Optimized Deep Convolutional Neural Network for IoT Based Healthcare Platform

Abstract

The rapid development in the wearable sensors helped develop the healthcare-based Internet of Things (IoT) platforms for continuous monitoring of chronic disease. As per the World Health Organization (WHO) 85% of the Cardiovascular Disease (CVD) is preventable with proper monitoring for symptoms and awareness. The electrocardiogram (ECG) signal refers to a recording of electrical activity that is generated by the human heart. An analysis of the ECG signal helps in building models that can make an automatic classification of various CVDs. Deep neural network is found to be efficient to make an automatic classification of the primary signals of the ECG. These ECG signals to the arrhythmia classes were grouped by using the Convolutional Neural Networks (CNN). The 9 Layers of the CNN techniques are employed in state-of-the-art modern research to classify the signal. The Tabu Search (TS) algorithm with the CNN, Hybrid Optimization Algorithm known as the CNN with ABC-TS, and CNN with Artificial Bee Colony (ABC) is used to optimize the CNN in this work for classifying ECG signals. An evaluation of the technique was made on the basis of metrics of performance evaluation that are capable of producing enhanced outcomes.