0000000000303495
AUTHOR
J. V. Francés
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).
Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks
Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…
P and R Wave Detection in Complete Congenital Atrioventricular Block
Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using non-invasive electrocardiographic surface leads. The proposed algorithm is divided into three stages. In the first stage, the R waves located by a QRS detector are used to generate the RR series and time references for the other stages of the algorithm. In the second stage, the ventricular activity (QT segment) is removed by using an adaptive filter that obtains an averaged pa…
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 fo…
Fast spiking neural network architecture for low-cost FPGA devices
Spiking Neural Networks (SNN) consist of fully interconnected computation units (neurons) based on spike processing. This type of networks resembles those found in biological systems studied by neuroscientists. This paper shows a hardware implementation for SNN. First, SNN require the inputs to be spikes, being necessary a conversion system (encoding) from digital values into spikes. For travelling spikes, each neuron interconnection is characterized by weights and delays, requiring an internal neuron processing by a Postsynaptic Potential (PSP) function and membrane potential threshold evaluation for a postsynaptic output spike generation. In order to model a real biological system by arti…