A personalized and reconfigurable cyberphysicalsystem to handle multi-parametric data acquisition
and analysis for mobile monitoring of epileptic
patients
A. Bideaux, P. Anastasopoulou, S. Hey

A. Cañadas, A. FernandezInstitute for Information Processing Technology
Karlsruhe Institute of Technology (KIT)
Karlsruhe, Germany
andre.bideaux@kit.edu

Sensing and Control
S&C
Barcelona, Spain

Abstract—This poster proposes a personalized andreconfigurable cyberphysical system to handle multi-parametricdata acquisition and analysis for mobile monitoring of epileptic
patients.

needed or the proposed system was not appropriate for use ineveryday life. That’s why this poster proposes a personalized
and reconfigurable cyberphysical system to handle multiparametric data acquisition and analysis for mobile monitoringof epileptic patients.

Keywords—epilepsy; multi-parametric monitoring; EEG; ECG;
GSR; Home Gateway

I. INTRODUCTION
Epilepsy is one of the commonest and most serious of theincurable neurological disorders, affecting at least 50 million
people in the world [1]. Epileptic or non-epileptic seizuresinterfere with the life of affected people dramatically. E.g.
every day activities like driving a car or riding a bike canbecome severely dangerous when having a seizure.
Clinical state-of-the-art in monitoring epileptic patients is
inpatient video-EEG recording, but this method suffers fromsome serious disadvantages as it is rather expensive and withlow availability due to limited numbers of places for wholenight recordings. In addition to this the number and type of
epileptic seizures during in hospital recording could differ fromnormal behavior and the equipment used in clinical practice israther inconvenient for patients.
The best solution would be to enhance out-of-hospitalmonitoring for diagnosis and management of epilepsy andrelated brain disorders. Recent research has shown that ECG
monitoring can also be used for real-time epileptic seizure
detection [2], and that activity monitoring via accelerometry
and GSR monitoring can be used as extra context parameters[3-4], while monitoring epileptic patients.
Depending on the type of epilepsy, different brain and bodyparameters need to be assessed in order to monitor the patient’s
state of health at best and to adapt the medical treatmentaccordingly.
Different solutions for multi-parametric assessment ofphysiological signals have been developed during the lastyears. However, none of them were able to provide anintegrated platform for the assessment of all the parameters

II. SYSTEM ARCHITECTURE AND COMPONENTS
Fig. 1 is giving an overview of the whole systemarchitecture which can be divided in 3 main parts the sensors,the Home Gateway and the Personal Health Record (PHR).

Fig. 1. System archetecture

A. Sensors
In order to achieve the main requirement, the multiparametric monitoring, different sensors should be integrated inthe system. This includes not only the most relevant bio-signal
for epilepsy monitoring, the EEG but also ECG, GSR and apush button that have be proven to be important in mobileepilepsy monitoring. For this system the following sensors
were selected.
The EEG module (TrackitTM, Lifelines Ltd, Over Wallop,UK) is a mobile ambulatory device that can measure up to 32
channels with a sampling rate of 256 Hz.
The ECG module (ekgMove, movisens GmbH, Karlsruhe,Germany) is a single channel ECG recorder with a 12 bit
resolution and a sampling rate from 256 Hz to 1024 Hz. The
electrodes are integrated into a wearable chest strap, which islight, small and comfortable.

The galvanic skin response (GSR) module (edaMove,movisens GmbH, Karlsruhe, Germany) measures the skinconductance with a sampling rate of 32Hz.
The push button module (bioPLUX, PluX, Arruda dos
Vinhos, Portugal) is actually a device that collects up to 8
signals from various sensors and transmits the signals via
Bluetooth to a computer, where they can be viewed in realtime. For this system only one push button sensor is used.
B. Home Gateway
The Home Gateway consists mainly of three parts. The firstpart is the data fusion unit that collects all the data from thesensors and fuses it to a synchronized data stream. The second
part is the communication unit, it communicates with sensors,
the PHR server and the third part the Data Stream Management
System (DSMS). Finally, the DSMS and the Event Manager
use the data stream to perform online analysis and with it thedetection of events of special interest.
1) Data Fusion Unit:
To connect all the data, synchronize them and handle thedata stream a lean and open framework, which can bepersonalized and reconfigured for each patient, is needed.
xAffect is a software framework developed by the ResearchCenter for Information Technology, Karlsruhe, Germany. It
was developed in Java to fulfill real-time data processing, easy
integration of different data sources, easy integration ofalgorithms and data logging of raw as well as derived data [5].
Libraries for some common sensors already exist in xAffect.
To use a broad spectrum of biosignals, additional libraries for
bioPlux and TrackIT devices from the companies bioPLUX
and lifelines had to be implemented.
The data format which is being used is the unisens-format.
This is a universal and generic format suitable for recording
and archiving sensor data from various recording systems andwith various sampling frequencies [6].
2) Communication Unit:
The Communication Unit handles the communication withall components. It connects to the PHR server to download theprofile for a measurement. It uses the downloaded profile tosetup the Sensors and it stores the data stream received fromthe sensors and uploads it to the PHR server. Furthermore itconfigures the DSMS with a custom set of algorithms also
described in the profile downloaded from the PHR server.
3) DSMS and Event Manager:
The main objective of the DSMS is to be able to detect
alarms and warnings for pre-defined events. The module thatwill communicate events to the PHR is called the EventManager. One of the simplest but most imported events
detected by the online algorithms is the push button. This event
is triggered by pressing the marker button in the bioPLUX™
device.
The DSMS canalize the push button raw data to theStreamInsight™ core. The Framework contains a developedquery to detect alarm signals, if it successfully detects an alarm
condition; it notifies the Event Manager which builds andsends an XML message to the PHR. Also if the user has
defined more queries to detect alarms they would produce
appropriate output that would be handled within a definedobserver.
C. Personal Health Record (PHR)
The PHR server is an online server were the clinicians canaccess all the patient related data.
A customized set of sensors and also a customized set ofalgorithms can be defined for each patient. Furthermore all dataacquired by the system will be uploaded to the PHR server
continuously. Hence the Data can be downloaded and viewed
by the clinicians almost instantly from any point where they
have an internet connection. In case of events the PHR server
set a marker for the time of interest and can notify immediately
by sending an email or a SMS.
III. CONCLUSION
Different sets of sensors can be used customized to theneeds of clinicians and patients to acquire the necessary datafrom a specific patient to handle his epilepsy. Furthermore thesystem does not acquire unnecessary data that is useless for theclinicians by reducing the hardware to a minimum set ofsensors personalized for the patient.
The first tests under clinical conditions showed that thedeveloped system is not only more comfortable for the patients
since they can move more freely but also easy usable for theclinicians who were in charge of the data acquisition. In thefuture the System should be tested not only in clinicalenvironment but also in patients’ homes to verify the reliabilityof the system. Furthermore the Home Gateway which isrunning on a laptop at the moment should be replaced with asmartphone for providing an even more comfortable
monitoring.
ACKNOWLEDGMENT
This work has been co-funded by the EuropeanCommission within the European Union's Seventh Framework
Program ([FP7/2007-2013]) in the ARMOR project(http://www.armor-project.eu).
REFERENCES
[1]

[2]

[3]

[4]

[5]
[6]

Poh M.-Z., Loddenkemper T., Swenson N., Goyal S., Madsen J., Picard
R.: Continuous Monitoring of Electrodermal Activity During Epileptic
Seizures Using a Wearable Sensor. In: 32nd Annual International
Conference of the IEEE
EMBS Buenos Aires, Argentina (2010)
Massé F., Penders J., Serteyn A., Bussel M. van, Arends J.: Miniaturized
Wireless ECG-Monitor for Real-Time Detection of Epileptic Seizures.
In: Wireless Health’10, San Diego, USA (2010)
Nijsen, T. M., Arends, J. B., Griep, P. A., Cluitmans, P. J.: The potential
value of three-dimensional accelerometry for detection of motor seizures
in severe epilepsy. In: Epilepsy & Behavior 7, 1, 74–84 (2005)
Poh M.-Z., Loddenkemper T., Swenson N., Goyal S., Madsen J., Picard
R.: Continuous Monitoring of Electrodermal Activity During EpilepticSeizures Using a Wearable Sensor. In: 32nd Annual International
Conference of the IEEE EMBS Buenos Aires, Argentina (2010)
Schaaff K., Mueller L., Kirst M., http://www.xaffect.org/Kirst M., Ottenbacher J., www.unisens.org