Classification of Cardiovascular Disease Using Feature Extraction and Artificial Neural Networks

Electrocardiogram (ECG) signals are used to identify cardiovascular disease. The availability of signal processing and neural networks techniques for processing ECG signals has inspired us to do research that consists of extracting features of an ECG signals to identify types of cardiovascular diseases. We distinguish between normal and abnormal ECG data using signal processing and neural networks toolboxes in Matlab. Data, which are downloaded from an ECG database, Physiobank, are used for training and testing the neural network. To distinguish normal and abnormal ECG with the significant accuracy, pattern recognition tools with NN is used. Feature Extraction method is also used to identify specific heart diseases. The diseases that were identified include Tachycardia, Bradycardia, first-degree Atrioventricular (AV), and second-degree Atrioventricular. Since ECG signals are very noisy, signal processing techniques are applied to remove the noise contamination. The heart rate of each signal is calculated by finding the distance between R-R intervals of the signal. The QRS complex is also used to detect Atrioventricular blocks. The algorithm successfully distinguished between normal and abnormal data as well as identifying the type of disease.