Multidimensional Analysis toward the Identification of ECG Nonlinear Dynamics
Using ECG it is possible to detect the rate and regularity of heartbeats and identify possible irregularities to the heart activity. In this paper, a method to classify normal and two types of abnormal ECG signals is introduced. In the first stage of described process, techniques used to extract a number of potential classification parameters evaluated from 2 minutes long ECG signal epochs are described. The extracted parameters can be generally divided into three groups: (i) standard statistical signal parameters, (ii) nonlinear parameters and (iii) specific heart rate variability parameters. Two dimensionality reduction algorithms, principal component analysis (PCA) and linear discriminant analysis (LDA), have been employed in order to reduce the size of dataset containing ECG parameters and followed by a clustering algorithm. The results show the ability of this method to detect different pathologies and to distinguish normal ECG behaviour from pathological ones. Furthermore, this approach could be implemented in real time applications and embedded in a portable device and this effort is the first step towards the final realization of an automatic solution for the real-time characterization and monitoring of heart signals.