In-vivo measurements of human brain tissue conductivity using focal electrical current injection through intracerebral multicontact electrodes.

In-vivo measurements of human brain tissue conductivity at body temperature were conducted using focal electrical currents injected through intracerebral multicontact electrodes. A total of 1,421 measurements in 15 epileptic patients (age: 28 ± 10) using a radiofrequency generator (50 kHz current injection) were analyzed. Each contact pair was classified as being from healthy (gray matter, n = 696; white matter, n = 530) or pathological (epileptogenic zone, n = 195) tissue using neuroimaging analysis of the local tissue environment and intracerebral EEG recordings. Brain tissue conductivities were obtained using numerical simulations based on conductivity estimates that accounted for the current flow in the local brain volume around the contact pairs (a cube with a side length of 13 mm). Conductivity values were 0.26 S/m for gray matter and 0.17 S/m for white matter. Healthy gray and white matter had statistically different median impedances (P < 0.0001). White matter conductivity was found to be homogeneous as normality tests did not find evidence of multiple subgroups. Gray matter had lower conductivity in healthy tissue than in the epileptogenic zone (0.26 vs. 0.29 S/m; P = 0.012), even when the epileptogenic zone was not visible in the magnetic resonance image (MRI) (P = 0.005). The present in-vivo conductivity values could serve to create more accurate volume conduction models and could help to refine the identification of relevant intracerebral contacts, especially when located within the epileptogenic zone of an MRI-invisible lesion. Hum Brain Mapp 38:974-986, 2017. © 2016 Wiley Periodicals, Inc.

Fall detection algorithm in energy efficient multistate sensor system.

Health issues for elderly people may lead to different injuries obtained during simple activities of daily living (ADL). Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. Many fall detection systems are proposed but only recently such health care systems became available. Nevertheless sensor design, accuracy as well as energy consumption efficiency can be improved. In this paper we present a single 3-axial accelerometer energy-efficient sensor system. Power saving is achieved by selective event processing triggered by fall detection procedure. The results in our simulations show 100% accuracy when the threshold parameters are chosen correctly. Estimated energy consumption seems to extend battery life significantly.

Denoising depth EEG signals during DBS using filtering and subspace decomposition.

In difficult epileptic patients, the brain structures are explored by means of depth multicontact electrodes [stereoelectroencephalography (SEEG)]. Recently, a novel diagnostic technique allows an accurate definition of the epileptogenic zone using deep brain stimulation (DBS). The stimulation signal propagates in the brain and thus it appears on most of the other SEEG electrodes, masking the local brain electrophysiological activity. The objective of this paper is the DBS-SEEG signals detrending and denoising in order to recover the masked physiological sources. We review the main filtering methods and put forward an approach based on the combination of filtering with generalized eigenvalue decomposition (GEVD). An experimental study on simulated and real SEEG shows that our approach is able to separate DBS sources from brain activity. The best results are obtained by an original singular spectrum analysis-GEVD approach.

Extraction of deep brain stimulation (DBS) source in SEEG using EMD and ICA.

In the context of drug resistant partial epilepsy, intra-cerebral electrical stimulation (Deep Brain Stimulation) constitutes one of the means of investigation to locate epileptic volume. This exogenous source can then activate the underlying epileptic networks and generate an electrophysiological reaction. The purpose of this work is to estimate and eliminate the overlapping electrical stimulation signal in order to subsequently explore the provoked underlying electrical activity. We propose here several methods to tackle this problem, using two different approaches based on different assumptions: BSS approach based on Independent Component Analysis (ICA) and non parametric decomposition - empirical modes decomposition (EMD) algorithms.

Propagation of electrical field in the brain using electrical intra-cerebral stimulations.

For drug resistant partial epilepsy, intra-cerebral electrical stimulation (Deep Brain Stimulation--DBS) constitutes one of the means to locate epileptic volume. This paper investigates, in the framework of source localization problem, the propagation of the electrical field and current density distribution induced in the brain during in vivo electrical stimulation. There are three objectives in this work: to validate the propagation model for different large frequencies, to highlight the problem of the close field with the DBS source and to show the influence of the proximity to the skull on the results. We compared the Stereo-EEG data, recorded during DBS, with those obtained using: (i) the simplest model, the dipolar model in an infinite homogeneous medium, (ii) a more realistic approach with a numerical method, the Boundary Element Method (BEM). Studies on ten subjects with 234 stimulations showed that the dipole model could be used in the brain far from the skull in direction of dipole moment but that BEM was more appropriate close to the skull.