U.S. patent application number 15/955504 was filed with the patent office on 2018-10-25 for method for detecting seizures.
The applicant listed for this patent is Brain Sentinel, Inc.. Invention is credited to Isa Conradsen, Kim Gomme Gommesen, Karsten Hoppe.
Application Number | 20180303366 15/955504 |
Document ID | / |
Family ID | 46545583 |
Filed Date | 2018-10-25 |
United States Patent
Application |
20180303366 |
Kind Code |
A1 |
Conradsen; Isa ; et
al. |
October 25, 2018 |
Method for Detecting Seizures
Abstract
A method and system for detecting the onset of seizures
comprising a measuring unit (1) having one or more sensors for
detecting the electromyographic signal of one or more muscles of
the user. The sensors are connected to a pre-processing module (3)
comprising a high-pass filter which filters out noise and motion
artefacts related to normal muscle activities. The preprocessing
module is connected to a feature extraction module (4) comprising a
threshold detector which counts the number of crossings with a
hysteresis within a predetermined time window. The feature
extraction module is connected to a classification module (5) which
compares the extracted features to a first and second threshold and
generates an event signal if the extracted features are above the
first and second threshold.
Inventors: |
Conradsen; Isa; (Kobenhavn
S, DK) ; Gommesen; Kim Gomme; (Odense N, DK) ;
Hoppe; Karsten; (Kobenhavn O, DK) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Brain Sentinel, Inc. |
San Antonio |
TX |
US |
|
|
Family ID: |
46545583 |
Appl. No.: |
15/955504 |
Filed: |
April 17, 2018 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
14233904 |
Jan 31, 2014 |
9949654 |
|
|
15955504 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/04004 20130101;
A61B 5/1123 20130101; A61B 5/4094 20130101; A61B 5/04012 20130101;
A61B 5/04014 20130101; A61B 5/04015 20130101; A61B 5/0488
20130101 |
International
Class: |
A61B 5/04 20060101
A61B005/04; A61B 5/00 20060101 A61B005/00; A61B 5/11 20060101
A61B005/11; A61B 5/0488 20060101 A61B005/0488 |
Foreign Application Data
Date |
Code |
Application Number |
Jul 19, 2011 |
DK |
PA 2011 00556 |
Claims
1-18. (canceled)
19. A system for detecting the onset of seizures, such as epileptic
seizures with a monitoring or detection device, comprising: a
monitoring or detection device; a measuring unit in the monitoring
or detection device having at least one sensor having a
configuration for detecting an electromyography signal generated by
a muscle on a body of a user; a data processing unit connected to
the measuring unit and configured to process and analyze the
measured signal, wherein the data processing unit comprises: a
pre-processing module configured to provide a filtered signal, a
feature extraction module connected to the pre-processing module
and having a threshold detector, wherein the feature extraction
module is configured to apply a first number of time windows to the
filtered signal, and a classification module connected to the
feature extraction module and configured to generate an output
signal, wherein the feature extraction module including a threshold
detector with a hysteresis value, defining a positive and a
negative threshold value, and is configured to use the threshold
detector to count a number of zero-crossings of the filtered signal
within each of the time windows, and the classification module is
configured to compare the count with a first threshold value, to
compare the number of time windows having a count above the first
threshold value with a second threshold value, and to generate the
output signal if the number of time windows having a count above
the first threshold value is above the second threshold value.
20. The system of claim 19, wherein the data processing unit is
connected wirelessly to the measuring unit.
21. The system of claim 19, wherein the hysteresis value is between
.+-.0 .mu.V and .+-.500 .mu.V.
22. The system of claim 19, wherein said first threshold is between
about 100 and 400 crossing counts.
23. The system of claim 19, further comprising an adaptive update
module, wherein the adaptive update module is configured to adjust
said first threshold for the system as seizures are detected.
24. The system of claim 19, further comprising an adaptive update
module, wherein the adaptive update module is configured to adjust
said first threshold, said second threshold, or both said first
threshold and said second threshold for the system as seizures are
detected.
25. The system of claim 19, further comprising an adaptive update
module, wherein the adaptive update module is configured to update
one or more thresholds settings for the system as seizures are
detected in order to reduce a latency of seizure detection for the
system.
26. The system of claim 19, wherein the system comprises at least a
second measuring unit which is configured to detect the muscle
activities of one or more muscles on the body of the user or
another signal characteristic of a seizure, and at least a second
data processing unit which is connected to the second measuring
unit and configured to process and analyze the detected signal of
the second measuring unit and further configured to generate an
output signal indicating whether the detected signal is above a
third threshold value or not.
27. The system of claim 26, wherein the classification modules in
the data processing unit is further connected to an evaluation
module which is configured to generate the event signal if two or
more of the output signals have a higher output value the
hysteresis value or if a weighted sum of the output signals is
above a fourth threshold value.
28. The system of claim 27, wherein the system further comprises an
alarm unit connected to the evaluation module.
29. The system of claim 19, wherein the system further comprises an
alarm unit connected to the data processing unit.
30. A method for indicating the onset of seizures, such as
epileptic seizures with a monitoring or detection device,
comprising: providing a monitoring or detection device; providing a
measuring unit in the monitoring or detection device and a data
processing unit connected to the measuring unit in the monitoring
or detection device, the data processing unit including a feature
extraction module and a classification module; detecting one or
more electromyographic signals generated by at least one muscle on
the body of a user using the measuring unit and transmitting at
least one detected signal to the data processing unit, processing
and analyzing the detected signal using the data processing unit in
order to generate an event signal by: filtering the detected signal
in order to provide a filtered signal and analyzing the filtered
signal within a first number of time windows, applying a hysteresis
value, defining a positive and a negative threshold value, to count
a number of crossing of the filtered signal within each of the
first number of time windows, determining a second number of time
windows having a count above a threshold count value, further
comparing the second number of time windows to a threshold value of
the classification module, and generating the event signal if the
second number of time windows is above the second threshold value.
Description
TECHNICAL FIELD
[0001] The invention relates to a system for detecting the onset of
seizures, such as epileptic seizures, comprising a measuring unit
having at least one sensor configured to detect an
electromyographic signal generated by a muscle on the body of a
user, and a data processing unit configured to process and analyse
the detected signal and generating an event signal if the analysed
signal is above at least one threshold value.
[0002] The invention also relates to a method for indicating the
onset of seizures, such as epileptic seizures, comprising the steps
of detecting one or more electromyographic signals generated by at
least one muscle on the body of a user by means of a measuring
unit, processing and analysing the detected signal by means of a
data processing unit which generates an event signal if the
analysed signal is above at least one threshold value, and
triggering an event based on the event signal.
PRIOR ART
[0003] Today there is a need for a way to improve the detection of
seizure onsets in order to improve treatment or to alert caretakers
or the patient of a seizure in order to prevent potential dangerous
situations associated with a seizure. Seizures, such as epileptic
seizures, can occur as partial or generalized seizures or a
combination of both, where generalized seizures can occur as tonic,
clonic, or tonic-clonic seizures. In a tonic-clonic seizure it
starts with a tonic phase which then develops into a clonic
phase.
[0004] Different methods or algorithms for detecting seizure onsets
are described in the literature. An example of such is disclosed in
WO 2008/131782 A1 which detects epileptic seizures by evaluating a
sensed EMG signal over a number of time windows, if the sensed EMG
signal is over a threshold value within the first time window,
where the threshold value is determined according to the maximum
voluntary contraction of the user. The algorithm has the user. The
algorithm has the disadvantage that the parameters have to be
adjusted to each individual person in order to reduce the number of
false positives and accurately detect the seizure.
[0005] US 2007/0142873 A1 describes an adaptive method and system
for detecting seizures by sensing iEEG signals which is used in a
feedback loop to administer a drop or give a simulate to counteract
the seizure. The parameters used to detect the seizures are
determined by using a time consuming and very complex data process,
where the system performs several measurements with different
parameter settings from which the best parameter settings are
selected. This system requires the desired signals to be captured
by multiple channels in order to accurately detect the signals and
to reduce the number of false positives. Furthermore, this method
uses an invasive procedure to place the iEEG sensors on the
body.
[0006] WO 2007/079181 A2 describes an epileptic seizure detection
method and system comprising a sensor unit having multiple
intracranial EEG electrodes connected to a processor unit in which
a prediction algorithm is implemented. The prediction algorithm
determines the probability of having a seizure within the near
future by combining multiple features extracted from the sensed EEG
signals. The processor unit may comprise any number of components
used to process the signals, but it does not disclose an exemplary
embodiment of the prediction algorithm. Furthermore, this method
requires the signal to be measured at multiple locations in order
to predict seizures and uses a training module/process to adjust
the parameters used to predict seizures.
[0007] US 2004/0230105 A1 describes a method and system for
predicting epileptic seizures which may use an EMG sensor to detect
a biomedical signal connected to a processor which processes the
signals using a segmentation algorithm. Various features are then
extracted and analysed in a prediction algorithm using a set of HMM
models to predict a seizure. The method uses an automatic training
algorithm to adjust the parameters of each HMM model. This system
uses a complex and time-consuming method to detect seizures which
are not suited for implementation in a small battery powered device
attached to the body of a user. attached to the body of a user.
Furthermore, this method requires the detection of multiple signals
at multiple locations and the adjustment of the parameters for each
individual user.
OBJECT OF THE INVENTION
[0008] The invention solves these problems of the closest prior art
by providing a method indicating the onset of seizures
characterized in that the detected signal is filtered by means of a
high-pass filter, the number of crossing between an amplitude of
the filtered signal and a hysteresis value is counted, and the
count is compared to a first threshold value and the event signal
generated if the count is above the first threshold value.
[0009] This method can be implemented as a generic algorithm in a
seizure detection or monitoring device without having to calibrate
the parameters for each individual user first. The seizure
detection method has a low false detection rate and a short
latency, so that seizures can be detected faster and more
accurately. Furthermore, this enables seizures to be detected by
using only one measuring channel, thus reducing the complexity of
the detection system and the number of components needed to detect
seizures.
[0010] According to one embodiment, a predetermined number of time
windows, such as overlapping time windows, are applied to the
filtered signal, and the number of crossings within each time
window is counted. This enables the length and shape of the time
windows to be selected so that the detection system has a
sufficiently short latency. This also enables the overlap of the
time windows to be selected so that the data analysis is
improved.
[0011] According to one embodiment, the number of time windows,
which has a count above the first threshold value, is compared to a
second threshold value, and the event signal is generated if the
number of time windows is above the second threshold value. This
enables the first and second threshold values to be selected so
that all seizures are detected while reducing the number of false
positives to a minimum and ensuring a short latency for the
detection system. This also enables the first and second threshold
values to be determined according to the seizure characteristics
detected at that measuring position in order to more accurately
detect the seizures.
[0012] According to one embodiment, at least a measuring unit
detects the muscle activities of one or more muscles on the body of
the user or another signal characteristic of a seizure, and at
least a second data processing unit processes and analyses the
detected signal from the second measuring unit and generates an
output signal indicating if the detected signal is above a third
threshold value or not. According to a specific embodiment, the
output signals are transmitted to an evaluation module which
generates the event signal if two or more of the output signals
have a high value or if a weighted sum of the output signals is
above a fourth threshold value. This improves the detection of
seizures by comparing the detected signal to other signals which
are detected at different locations, thus reducing the number of
false positives.
[0013] According to one embodiment, the event signal is transmitted
to an alarm unit which generates an alarm or an alarm message based
on the event signal. This enables the algorithm to inform or alert
the user or an external caretaker that a seizure is occurring so
that the necessary actions can be taken.
[0014] The invention also solves these problems by providing a
system for detecting the onset of seizures characterized in that
the data processing unit comprises a pre-processing module having a
high-pass filter configured to filter out signals having a
frequency below a predetermined cut-off frequency, a feature
extraction module which is connected to the pre-processing module
and configured to count the number of crossings between an
amplitude of the filtered signal and a predetermined hysteresis
value, and a classification module which is connected to the
feature extraction module and configured to compare the count to a
first threshold value and generate the event signal if the count is
above the first threshold value.
[0015] This enables the detection system to be implemented as a
generic detection system which does not require the parameters for
the algorithm to be calibrated for each individual user. This also
enables the system to detect seizures by using only one measuring
channel, thus reducing the complexity of the detection system and
the number of components needed to ponents needed to detect
seizures. This seizure detection system has a low false detection
rate and a short latency so that seizures can be detected faster
and more accurately.
[0016] According to a specific embodiment, the cut-off frequency is
above 100 Hz, preferably between 100-200 Hz. This enables the
system to filter out most of the noise and motion artefacts, which
do not relate to the seizure, before analysing the detected
signal.
[0017] According to a specific embodiment, the hysteresis value is
between .+-.0-500 .mu.V, preferably .+-.20-250 .mu.V. This enables
the hysteresis value to be selected so that low-level noise and
motion artefacts, which are not removed by the pre-processing
module, have no or a minimum effect on the counts. The threshold
detector mainly detects the frequency of the filtered signal if the
hysteresis has a low value, whereas the threshold detector detects
both the frequency and the amplitude of the filtered signal if the
hysteresis has a high value.
[0018] According to one embodiment, the feature extraction module
is configured to apply a predetermined number of time windows to
the filtered signal and is configured to count the number of
crossings within each time window. According to a specific
embodiment, a first time window overlaps a second time window, and
the overlap is between 0-95%, preferably between 50-75%. According
to another specific embodiment, the time windows have a length
between 0.25-2 sec. This enables the length of the time windows to
be selected so that the detection system has a sufficiently short
latency. This also enables the overlap of the time windows to be
selected according to the shape of the time window so that the data
analysis is improved.
[0019] According to one embodiment, the classification module is
further configured to compare the number of time windows, which
have a count above the first threshold value, with a second
threshold value and generate the event signal if the number of time
windows is above the second threshold value. According to a
specific embodiment, the first threshold is between 100-400,
preferably between 240-300, and the second threshold is between
1-40, preferably between 10-25. This enables the first and second
threshold values to be selected so that all seizures are detected
while reducing the number of false positives to a minimum and
ensuring a short latency for the detection system. This also
enables the first and second system. This also enables the first
and second threshold values to be determined according to the
seizure characteristics detected at that measuring position in
order to more accurately detect the seizures.
[0020] According to one embodiment, the system comprises at least a
second measuring unit which is configured to detect the muscle
activities of one or more muscles on the body of the user or
another signal characteristic of a seizure, and at least a second
data processing unit which is connected to the second measuring
unit and configured to process and analyse the detected signal and
configured to generate an output signal indicating if the detected
signal is above a third threshold value or not. According to a
specific embodiment, the data processing units are connected to an
evaluation module which is configured to generate the event signal,
if two or more of the output signals have a high value or if a
weighted sum of the output signals is above a fourth threshold
value. This improves the detection of seizures by comparing the
detected signal to other signals which are detected at different
locations, thus reducing the number of false positives.
[0021] According to one embodiment, the event signal is transmitted
to an alarm unit which is configured to generate an alarm or an
alarm message based on the event signal. This enables the detection
system to inform and alert the user or an external caretaker that a
seizure is occurring, thereby allowing the person to take proper
action. This also enables the alarm unit to be integrated into the
unit which can be easily attached to or placed on the body of the
user.
[0022] The invention also describes the use of the method or system
to detect seizures having tonic activity, such as tonic-clonic
seizures.
[0023] The drawings
[0024] The embodiments of the invention will now be described with
reference to the drawings, in which
[0025] FIG. 1 shows a first embodiment of the seizure detection
system according to the invention, and
[0026] FIG. 2 shows a second embodiment of the seizure detection
system according to the invention.
DESCRIPTION OF EXEMPLARY EMBODIMENT
[0027] FIG. 1 shows a first embodiment of the detection system
which comprises a measuring unit 1 having one or more sensors 1a
for sensing the muscle activities generated by one or more muscles,
i.e. the skeletal muscles, on the body of a user. The sensors 1a
may be configured as electromyographic sensors. The sensors 1a may
be integrated into the measuring unit 1, which may be attached or
fixed to the body by using an adhesive agent or a fixating band or
strap, or alternatively may be placed at different measuring
positions and connected to the measuring unit 1 by a wired or
wireless connection. The sensors 1a may be connected to a
controller and a memory module, and data from the sensors may be
stored in the memory module before being transmitted to a data
processing unit 2 either periodically, continuously or on request
from the data processing unit 2. In a preferred embodiment the
measuring unit 1 uses a single measuring channel to detect the
electromyographic signal. This enables the sensors to be easily
placed at the measuring positions without using any invasive
procedures.
[0028] The detected data are then transmitted via a wired or
wireless connection to the data processing unit 2 which processes
and analyses the data. The data processing unit 2 may be an
external device or integrated into the measuring unit 1. The
detected data are transmitted to a pre-processing module 3
comprising filter means for filtering out noise and motion
artefacts which relate to normal muscle activities. The filter
means may be configured as a high-pass filter with a predetermined
cut-off frequency which filters out most of the noise and motion
artefacts not related to the seizure. Studies have shown that all
or most of the data relating to the seizure are situated in a
frequency band above 100 Hz, while all or most of the noise and
motion artefacts are situated in a frequency band of 0-20 Hz. The
cut-off frequency may be selected as any frequency between the
upper frequency of the lower frequency band and the lower frequency
of the upper frequency band, i.e. between 20-200 Hz, preferably
between 100-200 Hz. The cut-off frequency may be selected according
to the desired order of the high-pass filter. The pre-processing
module 3 may comprise a biasing circuitry which removes any bias so
that the filtered signal is symmetric around zero. In a removes any
bias so that the filtered signal is symmetric around zero. In a
preferred embodiment the cut-off frequency for the filter means is
selected to be 150 Hz or 170 Hz.
[0029] The filtered data are then transmitted to a feature
extraction module 4 which extracts one or more predetermined
features. The feature extraction module 4 applies a number of
predetermined time windows to the filtered data. The time windows
may be configured as overlapping time windows with a predetermined
overlap and length. The time windows may be shaped as a
rectangular, a triangular, a cosine, a sinc or another type of
window where the overlaps have a rectangular, a triangular or
another shape. In order to improve the data analysis, the time
windows may have an overlap between 0-95%, preferably between
50-75%. The length of the time windows is selected so that the
detection system has a sufficiently short latency, i.e. the length
may be between 0.25-2 sec. In a preferred embodiment the time
window is selected to have an overlap of 75% and a length of 1
sec.
[0030] The feature extraction module 4 uses a threshold detector to
count the number of crossings, i.e. the zero-crossings, between the
amplitude of the filtered signal and at least one predetermined
threshold value within each time window. The threshold detector may
be configured as a threshold detector with a predetermined
hysteresis value, i.e. a positive and a negative threshold value.
The hysteresis value may be selected so that low-level noise and
motion artefacts, which are not removed by the pre-processing
module 3, have no or a minimum effect on the counts. The hysteresis
value may be selected within a range of .+-.0-500 .mu.V, preferably
within .+-.20-250 .mu.V. In a preferred embodiment the threshold
detector has a hysteresis value of .+-.50 .mu.V or .+-.100 .mu.V.
If the hysteresis has a low value, the threshold detector mainly
detects the frequency of the filtered signal, while the threshold
detector detects both the frequency and the amplitude of the
filtered signal if the hysteresis has a high value.
[0031] The feature extraction module 4 transmits the count for each
time window to a classification module 5 which evaluates the
extracted features and generates an event signal if at least one of
the extracted features is above a threshold value. The
classification module 5 compares the count for each time window to
a first threshold value and generates an event signal if the count
is above the threshold value. Alternatively, the classification
module 5 may compare fication module 5 may compare the number of
time windows, which has a count above the first threshold value, to
a second threshold value and generate an event signal if the number
of time windows is above the second threshold value. The number of
time windows with a count above the first threshold value may be
determined by counting the number of consecutive and/or
non-consecutive time windows within a second predetermined time
window.
[0032] The first and second threshold may be adjusted in order to
more accurately detect the seizures. The thresholds may be
determined according to the seizure characteristics detected at
that measuring position.
[0033] Studies have shown that a tonic-clonic seizure generates a
high number of crossings (counts) in the tonic phase, which then
drops to a lower number at the beginning of the clonic phase. The
first threshold value for the number of crossings may be between
100-400, preferably between 240-300. The second threshold value for
the number of time windows may be between 1-40, preferably between
10-25. The first and second threshold values may be selected so
that all seizures are detected while reducing the number of false
positives to a minimum and ensuring a short latency for the
detection system. In a preferred embodiment the first threshold
value is selected to be 260 and the second threshold value is
selected to be 15.
[0034] The event signal triggers an event which informs or alerts
the user or an external caretaker that a seizure is occurring. The
event signal may be transmitted to an alarm unit 6 by a wired or
wireless connection. The alarm unit 6 may be an external device or
integrated into the measuring unit 1 or the data processing unit 2.
The alarm unit 6 may generate one or more types of alarms or
messages, i.e. an audio, a visual, a vibrating alarm, an alarm
message, or any combination thereof. The measuring units 1, the
data processing units 2, and optionally the alarm unit 6 may be
integrated into a single unit which can be easily attached to or
placed on the body.
[0035] FIG. 2 shows a second embodiment of the detection system,
where the reference numbers are the same as in FIG. 1. The
detection system comprises a number of measuring units 1, 7, 8,
which are placed at different measuring positions on the body. The
measuring units 7,
[0036] The measuring units 7, 8 may have the same configuration as
the measuring unit 1 or different configurations. The measuring
unit 1, 7, 8 comprises one or more sensors 1a, 7a, 8a for measuring
the muscle activities generated by one or more muscles, i.e.
skeletal muscles. Alternatively, the measuring units 1, 7, 8 may
comprise different types of sensors, i.e. EMG, MMG or PMG, for
measuring the muscle activity or may comprise at least one other
sensor for measuring another type of signal related to a seizure,
i.e. respiration, heart rate or galvanic skin response. The sensors
may be easily placed at the measuring positions without using any
invasive procedures by using an adhesive agent or a fixating band
or strap. This enables the system to improve the detection of
seizures and reduce the number of false positives by comparing the
detected signal with other signals which are detected at different
locations.
[0037] The detection system may comprise a number of data
processing unit 2, 9, 10, which are connected to each of the
measuring units 1, 7, 8. The data processing unit 9, 10 may have
the same configuration as the data processing unit 2 or a different
configuration. The data processing unit 2, 9, 10 may comprise
processing means 3, 4, 11, 12 which analyses and extracts one or
more features or patterns from the measured signals, and a
classification module 5, 13, 14, which compares the extracted
features or patterns to one or more threshold values and generates
an output signal, i.e. a high or a low output signal, indicating if
a seizure is present or not. The outputs of each classification
modules 5, 13, 14 in the data processing units 2, 9, 10 are
transmitted by a wired or wireless connection to an evaluation
module 15 which evaluates the output signals and generates an event
signal if a seizure is detected. The evaluation module 15 may
generate an event signal if two or more of the output signals are
high. Alternatively, the evaluation module 15 may apply a
predetermined weighing factor to each of the output signals and
generate an event signal if the sum of these weighted outputs is
above a third threshold value. The event signal is then transmitted
by a wired or wireless connection to the alarm unit 6 which informs
or alerts the user or an external caretaker that a seizure is
occurring.
[0038] The data processing units 2, 9, 10 may be arranged as
parallel pipelines in a common data processing unit or as separate
data processing units which are connected to the evaluation module.
The evaluation module 15 may be integrated into the common data
processing unit processing unit or into one of the separate data
processing units.
[0039] The described method in FIGS. 1 and 2 for detecting the
onset of seizures can be implemented as a generic algorithm in a
detection or monitoring system, which only uses a single EMG
measuring channel to detect seizures. This enables the generic
algorithm to be implemented in a small detection or monitoring
device without having to calibrate the parameters, i.e. the
threshold values, for each individual user first. This reduces the
complexity of the detection system and reduces the number of
components needed to detect seizures.
[0040] The described detection system may comprise an adaptive
update module (not shown) which updates the threshold values each
time the system has detected a seizure. This enables the first and
second threshold values for the number of counts and time windows
to be adjusted so that the system is able to detect the seizures
more accurately. This in turn will reduce the number of false
positives and also reduce the latency for the system.
Example
[0041] Electromyographic measurements were conducted on six
different persons of different ages and genders as shown in table
1. The persons all had at least one tonic-clonic seizure which was
verified by other measurements, including video and ECG
measurements.
TABLE-US-00001 TABEL 1 Patient Gender Age Seizures 1 2 3 4 5 6
indicates data missing or illegible when filed
[0042] The EMG sensors were placed on the left deltoid muscle with
the active electrode on the center of the muscle and the reference
electrode placed on the acromioclavicular joint. The EMG signal was
sampled with a sampling rate of 1024 Hz. The measured EMG signals
were EMG signals were pre-processed with a high-pass Butterworth
filter with an order of 20 and a cut-off frequency of 150 Hz and
analysed with time windows with a length of 1 sec. and an overlap
of 75% and a hysteresis value of .+-.50 .mu.V. The first and second
threshold chosen for each of the test subjects can be seen in table
2.
TABLE-US-00002 TABEL 2 Patient Counts Window Sensitivit Latency FDR
1 0, 2 0, 3 4 8,00; 0, 5 0, 6 7,00; 0, indicates data missing or
illegible when filed
[0043] As shown in table 2 the described detection system has a
sensitivity of 100%, meaning that it detected all the seizures. The
detection system has a false detection rate (FDR) between 0-0.1885
per hour and a latency between 7-10.5 sec.
* * * * *