Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space. (July 2019) - Barmpoutis, Panagiotis; Dimitropoulos, Kosmas; Apostolidis, Anestis; Grammalidis, Nikos
Highlights: A novel methodology for automated myocardial infarction detection and localization. Modeling of multi-lead ECG through a higher-order LDS and the projection of LDS parameters to a Grassmann manifold. VLAD encoding of multi-lead ECG signal after a dyadic DWT and multiscale higher-order SVD analysis on sub-band tensors. Two fusion approaches for mapping the extracted feature representations in a common Hilbert space. Abstract: Background and objective: Electrocardiogram is commonly used as a diagnostic tool for the monitoring of cardiac health and the detection of possible heart diseases. However, the procedure followed for the diagnosis of heart abnormalities is time consuming and prone to human errors. Thus, the development of computer-aided techniques for the automatic analysis of electrocardiogram signals is of vital importance for the diagnosis and prevention of heart diseases. The most serious outcome of coronary heart disease is the myocardial infarction, i.e., the rapid and irreversible damage of cardiac muscles, which, if not diagnosed and treated in time, continues to damage further the myocardial structure and function. In this paper we propose a novel approach for the automatic detection and localization of myocardial infarction from multi-lead electrocardiogram signals. Methods: The proposed method initially reshapes the multidimensional signal into a third-order tensor structure and subsequently extracts feature representations in both Euclidean and Grassmannian space. In addition, two different methods are proposed for the mapping of the two different feature representations into a common Hilbert space before the final classification of signals. The first approach is based on the mapping of both Grassmannian and Euclidean features in a Reproducing Kernel Hilbert Space (RKHS), while the second one attempts to initially apply Vector of Locally Aggregated Descriptors (VLAD) encoding directly to Grassmann manifold and then concatenate the two VLAD representations. Results: For the evaluation of the proposed method, we have conducted extensive tests using a publicly available dataset, namely PTB Diagnostic ECG database, containing 549 multi-lead ECG data recordings from 290 subjects and from different diagnostic classes. The method provides an excellent detection rate of 100%, and localization rate, i.e., 100% with the first fusion method and 99.7% with the second one. Conclusions: The Experimental results presented in this paper show the superiority of the proposed methodology against a number of state-of-the-art approaches. The main advantage of the proposed approach is that it exploits better the intercorrelations between signals of different ECG leads, by extracting feature representations that lie in different geometrical spaces and contain complementary information with regard to the dynamics of signals.