  In this paper, a novel ECG monitoring approach based on IoT technology is
suggested. This paper proposes a routing system for IoT healthcare platforms
based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality
(REL). In addition, the Artificial Neural Network (ANN), Support Vector Machine
(SVM), and Convolution Neural Networks (CNNs)-based approaches for ECG signal
categorization were tested in this study. Deep-ECG will employ a deep CNN to
extract important characteristics, which will then be compared using simple and
fast distance functions in order to classify cardiac problems efficiently. This
work has suggested algorithms for the categorization of ECG data acquired from
mobile watch users in order to identify aberrant data. The Massachusetts
Institute of Technology (MIT) and Beth Israel Hospital (MIT/BIH) Arrhythmia
Database have been used for experimental verification of the suggested
approaches. The results show that the proposed strategy outperforms others in
terms of classification accuracy.
