Design and development of an impedimetric-based system for the remote monitoring of home-based dialysis patients.

A key clinical challenge is to determine the desired 'dry weight' of a patient in order to terminate the dialysis procedure at the optimal moment and thus avoid the effects of over- and under-hydration. It has been found that the effects of haemodialysis on patients can be conveniently monitored using whole-body bioimpedance measurements. The identified need of assessing the hydrational status of patients undergoing haemodialysis at home gave rise to the present Dialydom (DIALYse à DOMicile) project. The aim of the project is to develop a convenient miniaturised impedance monitoring device for localised measurements (on the calf) in order to estimate an impedimetric hydrational index of the home-based patient, and to transmit this and other parameters to a remote clinical site. Many challenges must be overcome to develop a robust and valid home-based device. Some of these are presented in the paper.

Estimation of physical activity monitored during the day-to-day life by an autonomous wearable device (SVELTE project).

Physical activity (PA) and the energy expenditure it generates (PAEE) are increasingly shown to have impacts on everybody's health (e.g. development of chronic diseases) and to be key factors in maintaining the physical autonomy of elderlies. The SVELTE project objective was to develop an autonomous actimeter, easily wearable and with several days of autonomy, which could record a subject's physical activity during his/her daily life and estimate the associated energy expenditure. A few prototypes and dedicated algorithms were developed based on laboratory experiments. The identification of physical activity patterns algorithm shows good performances (79% of correct identification), based on a trial in semi-free-living conditions. The assessment of the PAEE computation algorithm is under validation based on a clinical trial.

Classification of epileptic motor manifestations and detection of tonic-clonic seizures with acceleration norm entropy.

In this paper, three triaxis accelerometers positioned on the wrists and the head of epileptic patients submitted to long-term video electroencephalographic monitoring as part of presurgical investigation are evaluated to characterize the different classes of motor manifestations observed during seizures. Quadratic discriminant classifiers are trained on features extracted from 1 or 4 s windows. It is shown that a simple rule applied to the acceleration norm entropy HnA produces the best performances compared to other classifiers trained on other feature sets. The simple rule is as follows with values given in bits: (0 HnA 1.34), no movement; (1.34 HnA 3.87), tonic manifestations; (3.87 HnA), tonic-clonic manifestations. For this classifier, features are extracted from 1 s windows and the misclassification rate is 11% evaluated on 5,607 s of epileptic motor manifestations obtained from 58 seizures in 30 patients. A quantile normalization can improve the results with features based on absolute power spectral density but performances are not as good as the ones obtained with HnA. Based on the classifier using only HnA, a simple tonic-clonic seizure detector is proposed and produces a 80% sensitivity with a 95% specificity.

Neural adaptation to responsive stimulation: a comparison of auditory and deep brain stimulation in a rat model of absence epilepsy.

BACKGROUND: Responsive deep brain stimulation (rDBS) has been recently proposed to block epileptic seizures at onset. Yet, long-term stability of brain responses to such kind of stimulation is not known. OBJECTIVE: To quantify the neural adaptation to repeated rDBS as measured by the changes of anti-epileptic efficacy of bilateral DBS of the substantia nigra pars reticulata (SNr) versus auditory stimulation, in a rat model of spontaneous recurrent absence seizures (GAERS). METHODS: Local field potentials (LFP) were recorded in freely moving animals during 1 h up to 24 h under automated responsive stimulations (SNr-DBS and auditory). Comparison of seizure features was used to characterise transient (repetition-suppression effect) and long-lasting (stability of anti-epileptic efficacy, i.e. ratio of successfully interrupted seizures) effects of responsive stimulations. RESULTS: SNr-DBS was more efficient than auditory stimulation in blocking seizures (97% vs. 52% of seizures interrupted, respectively). Sensitivity to minimal interstimulus interval was much stronger for SNr-DBS than for auditory stimulation. Anti-epileptic efficacy of SNr-DBS was remarkably stable during long-term (24 h) recordings. CONCLUSIONS: In the GAERS model, we demonstrated the superiority of SNr-DBS to suppress seizures, as compared to auditory stimulation. Importantly, we found no long-term habituation to rDBS. However, when seizure recurrence was frequent, rDBS lack anti-epileptic efficacy because responsive stimulations became too close (time interval < 40 s) suggesting the existence of a refractory period. This study thus motivates the use of automated rDBS in patients having transient seizures separated by sufficiently long intervals.

Classification of epileptic motor manifestations using inertial and magnetic sensors.

In order to characterize objectively the succession of movements observed during motor seizures, inertial and magnetic sensors were placed on epileptic patients. Video recordings synchronized with motion recordings were analyzed visually during seizures and divided, for each limb, into events corresponding to different classes of motor manifestations. For each classified event, features were extracted and a subset selection was automated using artificial neural networks. The best artificial neural network was simulated on whole recordings to generate a stereotypic evolution of motor manifestations that we called motorograms. It is shown that motorograms can point out seizure movements and emphasize epileptic patterns.

BioMEA: a versatile high-density 3D microelectrode array system using integrated electronics.

Microelectrode arrays (MEAs) offer a powerful tool to both record activity and deliver electrical microstimulations to neural networks either in vitro or in vivo. Microelectronics microfabrication technologies now allow building high-density MEAs containing several hundreds of microelectrodes. However, dense arrays of 3D micro-needle electrodes, providing closer contact with the neural tissue than planar electrodes, are not achievable using conventional isotropic etching processes. Moreover, increasing the number of electrodes using conventional electronics is difficult to achieve into compact devices addressing all channels independently for simultaneous recording and stimulation. Here, we present a full modular and versatile 256-channel MEA system based on integrated electronics. First, transparent high-density arrays of 3D-shaped microelectrodes were realized by deep reactive ion etching techniques of a silicon substrate reported on glass. This approach allowed achieving high electrode aspect ratios, and different shapes of tip electrodes. Next, we developed a dedicated analog 64-channel Application Specific Integrated Circuit (ASIC) including one amplification stage and one current generator per channel, and analog output multiplexing. A full modular system, called BIOMEA, has been designed, allowing connecting different types of MEAs (64, 128, or 256 electrodes) to different numbers of ASICs for simultaneous recording and/or stimulation on all channels. Finally, this system has been validated experimentally by recording and electrically eliciting low-amplitude spontaneous rhythmic activity (both LFPs and spikes) in the developing mouse CNS. The availability of high-density MEA systems with integrated electronics will offer new possibilities for both in vitro and in vivo studies of large neural networks.

Detection system of motor epileptic seizures through motion analysis with 3D accelerometers.

A system of epilepsy seizure detection in real life conditions and based on inertial sensors is presented in this paper with a focus on the signal processing to recognize seizure moves. This system is based on several models of signals, one corresponding to general movements, and two others describing seizures moves. The detection algorithm evaluates for a given time window which model fits the best with the observed signals and trigger an alarm if this model is a seizure model. The signal processing algorithm is based on hidden Markov models.

SIMONE: a realistic neural network simulator to reproduce MEA-based recordings.

Contemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy.

A new 3-D finite-element model based on thin-film approximation for microelectrode array recording of extracellular action potential.

A transient finite-element model has been developed to simulate an extracellular action potential recording in a tissue slice by a planar microelectrode array. The thin-film approximation of the active neuron membrane allows the simulation within single finite-element software of the intracellular and extracellular potential fields. In comparison with a compartmental neuron model, it is shown that the thin-film approximation-based model is able to properly represent the neuron bioelectrical behavior in terms of transmembrane current and potential. Moreover, the model is able to simulate extracellular action potential recordings with properties similar to those observed in biological experiments. It is demonstrated that an ideal measurement system model can be used to represent the recording microelectrode, provided that the electronic recording system adapts to the electrode-tissue interface impedance. By comparing it with a point source approximated neuron, it is also shown that the neuron three-dimensional volume should be taken into account to simulate the extracellular action potential recording. Finally, the influence of the electrode size on the signal amplitude is evaluated. This parameter, together with the microelectrode noise, should be taken into account in order to optimize future microelectrode designs in terms of the signal-to-noise ratio.

Wavelet-based scale-dependent detection of neurological action potentials.

We study different wavelet-based algorithms for the detection of neurological action potentials recorded using micro-electrode arrays (MEA). We plan to develop a new family of ASIC-embedded low power algorithms close to the recording sites. We use the wavelet theory, not for previous-to-the-detection denoising stage (as it is usually used for) but for the detection itself. Different adaptive methods are presented with varying complexity levels. We demonstrate that wavelet-based detection of extracellular action potentials is superior than traditional and simpler approaches, at the expense of a slightly larger computational load. Moreover, our method is shown to be fully compatible with an embedded implementation. Proposed algorithms are applied to simulated datasets using a simplified model of the American cockroach antennal lobe.

Collection and exploratory analysis of attitude sensor data in an epilepsy monitoring unit.

The aim of this paper is to present the collection of attitude sensor data from an epilepsy monitoring unit and the results of standard exploration using principal component analysis. The collection of data from attitude sensors positioned on three limbs of epileptic patients at their bedside is described. The analysis of the data focuses, on one hand, on motor features extraction from attitude sensor data and on the other hand, on visual segmentation of seizures into events corresponding to motor manifestations classes by an expert. Principal component analysis is then realized over these features and groups of data are localized according to the expert classification. This exploration indicates a possible discrimination between these motor manifestation classes.