Hybrid optimized convolutional neural network for efficient classification of ECG signals in healthcare monitoring

Abstract

For the prediction of cardiovascular diseases, a wireless sensor network platform that employs a wireless sensor network-enabled electrocardiogram telemetry system will involve the following steps: the electrocardiogram signal’s acquisition, the electrocardiogram signal’s processing, and alerting the physician in case of any emergencies. This system will aid the physician in early as well as an accurate analysis of heart diseases. Currently, a wireless sensor network enabled electrocardiogram monitoring system is being developed for analyzing the electrocardiogram signal. In the past, the convolutional neural networks have been employed for resolving diverse artificial intelligence problems with good outcomes. Even so, designing the convolutional neural networks architecture continues to be a cumbersome as well as meticulous procedure that needs the field experts’ involvement. This work has explored the neuro-evolution application to the automatic design of convolutional neural networks topologies and also has devised a novel solution which is based on the Artificial Bee Colony as well as the Grey Wolf Optimizer. Moreover, this work has given the proposal of the hybrid Grey Wolf Optimizer-Artificial Bee Colony algorithm, in which the wolves will adopt the bees’ strategy of sharing information for promotion of their exploration capability whilst continuing with their original strategy of hunting strategy for maintenance of their exploitation capability. The experimental outcomes demonstrate the proposed algorithm’s superior performance over other existing algorithms.