Key result
Hybrid firefly-PSO neural network detects bundle branch block with ~99% accuracy on ECG.
Why the study?
Abnormal cardiac beat identification is crucial for detecting heart ailments, and improved feature optimization methods for ECG patterns are needed to enhance detection of Bundle Branch Block.
Absolute Event Rate: 99.1% vs 93.9%
May enhance automated bundle branch block detection; leaves open prospective clinical validation before adoption.
Abnormal Cardiac beat identification is a key process in the detection of heart ailments.This work proposes a technique for the detection of Bundle Branch Block (BBB) using hybrid Firefly and Particle Swarm Optimization (FFPSO) technique in combination with Levenberg Marquardt Neural Network (LMNN) classifier.BBB is developed when there is a block along the electrical impulses travel to make heart to beat.ECG feature extraction is a key process in detecting heart ailments.Our present study comes up with a hybrid method combining the two meta-heuristic optimization methods, Firefly algorithm (FFA) and Particle Swarm Optimization (PSO), for feature optimization of ECG (BBB and normal) patterns.One of the major controlling forces is the light intensity attraction of FFA algorithm that models the optimum solution.The light intensity attraction process of the FFA algorithm depends on random directions for search, this may delay in achieving the global optimization solution.The hybrid technique FFPSO, integrates the concepts from FF algorithm and PSO and creates new individuals.In the FFPSO method the local search is performed through the modified light intensity attraction step with PSO operator.The FFPSO features are compared with the classical FF, PSO features.The FFPSO feature values are given as the input to the Levenberg Marquardt Neural Network (LM NN) classifier.It has been observed that the performance of the classifier is improved with the help of the optimized features.
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Kora et al. (2015) studied Bundle Branch Block (n=19,039). Hybrid Firefly and Particle Swarm Optimization (FFPSO) with Levenberg Marquardt Neural Network vs. Firefly Algorithm (FFA) and Particle Swarm Optimization (PSO) was evaluated on Classification accuracy. The hybrid Firefly and Particle Swarm Optimization algorithm combined with a Levenberg-Marquardt Neural Network achieved an accuracy of 99.1% in detecting bundle branch block from ECG beats.
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