Mode by Mode Classification of Congestive Heart Failure from Long Term HRV Analysis
In this work a new approach for automatic discrimination of Congestive Heart Failure (CHF) from Normal Sinus Rhythm (NSR) using long-term Heart Rate Variability (HRV) signals is presented. Unlike classical approaches where features are extracted from the input HRV signal, in the present work the hidden information of the signal is retrieved from its different narrow-band components. These components are extracted using the Multivariate Empirical Modes Decomposition (MEMD). Being fully data driven approach, the MEMD is well suited for processing non-stationary signals such as HRV signals. The idea is to identify physically meaningful components that convey pertinent information about the CHF. From these components statistical and geometrical features extracted and combined. We investigate the potential of the higher order statistics (Skewness and Kurtosis) combined with the Hjorth and Poincare plot parameters as features for HRV classification. Different combinations of these features are analyzed, and feature vectors are selected as an input into the binary Support Vector Machines (SVM) classifier. To evaluate the performance of the proposed strategy, experimental tests have been conducted. Results of classification show an accuracy rate around 100%. We expected that the proposed method would be helpful, in clinical diagnosis, to discriminate CHF patients from NSR subjects using long-term HRV recordings.
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