A Collaborative Platform for Advancing Automatic Interpretation in ECG Signals.

Numerous papers report the efficiency of the automatic interpretation capabilities of commercial algorithms. Unfortunately, these algorithms are proprietary, and academia has no means of directly contributing to these results. In fact, nothing at the same stage of development exists in academia. Despite the extensive research in ECG signal processing, from signal conditioning to expert systems, a cohesive single application for clinical use is not ready yet. This is due to a serious lack of coordination in the academic efforts, which involve not only algorithms for signal processing, but also the signal acquisition equipment itself. For instance, the different sampling rates and the different noise levels frequently found in the available signal databases can cause severe incompatibility problems when the integration of different algorithms is desired. Therefore, this work aims to solve this incompatibility problem by providing the academic community with a diagnostic-grade electrocardiograph. The intention is to create a new standardized ECG signals database in order to address the automatic interpretation problem and create an electrocardiography system that can fully assist clinical practitioners, as the proprietary systems do. Achieving this objective is expected through an open and coordinated collaboration platform for which a webpage has already been created.

Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours.

Knowing exactly how much solar radiation reaches a particular area is helpful when planning solar energy installations. In recent years the use of renewable energies, especially those related to photovoltaic systems, has had an impressive up-tendency. Therefore, mechanisms that allow us to predict solar radiation are essential. This work aims to present results for predicting solar radiation using optimization with the Random Forest (RF) algorithm. Moreover, it compares the obtained results with other machine learning models. The conducted analysis is performed in Queretaro, Mexico, which has both direct solar radiation and suitable weather conditions more than three quarters of the year. The results show an effective improvement when optimizing the hyperparameters of the RF and Adaboost models, with an improvement of 95.98% accuracy compared to conventional methods such as linear regression, with 54.19%, or recurrent networks, with 53.96%, without increasing the computational time and performance requirements to obtain the prediction. The analysis was successfully repeated in two different scenarios for periods in 2020 and 2021 in Juriquilla. The developed method provides robust performance with similar results, confirming the validity and effectiveness of our approach.

A Simple Method to Measure Renal Function in Swine by the Plasma Clearance of Iohexol.

There is no simple method to measure glomerular filtration rate (GFR) in swine, an established model for studying renal disease. We developed a protocol to measure GFR in conscious swine by using the plasma clearance of iohexol. We used two groups, test and validation, with eight animals each. Ten milliliters of iohexol (6.47 g) was injected into the marginal auricular vein and blood samples (3 mL) were collected from the orbital sinus at different points after injection. GFR was determined using two models: two-compartment (CL2: all samples) and one-compartment (CL1: the last six samples). In the test group, CL1 overestimated CL2 by ~30%: CL2 = 245 ± 93 and CL1 = 308 ± 123 mL/min. This error was corrected by a first-order polynomial quadratic equation to CL1, which was considered the simplified method: SM = -47.909 + (1.176xCL1) - (0.00063968xCL1²). The SM showed narrow limits of agreement with CL2, a concordance correlation of 0.97, and a total deviation index of 14.73%. Similar results were obtained for the validation group. This protocol is reliable, reproducible, can be performed in conscious animals, uses a single dose of the marker, and requires a reduced number of samples, and avoids urine collection. Finally, it presents a significant improvement in animal welfare conditions and handling necessities in experimental trials.

Estimated Glomerular Filtration Rate in Renal Transplantation: The Nephrologist in the Mist.

BACKGROUND: Formulas do not estimate renal function with acceptable precision and accuracy. METHODS: We compared 51 creatinine-based and/or cystatin c-based formulas with a gold standard (iohexol plasma clearance) in 193 renal transplant recipients using concordance correlation coefficient, total deviation index, coverage probability and the error in chronic kidney disease (CKD) stage classification. RESULTS: No formula showed a concordance correlation coefficient greater than 0.90 (average for creatinine-based formulas: ∼0.70 and for cystatin c-based formulas: ∼0.85). A wide total deviation index was observed: approximately 70% (creatinine-based) and approximately 50% (cystatin c-based), indicating that 90% of the estimations showed bounds of error of ±70% or ±50%, respectively, compared with the gold standard. No formula included 90% of the estimations within a coverage probability of ±10%. Half the CKD stages classified by creatinine-based formulas were incorrect, mainly due to overestimation of renal function. One of 3 CKD stages diagnosed by cystatin c-based formulas was incorrect, with both overestimation and underestimation. Overall, the formulas showed very low precision and accuracy and a high degree of error in reflecting real renal function. CONCLUSIONS: In conclusion, formulas do not properly reflect renal function in kidney transplantation, which makes their use in clinical practice unreliable. Moreover, their use in clinical trials should be avoided.

Detection and measurement of the intracellular calcium variation in follicular cells.

This work presents a new method for measuring the variation of intracellular calcium in follicular cells. The proposal consists in two stages: (i) the detection of the cell's nuclei and (ii) the analysis of the fluorescence variations. The first stage is performed via watershed modified transformation, where the process of labeling is controlled. The detection process uses the contours of the cells as descriptors, where they are enhanced with a morphological filter that homogenizes the luminance variation of the image. In the second stage, the fluorescence variations are modeled as an exponential decreasing function, where the fluorescence variations are highly correlated with the changes of intracellular free Ca(2+). Additionally, it is introduced a new morphological called medium reconstruction process, which helps to enhance the data for the modeling process. This filter exploits the undermodeling and overmodeling properties of reconstruction operators, such that it preserves the structure of the original signal. Finally, an experimental process shows evidence of the capabilities of the proposal.

A new adaptive self-tuning Fourier coefficients algorithm for periodic torque ripple minimization in permanent magnet synchronous motors (PMSM).

A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM) Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component's harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.

Detecting abnormal vehicular dynamics at intersections based on an unsupervised learning approach and a stochastic model.

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.

Background subtraction approach based on independent component analysis.

In this work, a new approach to background subtraction based on independent component analysis is presented. This approach assumes that background and foreground information are mixed in a given sequence of images. Then, foreground and background components are identified, if their probability density functions are separable from a mixed space. Afterwards, the components estimation process consists in calculating an unmixed matrix. The estimation of an unmixed matrix is based on a fast ICA algorithm, which is estimated as a Newton-Raphson maximization approach. Next, the motion components are represented by the mid-significant eigenvalues from the unmixed matrix. Finally, the results show the approach capabilities to detect efficiently motion in outdoors and indoors scenarios. The results show that the approach is robust to luminance conditions changes at scene.