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Antonopoulos Christos P. - Cyberphysical Systems for Epilepsy and Related Brain Disorders: Multi-parametric Monitoring and Analysis for Diagnosis and Optimal Disease Management

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Cyberphysical Systems for Epilepsy and Related Brain Disorders: Multi-parametric Monitoring and Analysis for Diagnosis and Optimal Disease Management
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Antonopoulos Christos P / Voros Nikolaos S
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Springer International Publishing
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2015
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Introduction to ARMOR Project -- Introduction to Epilepsy and Related Brain Disorders -- Part I Epilepsy Medical Background -- Sleep Features and Underlying Mechanisms Related to Epilepsy and Its Long Term Monitoring -- Source-estimation from Non-invasive Recordings of Brain Electrical Activity in Sleep and Epilepsy -- Current Practices in Epilepsy Monitoring; Future Prospects and the ARMOR Challenge -- Data Management Processes -- System Architecture.;This book introduces a new cyberphysical system that combines clinical and basic neuroscience research with advanced data analysis and medical management tools for developing novel applications for the management of epilepsy. The authors describe the algorithms and architectures needed to provide ambulatory, diagnostic and long-term monitoring services, through multi parametric data collection. Readers will see how to achieve in-hospital quality standards, addressing conventional routine clinic-based service purposes, at reduced cost, enhanced capability, and increased geographical availability. The cyberphysical system described in this book is flexible, can be optimized for each patient, and is demonstrated in several case studies.

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Springer International Publishing Switzerland 2015

Nikolaos S. Voros and Christos P. Antonopoulos (eds.) Cyberphysical Systems for Epilepsy and Related Brain Disorders 10.1007/978-3-319-20049-1_1

1. Introduction to ARMOR Project

Nikolaos S. Voros 1 , Christos P. Antonopoulos 1, Michalis Koutroumanidis 2, 3, George K. Kostopoulos 4 and Andreas A. Ioannides 5

(1)

Embedded System Design and Application Laboratory, Department of Computer and Informatics Engineering, Technological Educational Institute of Western Greece, Patras, Greece

(2)

Department of Clinical Neurophysiology and Epilepsy, Guys and St Thomas NHS Foundation Trust, London, UK

(3)

Department of Academic Neurosciences, Kings College London, London, UK

(4)

Neurophysiology Unit, Department of Physiology, Medical School, University of Patras, Patras, Greece

(5)

Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, Cyprus

Nikolaos S. Voros

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Abstract

Epilepsies affect 12 % 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 patients 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.

1.1 Introduction

ARMOR project addresses the needs of the epileptic patient and healthcare professionals, aiming at the design and development of a non-intrusive Personal Health System (PHS) for the monitoring and analysis of epilepsy-relevant multi-parametric data (i.e. EEG, EOG, EMG, EKG, skin conductance data), and the documentation of the epilepsy related symptoms.

Epilepsy is a common, devastating and still incurable disorder (a detailed introduction in epilepsy is presented in Chap. ].

Current diagnostic methodologies to accurately diagnose epilepsy have limitations. The paroxysmal and multifaceted nature of the disease demand long-term multimodal monitoring which is a great burden to hospitals financial and human resources, while it is not always diagnostically effective. To face this challenge and observe seizures day or night at the patients own environment, ambulatory EEG is in practice for some time. However video-EEG at home which would further capture important behavioral features before, during and after the electrographic seizures are only recently being evaluated []. Sensors able to detect crucial autonomic, motor or other changes that cannot be appreciated by the video and the scalp EEG electrodes are seldom used in the EEG departments and when used a limited coverage is applied, while ambulatory EEG does not include such sensors. Therefore, there is a need for more accurate diagnosis of integrated seizure phenotype in individual patients, which will allow better understanding of underlying mechanisms, prediction (and alert) of time and type of seizure (and alert) and availability of medical assistance and advice.

ARMOR is visualized as an ambulatory monitoring system for diagnosis and management, limited, but optimally selected for each patient, scalp EEG covering and custom-designed multi-polygraphy (textile based EMG, body activity sensors, autonomic and other biological data such as blood pressure, temperature, sugar blood levels and O2 and CO2 saturation continuous monitoring). Diagnosis of a disease as multifactorial and unpredictable as epilepsy demands continuous observation and correlation analysis of as many parameters as possible of the patients brain, body and the environment. Such a system enables successful stepwise diagnosis i.e. Step -: Decision on whether the seizure belongs to Epilepsy or NEPE, 1a: What type of Epilepsy- classification, 1b: Which NEPEappropriate further diagnostic workout and treatment. Step 2 : Delineation of clinical and EEG expression of the epilepsy. 2a: Identification of different seizure types and mechanisms (for example a fall may be due to atonic, myoclonic, negative myoclonic or tonic muscle activity that can be monitored with chronically attached electromyography (EMG) electrodes, and therefore accurately diagnosed). 2b: Full clinical semeiology (identification of all clinical symptoms and signs); what are the constituents of a particular seizure (behavioral, muscle tone, autonomic, and other changes) and their timing.

We recognized in the above a major medical problem which can be solved with current advanced ICT technology and further advancement in data analysis, combined to benefit both the patient and the economy of the health care system. From the very beginning to the end of the project the theoretical background was elaborated and the effectiveness of the ARMOR sensors improved by targeted research work that proceeded in parallel with the steps described above. This research involves sophisticated analysis of existing data from expensive devices (that are not routinely available in clinics, e.g. multichannel EEG and/or MEG). As the project matured and new data were collected, the detailed analysis of selected subset of the ARMOR data were analyzed in detail, partly as prototypical examples or critical cases for diagnosis and classification and partly as part of the final evaluation of the ARMOR project.

Privacy and security [] also constitute major concerns within ARMOR. Different levels of security and privacy are considered depending on the type of data being managed and the type of function being executed/used. ARMOR particular privacy and security requirements are identified and enriched with regulatory constraints and societal requirements.

The next sections provide a brief overview of ARMOR project perspectives, and challenges as well as its main contributions both at medical and technological domain.

1.2 ARMOR Perspectives

The technology developed in ARMOR project offers on-line and off-line analysis of data with the help of medical databases and the patients medical file for the purpose of prediction and description/classification of seizures, prior to delivering to the patient appropriate alerts and treatment advises.

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