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 …

Analysis of ventricular fibrillation signals using feature selection methods

Feature selection methods in machine learning models are a powerful tool to knowledge extraction. In this work they are used to analyse the intrinsic modifications of cardiac response during ventricular fibrillation due to physical exercise. The data used are two sets of registers from isolated rabbit hearts: control (G1: without physical training), and trained (G2). Four parameters were extracted (dominant frequency, normalized energy, regularity index and number of occurrences). From them, 18 features were extracted. This work analyses the relevance of each feature to classify the records in G1 and G2 using Logistic Regression, Multilayer Perceptron and Extreme Learning Machine. Three fea…

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 …

Feature Selection Methods to Extract Knowledge and Enhance Analysis of Ventricular Fibrillation Signals

ELM Regularized Method for Classification Problems

Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation o…

Prediction of Temperature in Buildings Using Machine Learning Techniques

Energy efficiency is a trend due to ecological and economic benefits. Within this field, energy efficiency in buildings sector constitutes one of the main concerns due to the fact that approximately 40% of total world energy consumption corresponds to this sector. Climate control in buildings has the potential to increase its energy efficiency planning strategies for the heating, ventilation and air conditioning (HVAC) machines. These planning strategies may include a stage for long term indoor temperature forecasting. This chapter entails the use of four prediction models (NAÏVE, MLR, MLP, FIS and ANFIS) to forecast temperature in an office building using a temporal horizon of several hour…