Optimized EEMD feature extraction using bio-inspired optimization algorithms from electrocardiogram signals

Electrocardiogram (ECG) signal analysis is crucial for diagnosing heart conditions. The empirical Mode Decomposition (EMD) technique is quite effective in analyzing non-stationary signals. However, it has the inherent problem of mode mixing. To overcome this, the Ensemble Empirical Mode Decomposition (EEMD) method incorporates noise with known variance, utilizes the ensemble nature of EMD and enhances the decomposition process. This paper proposes a novel method for extracting features using EEMD to make its parameters independent. The intrinsic mode functions (IMFs) extracted from EEMD may vary depending on the parameters used. In contrast, EMD exhibits parameter independence, which ensures greater consistency. To obtain consistent results from EEMD without sacrificing its advantages over EMD, different bioinspired optimization techniques have been employed. Once consistent IMFs are generated, amplitude modulation (AM) and frequency modulation (FM) signals within each IMF are distinguished. Finally, the retrieved bandwidth of the AM/FM signals is utilized as feature vectors. These features are then evaluated using two well-established classifiers like Support Vector Machine (SVM) and Decision Tree (DT). The respective classifier accuracy levels of 91% and 98.94% were achieved using published datasets. The result shows the efficiency of the proposed feature extraction techniques.