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 (R(2) = 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 successfully 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.

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.

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 epoch's 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.

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 subject's 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 subject's physical constitution. This fact could explain the lack of agreement in the literature about REG interpretation.

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.

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

Hidden Markov models have shown promising results for identification of spike sources in Parkinson's 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.

Sensitivity of rheoencephalographic measurements to spatial brain electrical conductivity.

Rheoencephalography (REG) is impedance plethysmography applied to the head, and provides an indirect measurement of the pulsatility of the cerebral blood volume. To extend REG as a clinical and research tool, it is necessary to evaluate the sensitivity of REG measurement to local brain conductivity changes. By means of the analytical solution of a four-sphere geometrical model of the head, maps of impedance sensitivity were assessed for different electrode arrangements. Results showed a selective distribution of sensitivities, with a preference for cortical areas under electrodes. This suggests a potential for application of REG to regional evaluation of cortical cerebral perfusion.

Extraction of the intracranial component from the rheoencephalographic signal: a new approach.

The well-known inherent artifact on the rheoencephalogram (REG) caused by the pulsatility of the scalp blood flow left the REG out of the clinical practice. In fact, depending on the selected electrode arrangement, the measurement of the brain impedance changes time-locked with the heartbeat can be completely buried on that of the scalp. In this work, a novel mathematical method based on the physiological differences between the brain and scalp perfusions is proposed to extract the intracranial information from REG. This method is experimentally applied to REG signals recorded at five electrode positions and results are compared with those derived from our previous theoretical works. Intracranial components extracted from the REG signals are consistent with the stated hypothesis and reproduce the unexpected results obtained with our theoretical models. Although further studies would be needed, the evidences found in this work suggest that the method proposed in this work extracts the intracranial information from the REG signal.