Cyber-Physical Systems Design Flow to Manage Multi-channel Acquisition System for Real-Time Migraine Monitoring and Prediction

Chronic diseases represent the major health problems of the twenty-first century. These diseases kill 41 million people each year, equivalent to 71% of all deaths globally. The major chronic diseases listed by the World Health Organization are cardiovascular diseases, cancer, chronic respiratory diseases, diabetes mellitus, and neurodegenerative disorders. Monitoring and maintaining normal values for key health metrics play a primary role in reducing chronic disease risk. Powerful mechanisms based on prevention to combat the chronic disease crises are currently present and continue to evolve. A new healthcare delivery model is needed to implement these mechanisms effectively. This model implies the utilization of wearable devices connected to the Cloud, allowing continuous monitoring and prevention of chronic disease crises. Standard Modeling and Simulation (M&S) methodologies created to design Cyber-Physical Systems (CPS) and deploy them into the Cloud can help design and implement complex scenarios. In this chapter, we show the automatic CPS implementation process of a robust migraine prediction system that allows the generation of alarms before the appearance of new pain episodes. This method is used to implement the device in an FPGA and to study the scalability of the proposed infrastructure, the integration of the designed device into an Internet of Things (IoT) ecosystem is demonstrated.