Myocardial Infarction Detection Based on Deterministic Learning

Our study proposes an automatic technique for the detection of MI using hybrid signal processing tools and deterministic learning theory. To begin with, the characteristic envelope and first derivative of a single-lead ECG signal are extracted using Shannon energy. By incorporating the Shannon energy envelope (SEE) in the phase portrait of an ECG signal, the non-linear system dynamics can be captured. In the second step, using fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) with the SEE of the ECG and its derivative, scale-aligned intrinsic mode components (IMFs) are generated. The two initial IMFs, which contain the most energy in the ECG signals and their derivatives, are considered the predominant IMFs and employed as features. There are significant differences between ECG signals produced by normal (healthy) and MI-related cardiac systems. The third step involves applying deterministic learning theory with neural networks to model, identify and classify ECG signals into two groups. As a final step, an evaluation of the effectiveness of the method is then performed on the PTB diagnostic ECG database, which comprises signals from 148 MI patients and 52 healthy controls. The average classification accuracy achieved with a cross-validation scheme of 10 folds is reported as 99.21 %. In conclusion, our results confirm the proposed features are consistent with ECG system dynamics, as well as complementing existing ECG features for automatic MI detection.