Automated classification of normal and premature ventricular contractions in electrocardiogram signals

The objective of this project was to improve the accuracy of cardiac arrhythmia detection by using advanced signal processing and machine learning methods. The proposed Computer-Aided Diagnosis (CAD) system classified Premature Ventricular Contraction (PVC) and normal Electrocardiogram (ECG) signals using unsupervised machine learning algorithms. The classification quality was measured and expressed as accuracy, Positive Predictive Value (PPV), sensitivity and specificity. The ECG records, which were used to establish the CAD system quality, were obtained from the MIT-BIH arrhythmia database. These signals were analyzed in four stages. The pre-processing stage standardized and improved the ECG signals by subjecting them to Discrete Wavelet Transform (DWT) based noise reduction. The second stage used Independent Component Analysis (ICA) for dimension reduction. The third stage assessed the extracted features with Student’s t-test to determine if the features were discriminative enough to serve as classifier input. At the last stage, two unsupervised classifiers, k-means and Fuzzy C-Means (FCM), were used to find clusters. The proposed system