Premature ventricular beat detection by using spectral clustering methods

In this paper, we the look at the spectral properties of features extracted from segmented ECG signals containing Normal (N) and premature ventricular beats (V) prior to apply classification methods for reliable PVC detection. In a first stage, feature extraction based on signal basic analysis which computes not only intervals and amplitudes on each beat, but also description of wave morphology was performed. Extracted parameters that describe the basic shape of the beat such as: average wave amplitudes, durations and areas have been computed. In a second stage, the eigen decomposition of data allows finding structure in records which is optimal to attain high performance of classification. In a third stage, Support Vector Machines (SVM) which are benchmarked against several techniques have been chosen for PVC detection. By applying SVM Recursive Feature Elimination (SVM RFE) where the weight magnitude is used as ranking criterion we reduced the feature dimension to smaller sets. Then, with newly constructed dimension input features space we combine spectral clustering with SVM classifiers for attaining superior performance.