Key result
A hybrid firefly and particle swarm optimization method combined with a Levenberg Marquardt Neural Network classifier was proposed for the diagnosis of Bundle Branch Block.
Why the study?
Does a hybrid firefly and particle swarm optimization algorithm with a neural network classifier improve the automated diagnosis of Bundle Branch Block?
Does a hybrid firefly and particle swarm optimization algorithm with a neural network classifier improve the automated diagnosis of Bundle Branch Block?
A novel computational approach using hybrid firefly and particle swarm optimization with a neural network classifier is proposed for the automated diagnosis of bundle branch block from ECG signals.
May aid automated ECG diagnosis of bundle branch block; leaves open prospective clinical validation.
Anomalistic cardiac beat in identification is the key process in the diagnosis of the ailments of the heart. These works suggest the strategy for the diagnosis of Bundle Branch Block utilising the hybrid firefly and particle swarm type optimisation modus operandi in the amalgam with Leven Berg Marquardt Neural Network classifier. One among the major administering forces herein is the appealing of the light intensity of FFA type algorithm, which models the solution in an optimum manner. The hybrid method of FFPSI, which does associates the notion basically from the FF type algorithm and PSO does spawns newer individuals. An appropriate method is worked hence future scope can be realised even.
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Ballabh et al. (2017) studied Bundle Branch Block. Hybrid firefly and particle swarm optimization with Levenberg Marquardt Neural Network classifier was evaluated on Diagnosis of Bundle Branch Block. A hybrid firefly and particle swarm optimization method combined with a Levenberg Marquardt Neural Network classifier was proposed for the diagnosis of Bundle Branch Block.
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