M. Bataller

Modifications in the Evolution of the Dominant Frequency in Ventricular Fibrillation Induced by Amiodarone, Diltiazem, and Flecainide An Experimental Study

In 22 anesthetized mongrel dogs, spectral methods were used to analyze the surface electrocardiogram (ECG) for the time course of the dominant frequency in ventricular fibrillation and its modifications under the influence of amiodarone, diltiazem, and flecainide. The ECG was recorded over 5 minutes after triggering ventricular fibrillation. Following A/D conversion and by applying the Fourier fast transform, the frequency spectrum of the signals was obtained. In group 1 (5 dogs), the ECGs were obtained without prior drug administration; group 2 (5 dogs) first received amiodarone, 5 mg/kg; group 3 (7 dogs) received diltiazem, 0.2 mg/kg; and group 4 (5 dogs) received flecainide, 2 mg/kg. All drugs were administered intravenously. An initial increase in the dominant frequency of ventricular fibrillation was found in the control group and also in the groups that received amiodarone, diltiazem, or flecainide. Diltiazem significantly increased the dominant frequency and diminished the arrhythmia-slowing process. Amiodarone and flecainide tended to diminish the dominant frequency.

BioLab: An Educational Tool for Signal Processing Training in Biomedical Engineering

This paper introduces and evaluates BioLab, a tool for teaching biosignal processing. BioLab has been used in the biomedical engineering module that is given in the second semester of the fifth year of the electronic engineering degree at the University of Valencia, Spain. This module and its correspondent curricular pathway are also reviewed. BioLab allows the results obtained with digital processing techniques to be shown interactively in the theory classes, and it also provides support in laboratory sessions. The graphic interface of BioLab simplifies its learning and use and provides access to processing and visualization functions by means of menus. The tool is based on Matlab since the students have had previous experience in this environment. BioLab also supports diverse formats of data files, which facilitate access to real records and their conversion to usable formats. The modular structure of BioLab allows it to be easily extended to other educational materials that are related to signal processing and to research applications. An evaluation of BioLab has revealed that students found it useful for understanding the general concepts of digital processing and biosignal processing in particular. The students also found BioLab very easy to learn and use

Hardware-efficient matrix inversion algorithm for complex adaptive systems

This work shows an FPGA implementation for the matrix inversion algebra operation. Usually, large matrix dimension is required for real-time signal processing applications, especially in case of complex adaptive systems. A hardware efficient matrix inversion procedure is described using QR decomposition of the original matrix and modified Gram-Schmidt method. This works attempts a direct VHDL description using few predefined packages and fixed point arithmetic for better optimization. New proposals for intermediate calculations are described, leading to efficient logic occupation together with better performance and accuracy in the vector space algebra. Results show that, for a relatively small device as Xilinx Spartan3 XC3S1000, a matrix size up to 23 × 23 can be implemented, having a matrix inversion computation time of 253μs. Accuracy results compared to floating point computation and an estimation of required clock cycles as a function of matrix size are analyzed.

Causal relationships between neurons of the nucleus incertus and the hippocampal theta activity in the rat

The nucleus incertus is a key node of the brainstem circuitry involved in hippocampal theta rhythmicity. Synchronisation exists between the nucleus incertus and hippocampal activities during theta periods. By the Granger causality analysis, we demonstrated a directional information flow between theta rhythmical neurons in the nucleus incertus and the hippocampus in theta‐on states. The electrical stimulation of the nucleus incertus is also able to evoke a phase reset of the hippocampal theta wave. Our data suggest that the nucleus incertus is a key node of theta generation and the modulation network.

High performance hardware correlation coefficient assessment using programmable logic for ECG signals

Abstract Correlation coefficient is frequently used to obtain cardiac rhythm by peak estimation and appreciate differences in the signal compared to a pattern. This work focuses on the description of a real-time correlation assessment procedure. Applied to electrocardiogram (ECG) signals, a new correlation value is obtained every new sample and pulse detection information is provided. The ECG pattern is internally stored and can be changed when desired. This procedure is useful in Systems on Chip implementation and can be applied to design compact ECG monitoring systems consisting on a system on chip where programmable logic offloads the main processor. A Xilinx FPGA device has been used for prototyping.

Fast non-invasive ventricular fibrillation detection method using pseudo Wigner-Ville distribution

Detection of ventricular fibrillation (VF) at an early stage is crucial in order to lower the risk of sudden death and allow the specialist to have a greater reaction time to give the patient a good recovering therapy and avoid unrecoverable damage to the cardiac tissue. We present an algorithm oriented towards the real-time detection of VF, to be used as a part of monitoring systems in intensive care units or ambulatory centres. The study has been done using the AHA (American Heart Association) database, focusing mainly on the 8200 series, and the MIT (Massachussetts Institute of Technology) database. The detection algorithm combines both time-domain and time-frequency parameters. Using the appropriate parameters, the detection algorithm discerns between VF and non-VF rhythms, including VF-like rhythms like certain types of ventricular tachycardia (VT). A VF flutter sensitivity of 86% and an average specificity of 94.3 % (including VT separation) is obtained.

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity.

Implementation of a new adaptive algorithm using fuzzy cost function and robust to impulsive noise

Adaptive filters are used in a wide range of applications such as noise cancellation, system identification, and prediction. One of the main problems for theses filters is the impulsive noise as it generates algorithm unstability. This work shows the development, simulation and hardware implementation of a new algorithm robust to impulsive noise. Hardware implementation becomes essential in many cases where a real time execution, reduced size, or low power system is needed. An efficient hardware architecture is proposed and different optimizations for size and speed are developed: no need for control state machine, reduced computation requirements due to simplifications, etc. Furthermore, two different implementations were done to test two simplified cost functions. Finally, comparison results are provided to test accuracy, performance and logic occupation, showing an efficient architecture for impulsive noise robustness.

Synchrony Analysis of Unipolar Cardiac Mapping during Ventricular Fibrillation

Ventricular Fibrillation (VF) is one of the main causes of death in developed countries. Recent studies have shown that fibrillation have a complex organization scheme. This work uses three measures of synchrony to characterize three groups of rabbit hearts. These groups consist of rabbits trained with physical exercise (N=7), untrained rabbits treated with a drug (N=13) and a control group of untrained rabbits (N=15). Cardiac mapping records were acquired using a 240-electrode array placed on left ventricle of isolated rabbit hearts, and VF was induced pacing at increasing rates. Two acquisitions were performed: maintained perfusion, and ischemic damage produced by an artery ligation. The used measures are Spatial Correlation (SC), Coupling Index (CI) and Synchrony Index (SI), previously proposed to study fibrillation arrhythmias. These measures were used to quantify the synchrony level between a reference electrode and its neighbors at 4 increasing radiuses, increasing its electrode spacing. The significance of the effects of radius, group and its interaction was tested using the Generalized Estimating Equations methodology. The obtained results have shown that these synchrony measures provide a feasible way to characterize the three groups, where the effects of the interaction between group and radius (p<0.001), and the individual effect of the radius were statistically significant (p<0.01), both for maintained perfusion and with ischemic damage. All the groups decrease its synchrony as electrode spacing increases, as it was observed in the three used measurements. Within this perspective, the drugged group has shown the lowest synchrony reduction. The trained group have shown similarities with the control group.

Brain Activity Characterization Induced by Alcoholic Addiction: Spectral and Causality Analysis of Brain Areas Related to Control and Reinforcement of Impulsivity

Addiction to drugs generates modifications in the brain structure and its functions. In this work, an experimental model is described, using rats to characterize the brain activity induced by alcohol addiction. Four records were obtained using electrodes located in brain areas related to impulsivity control and reinforcement, i.e. the prelimbic (PL) and infralimbic (IL) cortex, together with the hippocampus (HPC). In the records, three main events related to the drinking action were selected: in the previous minute (T1), the first minute while drinking (T2) and the first minute after stopping drinking (T3).