Influence of spinal cord stimulation on evoked potentials by cutaneous electrical stimulation

In the past, limited research has been done to investigate the influence of spinal cord stimulation (SCS) for treatment of chronic pain on evoked potentials (EP). Further insight into the mechanism of SCS may provide explanations for unsatisfactory results with this therapy in certain subpopulations. It also might predict effectiveness of SCS. In previous research MEG responses were measured on median and tibial nerve stimulations in chronic pain patients with and without SCS (1). However, this stimulation method preferentially activates large myelinated proprioceptive fibres, leaving painrelated small fibres unrelated. We expect that the observation of pain processing is impaired by large amounts of non-painrelated activity

Predicting success of vagus nerve stimulation (VNS) from interictal EEG

AbstractPurposeVagus nerve stimulation (VNS) has shown to be an effective treatment for drug resistant epilepsy in numerous patients, however, not in all. It is still not possible to predict which patients will profit from VNS. In this pilot study, we explore predictive interictal EEG features for seizure reduction after VNS.Methods19 Patients with medically refractory epilepsy and an implanted VNS system were included. Interictal EEG registrations, recorded before implantation, were retrospectively analysed. A quantative symmetry measure, the pair wise derived brain symmetry index (pdBSI), was tested to predict VNS outcome. Reduction in seizure frequency was used to define the responders.Results10 Patients did respond to VNS, of whom 7 patients had a seizure reduction of at least 50% in a follow-up period of 2 years. On average, we find higher pdBSI values for delta, theta, alpha and beta bands for non-responders than for responders. The average pdBSI of the theta and alpha bands could significantly discriminate between responders and non-responders.ConclusionIn this study, quantifying EEG symmetry using the pdBSI shows promising results in predicting the reduction of seizure frequency after VNS treatment

A novel approach for computer assisted EEG monitoring in the adult ICU

Objective The implementation of a computer assisted system for real-time classification of the electroencephalogram (EEG) in critically ill patients. Methods Eight quantitative features were extracted from the raw EEG and combined into a single classifier. The system was trained with 41 EEG recordings and subsequently evaluated using an additional 20 recordings. Through visual analysis, each recording was assigned to one of the following categories: normal, iso-electric, low voltage, burst suppression, slowing, and EEGs with generalized periodic discharges or seizure activity. Results 36 (88%) recordings from the training set and 17 (85%) recordings from the test set were classified correctly. A user interface was developed to present both trend-curves and a diagnostic output in text form. Implementation in a dedicated EEG monitor allowed real-time analysis in the intensive care unit (ICU) during pilot measurements in four patients. Conclusions We present the first results from a computer assisted EEG interpretation system, based on a combination of eight quantitative features. Our system provided an initial, reasonably accurate interpretation by non-experts of the most common EEG patterns observed in neurological patients in the adult ICU. Significance Computer assisted EEG monitoring may improve early detection of seizure activity and ischemia in critically ill patients