Electrocardiogram (ECG) and Cardiac Condition Detection
An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity versus time. It is an important diagnostic tool for assessing heart functions. Artificial neural networks (ANNs) are generally presented as systems of interconnected "neurons", most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. In this book, pattern recognition techniques are used for the interpretation of an ECG signal. The techniques used in this pattern recognition application are, signal pre-processing, QRS detection, feature extraction and artificial neural network for signal and cardiac condition (healthy or not) classification. The signal processing and neural network toolbox are used in MATLAB. The ECG samples were pre-processed, then features representing the each sample were extracted to produce a set of features that can be used in a neural network to make the classification of samples and the recognition rates were recorded. The book is focused on finding an easy but reliable feature extraction method and best neural network structure to correctly classify different cardiac conditions.