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IoT based arrhythmia classification using the enhanced hunt optimization-based deep learning
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DOI：10.1111/exsy.13298.png)
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Abstract
En 中文
The advancement of information technology, the Internet of Things (IoT), and several miniaturize equipment's enhances the healthcare field that provides real-time patient monitoring, which helps to provide medication anywhere and anytime. However, accurate detection is still a challenging task for which an effective classification model is introduced in this research. The proposed method is the Enhanced Hunt optimization based Deep convolutional neural network (Enhanced Hunt based-Deep CNN), in which the Enhanced Hunt optimization algorithm (EHOA) is developed by fusing the hunting habit of the predator and the herding characteristics of herding dog for enhancing the global optimal convergence. Here, the ECG signal from the individuals is collected using the IoT network and stored in the Hospital server, which is accessed by the doctor when requested, the classification is performed using the Enhanced Hunt based-Deep CNN and the performance revealed the effectiveness with the accuracy, sensitivity, and specificity of 95.33%, 94.92%, and 97.57%.
Keywords:
arrhythmia
deep learning
ECG signals
IoT
optimization
Journal
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2.3
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2.5K
Citations:
3.8K