The Internet of Things (IoT) holds big promises for healthcare, especially in proactive personal eHealth. Prediction of symptomatic crises in chronic diseases in the IoT scenario leads to the deployment of ambulatory monitoring systems. These systems place a major concern in the amount of data to be processed and the intelligent management of the energy consumption. The huge amount of data generated for these systems require high computing capabilities only available in Data Centers. This paper presents a real case of prediction in the eHealth scenario, devoted to neurological disorders. The presented case study focuses on the migraine headache, a disease that affects around 15% of the European population. This paper extrapolates results from real data and simulations in a study where migraine patients are monitored using an unobtrusive Wireless Body Sensor Network. Low-power techniques are applied in monitorization nodes. Techniques such us: on-node signal processing and radio policies to make node's autonomy longer and save energy, have been applied. Workload balancing policies are carried out in the coordinator nodes and Data Centers to reduce the computational burden in these facilities and minimize its energy consumption. Our results draw average savings of (sic) 288 million in this eHealth scenario applied only to 2% of European migraine sufferers; in addition to savings of (sic) 1272 million due to the benefits of the migraine prediction. (C) 2017 Elsevier B.V. All rights reserved.