Introduction to ARMOR Project

Abstract

Epilepsies affect 1–2 % of general population, especially in childhood and adolescence. Epileptic seizures, manifest with a wide range of paroxysmally recurring motor, cognitive, affective, and autonomic symptoms and EEG changes. Their recognition and full understanding is the basis of their optimal management. The yield of epilepsy diagnosis is considered unsatisfactory, as seizures occur unpredictably and typically outside hospital, other paroxysmal disorders are often misdiagnosed as epilepsy, and hospital evaluation costs of patients with uncertain clinical features or possibly mixed disorders are quite substantial. Reliable diagnosis requires state of the art monitoring and communication technologies providing real-time, accurate and continuous brain and body multi-parametric data measurements, suited to the patient’s medical condition and normal environment and facing issues of patient and data security, integrity and privacy.

In this context, a cornerstone objective of the ARMOR project was to manage and analyze a large number of already acquired and new multimodal and advanced technology data from brain and body activities of epileptic patients and controls (MEG, multichannel EEG, ECG, GSR, EMG, etc.) aiming to design a more holistic, personalized, medically efficient and economical monitoring system. New methods and tools have been developed for multimodal data pre-processing and fusion, real-time and offline data mining of multi-parametric streaming and archived data to discover patterns and associations between external indicators and mental states, lag correlation detection, identification of motifs or outliers (vital signs changing significantly), automatic summarization of results and efficient medical context data management. In addition to the technical advances, work within research produced significant clinical results and important new insights on the nature of sleep and its putative reciprocal relationship with sleep.