Computing and Processing on the Edge: Smart Pathology Detection for Connected Healthcare

With the progress of new generation wireless communication technology and machine learning algorithms to deal with big data, a variety of smart systems are realized to bring comfort to human life. Smart healthcare systems are one of the important developments recently. Such systems will become a necessary ingredient in our connected living. In this article, we propose a new smart pathology detection system using deep learning, edge computing, and cloud computing. Sensors will capture electroencephalogram (EEG) signals of a person and send the signals to a nearby edge computing server. The server will distribute a preprocessing step to available edge devices. The preprocessed signal will then be sent to a cloud computing server. In the cloud server, a proposed tree-based deep model will extract deep features from the EEG signal. The classified decision of whether the signal belongs to a normal person or a pathological person will be distributed to the stakeholders.