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

•In the smart healthcare applications, the arrhythmia classification in patients is performed using the ECG signals collected through the IoT nodes.•The arrhythmia classification is performed using the proposed Coy-Grey Wolf optimization-based deep convolution neural network (Coy-GWO-based Deep CNN) classifier from the ECG features.•The proposed Coy-Grey Wolf optimization is developed through hybridizing the social prevalence characteristics and the hierarchy-based hunting characteristics of the Canidae family.•The proposed Coy-GWO-based Deep CNN's classification accuracy is 95%, showing an improved performance than the existing techniques.