IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier

An electrocardiogram (ECG) is extensively used to evaluate the heart condition that can lead to further investigate heart ailments detection of heart diseases. The process is simple, quick, and non-invasive. However, manually detecting and classifying arrhythmia is not easy, as manually analyzing the ECG signals is timeconsuming. This research proposes an automatic detection and classification of arrhythmia using the proposed optimized deep learning classifier. Initially, the ECG signals collected using the Internet of Things (IoT) nodes are processed to generate the QRS complex and the RR interval for establishing the feature vector for the arrhythmia classification, which is done using the proposed Coy-Grey Wolf optimization-based deep convolution neural network (Coy-GWO-based Deep CNN) classifier that detects the anomalies in the ECG signal. The proposed CoyGWO algorithm inherits the hybrid characteristics, such as social hunting hierarchies and the hunting experiences of the canids, which ensures the effective parameter update in the classifier. Finally, the classification model is implemented and analyzed based on the performance measures. The proposed Coy-GWO-based Deep CNN attained the classification accuracy of 95%, which outperforms the existing techniques.

Automated summary

This study proposes an automatic detection and classification method for arrhythmia using an optimized deep learning classifier. By collecting ECG signals using IoT nodes and establishing feature vectors through Coy-GWO-based Deep CNN classifier, anomalies in the ECG signal can be detected, achieving a classification accuracy of 95%.