Vagus nerve stimulation therapy for pharmacoresistant epilepsy: effect on health care utilization.

We retrospectively analyzed the effects of vagus nerve stimulation (VNS) therapy on utilization of medical services by 138 patients in a large staff-model health maintenance organization. We compared average quarterly rates for 12 months before device implantation with quarterly rates during 48 months of follow-up. Wilcoxon matched-pairs signed-ranks tests comparing pre-VNS with post-VNS utilization rates showed statistically significant reductions in numbers of emergency department visits, hospitalizations, and hospital lengths of stay, beginning with the first quarter after implantation (P<0.05 for all post-implantation quarters for these three aspects). For the first two quarters after implantation, the average number of outpatient visits was significantly greater than the pre-implant quarterly average (quarter 1: P<0.0001; quarter 2: P=0.0067), but the average was 12.2% less by the fourth quarter of the first year after implantation and significantly less beginning with the first quarter of the second year (P=0.0017) and continuing through the end of the study (P<0.0001 for all subsequent quarters). A comparison of time spent on epilepsy-related tasks during the year before implantation with the year after implantation also revealed significant decreases in the average number of days on which patients could not work because of health-related concerns, from 3.67 to 1.04 days (P=0.002, paired Student's t test) and the average time spent caring for health problems, from 352.6 to 136.1 minutes per week (P<0.001). VNS therapy had a positive effect on both the utilization of health care services and the time spent on epilepsy-related tasks for these patients with pharmacoresistant epilepsy.

Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (ß) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform.

Alzheimer's disease (AD) is associated with deficits in a number of cognitive processes and executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power spectrum develop with the progression of AD. These features have been traditionally characterized with montage recordings and conventional spectral analysis during resting eyes-closed and resting eyes-open (EO) conditions. In this study, we introduce a single lead dry electrode EEG device which was employed on AD and control subjects during resting and activated battery of cognitive and sensory tasks such as Paced Auditory Serial Addition Test (PASAT) and auditory stimulations. EEG signals were recorded over the left prefrontal cortex (Fp1) from each subject. EEG signals were decomposed into sub-bands approximately corresponding to the major brain frequency bands using several different discrete wavelet transforms and developed statistical features for each band. Decision tree algorithms along with univariate and multivariate statistical analysis were used to identify the most predictive features across resting and active states, separately and collectively. During resting state recordings, we found that the AD patients exhibited elevated D4 (~4-8 Hz) mean power in EO state as their most distinctive feature. During the active states, however, the majority of AD patients exhibited larger minimum D3 (~8-12 Hz) values during auditory stimulation (18 Hz) combined with increased kurtosis of D5 (~2-4 Hz) during PASAT with 2 s interval. When analyzed using EEG recording data across all tasks, the most predictive AD patient features were a combination of the first two feature sets. However, the dominant discriminating feature for the majority of AD patients were still the same features as the active state analysis. The results from this small sample size pilot study indicate that although EEG recordings during resting conditions are able to differentiate AD from control subjects, EEG activity recorded during active engagement in cognitive and auditory tasks provide important distinct features, some of which may be among the most predictive discriminating features.