Enhanced analysis of somatosensory evoked potentials at 20-30 milliseconds can predict neurological outcome after cardiac arrest.

OBJECTIVE: This study attempted to test the effectiveness of an enhanced analysis of the 20-30 ms complex of somatosensory evoked potentials, in predicting the short-term outcome of comatose survivors of out of hospital cardiac arrest and compare it with the current clinical practice. METHODS: Single-centre, prospective, observational study. Median nerve SSEP recording performed at 24-36 h post-return of spontaneous circulation. Recording was analysed using amplitude measurements of P25/30 and Peak-To-Trough of 20-30 ms complex and thresholds to decide P25/30 presence. Neurological outcome was dichotomised into favourable and unfavourable. RESULTS: 89 participants were analysed. 43.8% had favourable and 56.2% unfavourable outcome. The sensitivity, specificity, positive and negative predictive values of the present SSEP and favourable outcome were calculated. P25/30 presence and size of PTT improved positive predictive value and specificity, while maintained similar negative predictive value and sensitivity, compared to the current practice. Inter-interpreter agreement was also improved. CONCLUSIONS: Enhanced analysis of the SSEP at 20-30 ms complex could improve the short-term prognostic accuracy for short-term neurological outcome in comatose survivors of cardiac arrest. SIGNIFICANCE: Peak-To-Trough analysis of the 20-30 ms SSEP waveform appears to be the best predictor of neurological outcome following out of hospital cardiac arrest. It is also the easiest and most reliable to analyse.

Multiway array decomposition analysis of EEGs in Alzheimer's disease.

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer's disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied two state of the art multiway array decomposition (MAD) methods to extract unique features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE), and singular value decomposition (SVD) coupled to tensor unfolding. We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Characterization of EEGs in Alzheimer's disease using information theoretic methods.

The number of people that now go on to develop Alzheimer's disease (AD) and other types of dementia is rapidly rising. For maximum benefits from new treatments, the disease should be diagnosed as early as possible, but this is difficult with current clinical criteria. Potentially, the EEG can serve as an objective, first line of decision support tool to improve diagnosis. It is non-invasive, widely available, low-cost and could be carried out rapidly in the high-risk age group that will develop AD. Changes in the EEG due to the dementing process could be quantified as an index or marker. In this paper, we investigate two information theoretic methods (Tsallis entropy and universal compression algorithm) as a way to generate potentially robust markers from the EEG. The hypothesis is that the information theoretic makers for AD are significantly different to those of normal subjects. An attraction of the information theoretic approach is that, unlike most existing methods, there may be a natural link between the underlying ideas of information theoretic methods, the physiology of AD and its impact on brain functions. Data compression has not been investigated as a means of generating EEG markers before and is attractive because it does not require a priori knowledge of the source model. In this paper, we focus on the LZW algorithm because of its sound theoretical foundation. We used the LZW algorithm and Tsallis model to compute the markers (compression ratios and normalized entropies, respectively) from two EEG datasets.

Development and assessment of methods for detecting dementia using the human electroencephalogram.

This paper makes an outline case for the need for a low-cost, easy to administer method for detecting dementia within the growing at risk population. It proposes two methods for electroencephalogram (EEG) analysis for detecting dementia that could fulfil such a need. The paper describes a fractal dimension-based method for analyzing the EEG waveforms of subjects with dementia and reports on an assessment which demonstrates that an appropriate fractal dimension measure could achieve 67% sensitivity to probable Alzheimer's disease (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. An alternative method based on the probability density function of the zero-crossing intervals is shown to achieve 78% sensitivity to probable Alzheimer's disease and an estimated sensitivity to probable Vascular (or mixed) dementia of 35% (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. This compares well with other studies, reported by the American Academy of Neurology, which typically provide a sensitivity of 81% and specificity of 70%. The EEG recordings used to assess these methods included artefacts and had no a priori selection of elements "suitable for analysis." This approach gives a good prediction of the usefulness of the methods, as they would be used in practice. A total of 39 patients (30 probable Alzheimer's Disease, six Vascular Dementia and three mixed dementia) and 42 healthy volunteers were involved in the study. However, although results from the preliminary evaluation of the methods are promising, there is a need for a more extensive study to validate the methods using EEGs from a larger and more varied patient cohorts with neuroimaging results, to exclude other causes and cognitive scores to correlate results with severity of cognitive status.