# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 1 Introduction  
With the economic prosperity and technological progress, wireless and mobile healthcare applications are proliferating [1]. Internet of things devices are widely used in human health monitoring. Due to the characteristics of streaming data, heterogeneous and high noise [2], the efficient analysis and diagnosis of health data face many challenges. The maturity of cloud computing and deep learning technology provides strong support for solving this problem. Nevertheless, there are still some problems in the application of deep learning model based on cloud platform for real time health monitoring. First, continuous transmission of health data will consume a lot of network resources. Second, the return time of cloud decision is uncertain due to the fluctuation of network transmission. Last, users' health data are stored in the cloud, which will involve personal privacy issues. Therefore, data-intensive analysis in smart healthcare requires a new computational model to provide location-aware and delay sensitive monitoring with intelligent and reliable control [3]. Electrocardiogram (ECG) is one of the most commonly used examinations in clinic. Remote ECG monitoring plays an important role in early diagnosis and prevention of cardiovascular diseases. This usually requires low-cost, high-convenience and low-latency services for ECG users. With the application of deep learning in the field of ECG, the automatic detection of ECG has made great progress [4]. At present, most of the training data used by researchers are MIT-BIH [5] and other public datasets, which have certain limitations in data volume and category. For example, the MIT-BIH dataset is sufficient in a few kinds of heart disease data, while the category of disease data is not enough to meet the needs of deep learning model training. As a result, research on ECG data is far from reaching the practical application. In this work, we take advantage of both edge and cloud to design an efficient health monitoring architecture. We use convolutional neural networks (CNN) to develop a streamlined and efficient model to identify ECGs on the edge. We noticed the imbalance of ECG data and its influence on diagnostic accuracy. Specifically, the contributions of this work can be summarized as follows. Firstly, a new hybrid intelligent healthcare architecture based on edge computing and cloud computing, named EdgeCNN, is proposed. This architecture can flexibly learn medical data from edge devices. Deep learning model is deployed to run on edge devices, which makes analysis and diagnosis closer to Input/Output(IoT) data sources. This can significantly relieve learning latency and network I/O and reduce the pressure of large user groups and massive data on the cloud platform. Secondly, relying on the EdgeCNN architecture, we design an efficient ECG edge computing diagnosis model and learning algorithm. Then, we successfully deploy it on edge devices. The model can infer ECG in real time closer to data source and get a good balance between diagnosis accuracy and resource loss. Experimental results show that, compared with pure cloud computing architecture, EdgeCNN can not only ensure reasonable accuracy, but also have obvious advantages in diagnosis delay, network I/O, application availability and resource cost. More importantly, it can effectively protect the privacy of user data from IoT devices. Thirdly, we propose a method to enhance ECG by deep convolutional generative adversarial network (DCGAN) [6]. DCGAN is a stable network architecture based on CNN extension, which has a very high credibility in unsupervised learning. Data enhancement can provide enough data quantity and category for ECG diagnosis. The experimental results show that, after the data enhancement of ECG data related categories, not only the overall accuracy of the deep learning model is improved, but also the ECG categories that the model can diagnose are greatly expanded, which greatly improves the practical usability of the system.