Enrique Guijarro

The effect of fibrin glue patch in an in vitro model of postdural puncture leakage.

We studied the possibility of stopping a continuing transdural leakage with fibrin glue, a biologic adhesive, in an in vitro model.The model was made by sealing the bottom of a tube filled with saline to a height of 50 cm with a human lyophilized dural specimen. Dural punctures were performed with a 17-gauge Tuohy needle. The needle was then withdrawn, and 0.8 mL of fibrin glue was injected through the same needle to seal the defect. The column was refilled 3 min after sealing. The pressure in the intrathecal chamber was measured during the procedure. Macroscopic and microscopic histological studies of the dura and the fibrin plug were performed. In the five cases studied, the leak was sealed by the fibrin plug at closing pressures of 25-35 cm H2 O, and no further leakage was detected after refilling. The dural specimens showed a fibrin glue plug stuck at the edges of the hole. We conclude that fibrin glue stops leakage of fluid from dural holes created by a 17-gauge Tuohy needle in an in vitro pressurized model. Implications: We explored the possibility of repairing a cerebrospinal fluid leak produced by an accidental dural puncture during epidural anesthesia by percutaneously injecting tissue adhesive in vitro. This technique seems promising for the prophylaxis and treatment of the headache associated with this leakage but requires further study in vivo. (Anesth Analg 1998;87:1125-8)

Multisensor System for Isotemporal Measurements to Assess Indoor Climatic Conditions in Poultry Farms

The rearing of poultry for meat production (broilers) is an agricultural food industry with high relevance to the economy and development of some countries. Periodic episodes of extreme climatic conditions during the summer season can cause high mortality among birds, resulting in economic losses. In this context, ventilation systems within poultry houses play a critical role to ensure appropriate indoor climatic conditions. The objective of this study was to develop a multisensor system to evaluate the design of the ventilation system in broiler houses. A measurement system equipped with three types of sensors: air velocity, temperature and differential pressure was designed and built. The system consisted in a laptop, a data acquisition card, a multiplexor module and a set of 24 air temperature, 24 air velocity and two differential pressure sensors. The system was able to acquire up to a maximum of 128 signals simultaneously at 5 second intervals. The multisensor system was calibrated under laboratory conditions and it was then tested in field tests. Field tests were conducted in a commercial broiler farm under four different pressure and ventilation scenarios in two sections within the building. The calibration curves obtained under laboratory conditions showed similar regression coefficients among temperature, air velocity and pressure sensors and a high goodness fit (R2 = 0.99) with the reference. Under field test conditions, the multisensor system showed a high number of input signals from different locations with minimum internal delay in acquiring signals. The variation among air velocity sensors was not significant. The developed multisensor system was able to integrate calibrated sensors of temperature, air velocity and differential pressure and operated succesfully under different conditions in a mechanically-ventilated broiler farm. This system can be used to obtain quasi-instantaneous fields of the air velocity and temperature, as well as differential pressure maps to assess the design and functioning of ventilation system and as a verification and validation (V&V) system of Computational Fluid Dynamics (CFD) simulations in poultry farms.

Quantification of intracranial contribution to rheoencephalography by a numerical model of the head.

OBJECTIVES Partial contributions of intracranial and extracranial circulation to rheoencephalography (REG) remain uncertain. The main goal of this work is to determine theoretically the capability of REG techniques to reflect intracranial blood flow. METHODS Head and current injection electrodes were computationally modeled to assess REG sensitivity to brain and scalp conductivity changes. Data obtained were related to tissue perfusions to calculate the partial contribution of cerebral blood perfusion to REG I, REG II and monopolar REG and to assess their amplitudes. RESULTS When REG I and monopolar REG were used, the theoretical maximum of intracranial contribution was reached with large current injection electrodes, being 8% for REG I and 12% for monopolar REG. However, some specific REG II electrode arrangements showed a nil contribution of the extracranial circulation and a minimum influence of the electrode size. CONCLUSIONS These results may explain the disagreement on REG origin and suggest a theoretically optimum electrode arrangement.

Identification of epilepsy stages from ECoG using genetic programming classifiers

OBJECTIVE Epilepsy is a common neurological disorder, for which a great deal of research has been devoted to analyze and characterize brain activity during seizures. While this can be done by a human expert, automatic methods still lag behind. This paper analyzes neural activity captured with Electrocorticogram (ECoG), recorded through intracranial implants from Kindling model test subjects. The goal is to automatically identify the main seizure stages: Pre-Ictal, Ictal and Post-Ictal. While visually differentiating each stage can be done by an expert if the complete time-series is available, the goal here is to automatically identify the corresponding stage of short signal segments. METHODS AND MATERIALS The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days. RESULTS Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average classification error of 14.55%, sensitivity values between 0.65 and 0.97, and specificity values between 0.86 and 0.94. CONCLUSIONS The proposed approach achieves good results for stage identification, particularly when compared with previous works that focus on this task. The results show that the problem of intra-class classification can be solved with a low error, and high sensitivity and specificity. Moreover, the limitations of the approach are identified and good operating configurations can be proposed based on the results.

Influence of the scalp thickness on the intracranial contribution to rheoencephalography

In spite of the great efforts made by the scientific community, up to now there is no agreement about the rheoencephalography (REG) capability to reflect cerebral blood flow (CBF). Moreover, a standard procedure and the optimal electrode arrangement have not been established yet. In a previous study, we found, using a classical four-shell spherical model of the head and solving it by numerical methods that, theoretically, there could exist an electrode arrangement to register an REG II free of extracranial contribution. In this paper, we have studied the influence of scalp thickness on the intracranial contribution to REG II. The study has been performed by solving the head model, using in this case analytical methods, and then estimating the partial contribution of CBF pulsatility to REG for a given set of scalp thicknesses. Although our theoretical results validate the previous finding and suggest that, in some cases, an optimal electrode arrangement to register REG II exists, such an arrangement, and even its existence, is very sensitive to the subjects scalp thickness. According to this, there could not exist a universal electrode arrangement suitable for all individuals to register an REG II free of extracranial contribution, since it depends on the subjects physical constitution. This fact could explain the lack of agreement in the literature about REG interpretation.

Spatiotemporal pattern of the extracranial component of the rheoencephalographic signal

The use of rheoencephalography (REG) in the clinical practice to evaluate cerebral blood flow is conditional on the finding of a method for removing the extracranial interference caused by the scalp blood flow. To remove this undesirable influence, digital processing based on statistics could be an effective technique if the appropriate data model were applied. This paper focuses on the analysis of the spatiotemporal features of the extracranial REG component, by comparing its morphology and phase shift at several scalp sites. For this purpose, a numerical model of the scalp was employed to assess tissue impedance changes caused by the inflow of a stepwise blood pulse wave. These results were compared with the experimental impedance waveforms recorded on six pairs of adjacent electrodes. The correlation coefficients between each pair of impedance recordings of each subject were always greater than 0.942, showing a mean value of 0.986. This result suggests that the extracranial REG component can be considered as morphologically invariant. On the other hand, negligible phase shifts were observed when mean electrode distances, measured in the blood flow direction, were relatively small, although temporal corrections in the data model would be advisable for longer distances.

Identification of spike sources using proximity analysis through hidden Markov models.

Hidden Markov Models have shown promising results for identification of spike sources in Parkinsons disease treatment, e.g., for deep brain stimulation. Usual classification criteria consist in maximum likelihood rule for the recognition of the correct class. In this paper, we present a different classification scheme based in proximity analysis. For this approach matrices of Markov process are transformed to another space where similarities and differences to other Markov processes are better revealed. The authors present the proximity analysis approach using hidden Markov models for the identification of spike sources (Thalamo and Subthalamo sources, Gpi and GPe sources). Results show that proximity analysis improves recognition performance for about 5% over traditional approach

Seizure states identification in experimental epilepsy using gabor atom analysis

BACKGROUND Epileptic seizures evolve through several states, and in the process the brain signals may change dramatically. Signals from different states share similar features, making it difficult to distinguish them from a time series; the goal of this work is to build a classifier capable of identifying seizure states based on time-frequency features taken from short signal segments. METHODS There are different amounts of frequency components within each Time-Frequency window for each seizure state, referred to as the Gabor atom density. Taking short signal segments from the different states and decomposing them into their atoms, the present paper suggests that is possible to identify each seizure state based on the Gabor atom density. The brain signals used in this work were taken form a database of intracranial recorded seizures from the Kindling model. RESULTS The findings suggest that short signal segments have enough information to be used to derive a classifier able to identify the seizure states with reasonable confidence, particularly when used with seizures from the same subject. Achieving average sensitivity values between 0.82 and 0.97, and area under the curve values between 0.5 and 0.9. CONCLUSIONS The experimental results suggest that seizure states can be revealed by the Gabor atom density; and combining this feature with the epochs energy produces an improved classifier. These results are comparable with the recently published on state identification. In addition, considering that the order of seizure states is unlikely to change, these results are promising for automatic seizure state classification.

Epilepsy Ictal Stage Identification by 0-1 Test of Chaos

Abstract During an epilepsy seizure an Electrocorticosignals (ECoS) may change dramatically from a nearly disordered signal (Inter-Ictal stage) into a highly synchronized signal (Ictal stage), characterized by a high amplitude and low frequency components, and then suddenly goes back to the Inter-Ictal stage. However, identifying each stage from a time series is a non-trivial task. In particular, this work studies the identification of the Ictal stage during an epileptic episode. As most bioelectrical signals, an ECoS is a highly non-periodical and non-stationary signal. Moreover, ECoS from each seizure stage have their own features which characterize them. We identify the Ictal stage by analyzing short signal segments (epochs), based on the chaos like behavior shown by the signal. This was done by the application of the well known 0-1 Test of chaos. Signals are intracranially recorded from Wistar rats at cortex level, epileptic subjects of the Kindling model for whom seizures were elicited by electrical stimulation. Then as a result, we have a successfully identified the Ictal stage validated by the standard Diagnostic Test method.

Analysis and Classification of Epilepsy Stages with Genetic Programming

Epilepsy is a widespread disorder that affects many individuals worldwide. For this reason much work has been done to develop computational systems that can facilitate the analysis and interpretation of the signals generated by a patients brain during the onset of an epileptic seizure. Currently, this is done by human experts since computational methods cannot achieve a similar level of performance. This paper presents a Genetic Programming (GP) based approach to analyze brain activity captured with Electrocorticogram (ECoG). The goal is to evolve classifiers that can detect the three main stages of an epileptic seizure. Experimental results show good performance by the GP-classifiers, evaluated based on sensitivity, specificity, prevalence and likelihood ratio. The results are unique within this domain, and could become a useful tool in the development of future treatment methods.