparetic patients with gait disturbances attended two experimental sessions with more than 24 hours apart, in a cross-over, double-blind paradigm. In one session, high-frequency rTMS of 10 Hz was applied over the leg motor area bilaterally in a 10-sec train using a double cone coil for 20 minutes (total 2,000 pulses). In the other session, sham stimulation was applied for 20 minutes at the same site. To assess walking function, walking velocity and Physiological Cost Index (PCI) were evaluated serially before, immediately after, and 10 and 20 minutes after each intervention. Results: The walking velocity was significantly higher for 20 minutes after stimulation in the high-frequency rTMS group than the sham group. PCI was lower in the high-frequency rTMS group than the sham group, but this was significant only immediately after stimulation. Conclusions: High-frequency rTMS of bilateral motor areas using a double cone coil can potentially improve walking function in post-stroke hemiparetic patients.


2010 ◽  
Vol 121 ◽  
pp. S167
Author(s):  
K. Hosomi ◽  
Y. Saitoh ◽  
H. Kishima ◽  
M. Hirata ◽  
S. Oshino ◽  
...  

2021 ◽  
Vol 429 ◽  
pp. 117660
Author(s):  
Giulia Gamberini ◽  
Enrico Matteoni ◽  
Claudio Solaro

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4372
Author(s):  
Jenny Carolina Castiblanco ◽  
Ivan Fernando Mondragon ◽  
Catalina Alvarado-Rojas ◽  
Julian D. Colorado

Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.


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