Multi-objective multi-optima ensemble binary optimization algorithm for identifying optimal set of features for ECG-based identification

Reducing the number of input features for a machine learning model decreases its complexity and computation time. However, it is crucial to choose the best set of features without compromising the model's performance. There could be several subsets of features with optimal behavior. Evolutionary algorithms are great for feature optimization. However, different evolutionary algorithms may produce different solutions, and their performance is influenced by the size of the data and the types of features. To address these issues, three popular algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Binary Differential Evolution (BDE) have been adapted to accommodate multiple populations for achieving multiple optima. The BDE algorithm applied here is a novel variant with modified mutation and crossover operators. Then they are combined to create a novel 'Multi-Objective Multi-Optima Ensemble Binary Optimization Algorithm. The algorithm has been tested on 71 fiducial ECG features including temporal, amplitude, distance, slope, angular, and HRV features for ECG-based identification. These 71 features can identify individuals using the SVM classifier with 98 % accuracy. With 71 features, there could be a maximum of $2^{71}$ subsets. The optimization objective is to find all feature subsets that maximize classifier accuracy while minimizing the number of features. The ensemble optimizer has found 190 unique optimized subsets. These subsets have been analyzed to identify critical features for identification. The most optimal subset with the minimum number of features and maximum accuracy has been identified. The practical implementation of an ECG-based identification system requires an efficient system that can process incoming signals, extract features from the signal, and identify individuals in the shortest time possible. To speed up the processing of the input signal, a novel DFA-based algorithm has been proposed to identify fiducial points P, Q, R, S, and T from an ECG signal. The proposed algorithm applies to both recorded and live ECG signals.