Remote health monitoring is a powerful tool to provide
preventive care and early intervention for populations-at-risk. Such monitoring
systems are becoming available nowadays due to recent advancements in
Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These
systems require a high level of quality in attributes such as availability and
accuracy due to patients critical conditions in the monitoring. Deep learning
methods are very promising in such health applications to obtain a satisfactory
performance, where a considerable amount of data is available. These methods
are perfectly positioned in the cloud servers in a centralized cloud-based IoT
system. However, the response time and availability of these systems highly
depend on the quality of Internet connection. On the other hand, smart gateway
devices are unable to implement deep learning methods (such as training models)
due to their limited computational capacities. In our previous work, we proposed
a hierarchical computing architecture (HiCH), where both edge and cloud
computing resources were efficiently exploited, allocating heavy tasks of a
conventional machine learning method to the cloud servers and outsourcing the
hypothesis function to the edge. Due to this local decision making, the
availability of the system was highly improved. In this paper, we investigate
the feasibility of deploying the Convolutional Neural Network (CNN) based
classification model as an example of deep learning methods in this
architecture. Therefore, the system benefits from the features of the HiCH and
the CNN, ensuring a high-level availability and accuracy. We demonstrate a
real-time health monitoring for a case study on ECG classifications and
evaluate the performance of the system in terms of response time and accuracy.