CAAos platform: an integrated platform for analysis of cerebral hemodynamics data.

Objective.The purpose of this article is to introduce readers to the concept and structure of the CAAos (CerebralAutoregulationAssessmentOpenSource) platform, and provide evidence of its functionality.Approach.The CAAos platform is a new open-source software research tool, developed in Python 3 language, that combines existing and novel methods for interactive visual inspection, batch processing and analysis of multichannel records. The platform is scalable, allowing for the customization and inclusion of new tools.Main results.Currently, the CAAos platform is composed of two main modules, preprocessing (containing artefact removal, filtering and signal beat to beat extraction tools) and cerebral autoregulation (CA) analysis modules. Two methods for assessing CA have been implemented into the CAAos platform: transfer function analysis (TFA) and the autoregulation index (ARI). In order to provide validation of the TFA and ARI estimates derived from the CAAos platform, the results were compared with those derived from two other algorithms. Validation was performed using data from 28 participants, corresponding to 13 acute ischemic stroke patients and 13 age- and sex-matched control subjects. Agreement between estimates was assessed by intraclass correlation coefficient and Bland-Altman analysis. No significant statistical difference between the algorithms was found. Moreover, there was an excellent correspondence between the curves of all parameters analysed, with the intraclass correlation coefficient ranging from 0.98 (95%CI 0.976-0.999) to 1.00 (95%CI 1 -1). The mean differences revealed a very small magnitude bias indicating an excellent agreement between the estimates.Significance.As open-source software, the source code for the software is freely available for noncommercial use, reducing barriers to performing CA analysis, allowing inspection of the inner-workings of the algorithms, and facilitating networked activities with common standards. The CAAos platform is a tailored software solution for the scientific community in the cerebral hemodynamic field and contributes to the increasing use and reproducibility of CA assessment.

EASY score (Eloquent, Age and baseline SYmptoms score) for outcome prediction in patients with acute ischemic stroke.

OBJECTIVE: A pragmatic tool for the early and reliable prediction of recovery in patients with acute ischemic stroke is needed. We aimed to test the addition of brain eloquent areas involvement in variables predicting poor outcome, using a simple scoring system. METHODS: Retrospective study of patients with anterior circulation acute ischemic stroke treated with best medical treatment and/or endovascular reperfusion. Primary outcome measure was 3-months poor outcome (mRs 3-6). We developed a prognostic model based on clinical data and a quantitative scoring system of the main eloquent brain areas involved on early follow-up CT, and analyzed its accuracy to predict poor outcome comparatively to three other prognostic models. The final model was used to develop a score for outcome prediction based on the multivariable analysis. RESULTS: A total of 197 patients were included (poor outcome = 62; mean age 67 ± 15.1 years; 44% females). Independent predictors of poor outcome were increasing age (p < 0.001), baseline NIHSS (p = 0.03), and the involvement of two brain areas: posterior limb of internal capsule (p < 0.001) and postero-superior corona radiata (p < 0.001). This model showed to be the most accurate to predict poor outcome (Balance Accuracy = 77.74%; C-Statistic = 0.891). The derived risk score attributing points for each of these variables (EASY score) showed similar performances (Balance Accuracy = 82.11%; C-Statistic = 0.90). CONCLUSION: The EASY score is an easy-to-apply and accurate tool to predict the 3-months functional outcome after ischemic stroke, relying on simple clinical features and the assessment of two key eloquent brain areas on early follow-up CT.

The ACE1 Electrical Impedance Tomography System for Thoracic Imaging.

The design and performance of the ACE1 (Active Complex Electrode) electrical impedance tomography system for single-ended phasic voltage measurements is presented. The design of the hardware and calibration procedures allow for reconstruction of conductivity and permittivity images. Phase measurement is achieved with the ACE1 active electrode circuit which measures the amplitude and phase of the voltage and the applied current at the location at which current is injected into the body. An evaluation of the system performance under typical operating conditions includes details of demodulation and calibration and an in-depth look at insightful metrics, such as signal-to-noise ratio variations during a single current pattern. Static and dynamic images of conductivity and permittivity are presented from ACE1 data collected on tank phantoms and human subjects to illustrate the system's utility.

A Review of Electrical Impedance Tomography in Lung Applications: Theory and Algorithms for Absolute Images.

Electrical Impedance Tomography (EIT) is under fast development, the present paper is a review of some procedures that are contributing to improve spatial resolution and material properties accuracy, admitivitty or impeditivity accuracy. A review of EIT medical applications is presented and they were classified into three broad categories: ARDS patients, obstructive lung diseases and perioperative patients. The use of absolute EIT image may enable the assessment of absolute lung volume, which may significantly improve the clinical acceptance of EIT. The Control Theory, the State Observers more specifically, have a developed theory that can be used for the design and operation of EIT devices. Electrode placement, current injection strategy and electrode electric potential measurements strategy should maximize the number of observable and controllable directions of the state vector space. A non-linear stochastic state observer, the Unscented Kalman Filter, is used directly for the reconstruction of absolute EIT images. Historically, difference images were explored first since they are more stable in the presence of modelling errors. Absolute images require more detailed models of contact impedance, stray capacitance and properly refined finite element mesh where the electric potential gradient is high. Parallelization of the forward program computation is necessary since the solution of the inverse problem often requires frequent solutions of the forward problem. Several reconstruction algorithms benefit by the Bayesian inverse problem approach and the concept of prior information. Anatomic and physiologic information are used to form the prior information. An already tested methodology is presented to build the prior probability density function using an ensemble of CT scans and in vivo impedance measurements. Eight absolute EIT image algorithms are presented.

Dynamic imaging in electrical impedance tomography of the human chest with online transition matrix identification.

One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on electrical potential measurements at its boundary generated by an imposed electrical current distribution into the boundary. One of the methods used in dynamic estimation is the Kalman filter. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, poor tracking ability of the extended Kalman filter (EKF) is achieved. An analytically developed evolution model is not feasible at this moment. The paper investigates the identification of the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model transition matrix is identified using the history of the estimated resistivity distribution obtained by a sensitivity matrix based algorithm and a Newton-Raphson algorithm. To numerically identify the linear evolution model, the Ibrahim time-domain method is used. The investigation is performed by numerical simulations of a domain with time-varying resistivity and by experimental data collected from the boundary of a human chest during normal breathing. The obtained dynamic resistivity values lie within the expected values for the tissues of a human chest. The EKF results suggest that the tracking ability is significantly improved with this approach.