Information Technology Reference
In-Depth Information
EEG signals depict a complex behavior that
includes a 'preictal' stage in addition to 'seizure'
and 'interictal' states (D'Alessandro, et al, 2005).
EEG signals demonstrate the superposition of
brain activities recorded as electrical potential
variations at multiple spots over the scalp. EEG
signals ingest a considerable amount of informa-
tion related to the brain's functions. EEG obtained
from scalp electrodes is superposition of a large
amount of electrical potentials originating from
many sources which include brain cells such as
neurons and artifacts (Nayak and Cholayya, 2006).
The signals of interest, from the EEG obtained
on the scalp, need to be determined. These signals
are weighed sums of the neurons activity which
depends on the signal path from the brain cell to
the electrodes. As the same potential is recorded
from many electrodes, the signals from electrodes
are supposed to be highly correlated. If the weights
were known, the potentials in the sources can be
calculated from a sufficient number of electrode
signals using independent component analysis
(Ungureanu, Bigan, Strungaru, & Lazarescu,
2004).
Together with useful information, EEG signals
also contain redundant or noise information. Col-
lected data needs to be processed and elaborated
to obtain a good data format which can be used
for clinical diagnosis (Ungureanu, et al, 2004).
Signal processing addresses diverse issues in EEG
analysis including “data compression, detection
and classification, noise reduction, signal separa-
tion, and feature extraction” (Thanushkodi, 2010).
alerts of epileptic seizures, there have been quite
a few commercial products developed to detect
seizures using various methods, and transmit
alert signal to the carers. These products have
received a considerable number of positive feed-
backs from Epilepsy patients and their families.
While the detection algorithms of these devices
are unknown, the common detection method is
based on abnormal body movements. Clinical
values of this detection method is rather limited
and majorly used for alerting purpose only. These
devices cannot be considered medical devices
and their application in medical treatment is also
minor. However, the device models consisting of
a compact sensor, chip-embedded software for
analyzing potential seizure, a radio transmitter,
and a remote alarm system can be applied for a
complete system for Remote Detection and detec-
tion of epileptic seizures.
Wireless Motion Detection Monitor
by Epi-
lepsy Ontario (2010) is probably the first com-
mercial device worth to be mentioned. The device
together with its brothers such as
Motion Detec-
tion Monitor Basic
and
bedtime movement spasm
detector
provide Epileptic seizure detection using
various methods of detection and transmit the
alerts to people who can assist Epilepsy patients
in case assistance is necessary. The suggested
detection methods are via the changes in pat-
terns of Epilepsy patients'
physical movements,
heartbeat, and breath
.
A similar product to
Wireless Motion Detec-
tion Monitor
is
Emfit tonic-clonic seizure monitor.
This device is designed to detect both seizures
with
muscle jerking
of a sleeping Epilepsy patient
and continued abnormal movements including
respiration and heartbeats
. The sensor also detects
hyperventilation and partial convulsions and the
alarm will be activated remotely or locally after
a preset amount of time (Emfit, 2009; Tunstall,
2008).
Epilepsy Bed Sensor
is developed and dis-
tributed by Chubb Community Care (2008). This
device also sends alert to a central alarm system
A Review of Epileptic Seizure
Automated Detection Methods
Commercial Automated Devices
for Epileptic Seizure Detection
Epileptic seizures, happening when Epilepsy pa-
tients are alone or sleep, have always been one of
the most concerned issues for Epilepsy patients and
the families. In responding to the need of getting
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