Mobile monitoring of epileptic patientsusing a reconfigurable cyberphysical system thathandles multi-parametric data acquisition and analysis
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— Epilepsy is one of the commonest, serious anddivesting brain disorders. Although it is still an incurable
disorder in most cases its symptoms can be ameliorated by life long pharmaceutical treatment. Depending on the type ofepilepsy and due to its multifactorial causes, different brain andbody parameters need to be assessed continuously over a longperiod. This allows clinicians to have a better understanding of
the patient’s state of health and to be able to continuously adjust
and change the medical treatment accordingly. Beside this, multiparametric monitoring could be used for other purposes such as
accurate diagnosis, detection of seizures, alerting and prevention
and presurgical evaluation. The purpose of this paper is topresent the architecture of the whole cyberphysical system,comprising a modular framework that is able to connect all therequired sensor types and perform online data analysis for themonitoring of patients with epilepsy.

Although EEG is the most important biosignal whenmonitoring epileptic patients, 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]. Therefore, depending on the type ofepilepsy, different brain and body parameters need to beassessed in order to monitor the patient’s state of health at best
and to adapt the medical treatment accordingly.

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

INT RODUCT ION

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. Everyday
activities like driving a car or riding a bike can become
severely dangerous when having a seizure.
Clinical state-of-the-art in monitoring epileptic patients is
inpatient video-EEG recording. However, this method suffersfrom some serious disadvantages as it is rather expensive andwith low availability due to limited numbers of places forwhole night recordings. In addition to this the number and typeof epileptic seizures during in hospital recording could differ
from normal behavior and the equipment used in clinicalpractice is rather inconvenient for patients. The best solution
would be to enhance out-of-hospital monitoring.

MOBIHEALTH 2014, November 03-05, Athens, Greece
Copyright © 2014 ICSTDOI 10.4108/icst.mobihealth.2014.257503

Different solutions for multi-parametric assessment ofphysiological signals have been developed during the lastyears. However, none of them were able to provide an
integrated platform for the assessment of all the parameters
needed or the proposed system was not appropriate for use ineveryday life.
This paper proposes a reconfigurable cyberphysical systemthat handles multi-parametric data acquisition and analysis forthe mobile monitoring of epileptic patients .
II. SYST EM ARCHIT ECT URE AND COMPONENT S
Since different types of epilepsy require monitoring ofdifferent brain and body parameters, our goal is to develop apersonalized and reconfigurable system that assists indiagnosis, prognosis and treatment of the disease. Some of themain requirements of the system are that it should be non invasive, mobile, continuous, unobtrusive, reconfigurable andable to perform online analysis. The system can be divided into
three main components: the sensors, the Home Gateway
containing the middleware and the Personal Health Record(PHR) server. Fig. 1 shows an overview of the system and itscomponents.

Fig. 1: S ystem overview

The PHR server contains all the information related to thepatients. When a new measurement shall be started the Home
Gateway downloads a profile from the PHR. This profile
contains the sensor configuration. Having this information, themiddleware automatically configures the sensors and starts themeasurement.
The sensors are streaming data via Bluetooth to the Home
Gateway. The middleware fuses the data together, stores it,
uploads it to the PHR server and also streams it to the Data
Stream Management System (DSMS).
The different monitoring parameters used for multiparametric analysis include ECG, GSR, multichannel EEG andphysical activity sensor (acceleration sensors) [1]. Furthermore
there are some additional sensors providing context
information about the measurement conditions and thepatient’s environment. Temperature, air pressure, body position
and a push button where the patient can mark specific events ofinterest need to be acquired and synchronized with thephysiological data and then stored in a central database. The
fact that for each patient some parameters are more important
than others makes it necessary to adapt the system to thepatient’s needs. Therefor the system is designed reconfigurable
regarding the sensors. This means an individually selected setof sensors is attached to each patient profile in the PHR and theSystem uses only these required sensors to monitor the patient.
The DSMS runs different online algorithms to detectspecial events. Some examples are push button, alpha rhythm
and seizure detection. These algorithms are also defined in theprofile and can therefore be adapted to each patientindividually.
The Event Manager handles the different events of special
interest found by the algorithms in the DSMS. When an eventis detected the event manager notifies the PHR directly, that
can then inform the clinicians via SMS or e-mail about thepatients state.

A. Sensors
In order to achieve the main requirement, the multiparametric monitoring, different sensors should be integrated in
the 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 mobile
epilepsy monitoring [2-4]. For this system the following
sensors were selected.
The EEG module (Trackit TM, Lifelines Ltd, Over Wallop,UK) is a mobile ambulatory device that can measure up to 32
channels with a sampling rate of 256 Hz. Each channel has an
ADC resolution of 16 Bit and has a maximum differential AC
input 10 mV. Depending on the number of channels, thesampling rate and the battery used the device can achieve up to
96 hours of recording.
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
module can either be used with electrodes integrated into awearable chest strap, which is light, small and comfortable or
be used with conventional disposable wet electrodes. The
electrodes of the chest strap are dry, allowing the everyday use.
To assess the patient’s physical activity, the module has also an
integrated triaxial acceleration sensor (adxl345, Analog
Devices Inc.) with a range of ±8 g and a sampling frequency of
64 Hz and an air pressure sensor (BMP085, Bosch GmbH)with a sampling frequency of 8 Hz and a resolution of 0.03hPa.
The GSR module (edaMove, movisens GmbH, Karlsruhe,Germany) measures the skin conductance with a sampling rateof 32Hz. The measurement range of the GSR module is 2μS to100μS and its resolution is 14 bit. With this one can measureboth the electrodermal activity level (EDL) and responses(EDR). Furthermore the module has the same sensor set tomeasure physical activity integrated as the ECG module.

The push button module (bioPLUX, PluX, Arruda dos
Vinhos, Portugal) is a device that collects up to 8 signals fromvarious sensors and transmits the signals via Bluetooth to acomputer, where they can be viewed in real time. Theresolution of bioPLUX is 12 bits, and its sampling frequency isup to 1 kHz. For this system only one channel with a pushbutton 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, which communicates withsensors, the PHR server and the third part, which is the DSMS.
The DSMS and the Event Manager use the data stream toperform online analysis which detects events of special interest.
1) Data Fusion Unit:
To connect all the data, synchronize it and handle the data
stream a lean and open framework, which can be personalized
and reconfigured for each patient, is needed. xAffect is a
software framework developed by the Research Center for
Information Technology, Karlsruhe, Germany. It wasdeveloped 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 andgeneric format suitable for recording and archiving sensor datafrom various recording systems and with various sampling
frequencies [6].

2) Communication Unit:
The Communication Unit handles the communication withall components and with the User himself. A simple graphicaluser interface (GUI), shown in Fig. 2 enables the the user to setup the measurement.
In the GUI the user enters his username and password andthe patient-id. With the patient-id the Communication Unit candownload the personalized profile from the PHR. Afterwards
the user can press the configure button to initialize thecommunication with the sensors. Furthermore the profile
contains information about the alarm settings. This enables theCommunications Unit to set up the DSMS with the customized
set of alarms.
When the configuration process has finished successfully
the user is able to start the measurement by pressing the record
button. During the recording the data is streamed from thesensors through xAffect towards the DSMS and the storage.
The Communication Unit takes the data from the storage anduploads it in junks of 30 minutes to the PHR. In case an Eventoccurs the data will be uploaded immediately. This ensures thatwhen an alarm is send to a clinician the data will be ready todownload and view.
The GUI also allows the User to pause or resume themeasurement. This allows the subject interrupt the dataacquisition and move out of the Bluetooth range of the Home
Gateway.
3) Data Stream Management System (DSMS) and EventManager:
The DSMS function takes place on-line, where real timeprocessing of modalities is performed. The DSMS is based on
Microsoft™ StreamInsight™ platform created for thedevelopment and deployment of complex event processing(CEP) applications. It’s a high-throughput stream processing
architecture that uses .NET Framework-based development
platform.
The development done at the DSMS allows receiving xAffect™ sensor data in real time, to provide it in a lossless way tothe computation algorithms. With this, the framework is able tocreate own queries and policies over data.
In order to explain the data workflow within the DSMS, weneed to introduce some concepts related to StreamInsight™ asfollowing:Fig. 2 GUI of monitoring system

Sources: They are data providers and can be
implementedwithadapters,IEnumerable
orIObservable objects. They are in charge of collecting
data, fit it into a payload, generate an appropriate
timestamp (if needed) and redirect this toStreamInsight's core adding appropriate CTI (CurrentTime Increment) insertions. Sensor sources areIObservable objects built with information coming from
xAffect through TCP sockets.

Queries: All data from sources goes directly to
StreamInsight core (in any number of streams) where
it’s processed in order to satisfy one or more queries(LINQ). That will produce an output that will go
through to the data consumers (observers) where
custom operations can be defined. It can also computedifferent operations depending on query needs such as
aggregation, unions, max/min etc.
Consumers: They are pieces of software which mainfunction is to process data output from StreamInsight™
queries. They are usually implemented with theobserver interface so it can be easy to notify them when
new data is available.

We are achieving lossless stream between xAffect™ andDSMS by using TCP channel. This makes sure the built-in
architecture of StreamInsight™ can work with thesynchronized data coming from xAffect™.

from 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 indicated that thedeveloped system is not only more comfortable for the patients
since they can move more freely but also usable for theclinicians who were in charge of the data acquisition. The key
factor to usefulness seemed to be a GUI that is designed as
simple as possible. Future tests have to focus also on the user
feedback to show the usefulness of the device. Furthermore in
the future 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.
A CKNOWLEDGMENT

The main objective of the DSMS is to deliver alarm andwarning events predefined in the profile. One of the mostimportant events is the push button detection. Other events arefor example an alpha rhythm or seizure detection. The modulethat communicates events towards the PHR is called theNotification Manager.

This work has been co-funded by the European
Commission within the European Union's Seventh Framework
Program ([FP7/2007-2013]) in the ARMOR project(http://www.armor-project.eu).

C. Personal Health Record (PHR)[1]

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 of algorithms can be defined for eachpatient. Furthermore all data acquired by the system is uploaded
to the PHR server continuously. Hence, data can be downloaded
and viewed by the clinicians almost instantly from any point
where they have an internet connection. In case of events thePHR server set a marker for the time of interest and can notify
immediately by sending an email or a SMS.

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III. CONCLUSION
Different sets of sensors can be used customized to theneeds of clinicians and patients to acquire the necessary data

[5]
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