Strength, physical activity, and fasciculations in patients with ALS
ABSTRACT Fasciculations are a nearly universal feature in people with amyotrophic lateral sclerosis (ALS). The prognostic value of fasciculations remains uncertain. Twenty-four patients with ALS were evaluated for the effects of atrophy, limb weakness, disease duration, and physical activity on fasciculation frequency (as measured by surface electromyography and clinical counting). Variables were compared by multiple linear regression. As strength of the limb deteriorated, the number of fasciculations in the same limb increased, as long as physical activity was maintained or increased. Fasciculation frequency was not associated with the duration of ALS (r = 0.22; p = 0.30) and was independent of the degree of limb weakness (p>0.05) and limb atrophy (p>0.05). No prediction of disease duration could be made based on fasciculation frequency alone. Fasciculations therefore appear to have diagnostic, but not prognostic, utility in the care of people with ALS.
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ABSTRACT: We investigated the relationship between fasciculation potentials (FPs) and survival in patients with ALS. In 85 ALS patients, we prospectively performed needle EMG in five to seven muscles of each patient. The shape of the detected FPs was analyzed by inspection, and FPs with >4 phases were judged as complex FPs. We analyzed the correlation between complex FPs and survival period using the Cox proportional hazard model. Complex FPs were observed in 47 patients, more frequently in the muscles with normal strength or mild weakness. The presence of complex FPs was associated with shorter survival (hazard ratio 3.055; p=0.004). The greater the number of muscles with complex FPs, the shorter the survival and the faster the progression speed. Wide distribution of complex FPs is associated with shorter survival in ALS. Complex FPs are useful to predict prognosis of ALS patients and should be evaluated in the EMG examination.Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 11/2013; 125(5). DOI:10.1016/j.clinph.2013.10.052 · 2.98 Impact Factor
Chapter: Brain-Computer Interfaces[Show abstract] [Hide abstract]
ABSTRACT: Brain–computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see ). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2–4].Edited by Graimann, Bernhard and Pfurtscheller, Gert and Allison, Brendan, 01/2010: pages 65-78-78; Springer., ISBN: 978-3-642-02090-2
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ABSTRACT: Most Brain–Computer Interface (BCI) research aims at helping people who are severely paralyzed to regain control over their environment and to communicate with their social environment. There has been a tremendous increase in BCI research the last years, which might lead to the belief that we are close to a commercially available BCI applications to patients. However, studies with users from the future target group (those who are indeed paralyzed) are still outnumbered by studies on technical aspects of BCI applications and studies with healthy young participants. This might explain why the number of patients who use a BCI in daily life, without experts from a BCI group being present, can be counted on one hand.10/2010: pages 185-201;