Abstract
This paper aims to improve the performance of a proposed electroencephalogram (EEG) spike detection technique. This technique accentuates the signature of spikes in the time domain signal using a nonlinear energy operator by amplifying high frequency activities such as spikes. The resulted signal is convolved with a smoothing window to reduce the effect of noise. Then, values of the resulted signal higher than a threshold value are considered as spikes. The instantaneous nature of the technique and its very low computation make it an ideal tool for spike detection. In this approach selection of the threshold value is crucial for the accuracy of the technique. This paper is aimed at improving the technique using a new approach for the threshold selection using the histogram of the smoothed nonlinear energy operator. The efficiency of the presented spike detection method has been evaluated using both synthetic signals and real newborn EEG. Results in this paper show that the proposed technique is superior to the original technique both in terms of sensitivity and selectivity.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005 |
| Pages | 207-210 |
| Number of pages | 4 |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | 8th International Symposium on Signal Processing and its Applications, ISSPA 2005 - Sydney, Australia Duration: Aug 28 2005 → Aug 31 2005 |
Other
| Other | 8th International Symposium on Signal Processing and its Applications, ISSPA 2005 |
|---|---|
| Country | Australia |
| City | Sydney |
| Period | 8/28/05 → 8/31/05 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
A histogram-based electroencephalogram spike detection. / Malarvili, M. B.; Hassanpour, Hamid; Mesbah, Mostefa; Boashash, Boualem.
Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005. Vol. 1 2005. p. 207-210 1580232.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A histogram-based electroencephalogram spike detection
AU - Malarvili, M. B.
AU - Hassanpour, Hamid
AU - Mesbah, Mostefa
AU - Boashash, Boualem
PY - 2005
Y1 - 2005
N2 - This paper aims to improve the performance of a proposed electroencephalogram (EEG) spike detection technique. This technique accentuates the signature of spikes in the time domain signal using a nonlinear energy operator by amplifying high frequency activities such as spikes. The resulted signal is convolved with a smoothing window to reduce the effect of noise. Then, values of the resulted signal higher than a threshold value are considered as spikes. The instantaneous nature of the technique and its very low computation make it an ideal tool for spike detection. In this approach selection of the threshold value is crucial for the accuracy of the technique. This paper is aimed at improving the technique using a new approach for the threshold selection using the histogram of the smoothed nonlinear energy operator. The efficiency of the presented spike detection method has been evaluated using both synthetic signals and real newborn EEG. Results in this paper show that the proposed technique is superior to the original technique both in terms of sensitivity and selectivity.
AB - This paper aims to improve the performance of a proposed electroencephalogram (EEG) spike detection technique. This technique accentuates the signature of spikes in the time domain signal using a nonlinear energy operator by amplifying high frequency activities such as spikes. The resulted signal is convolved with a smoothing window to reduce the effect of noise. Then, values of the resulted signal higher than a threshold value are considered as spikes. The instantaneous nature of the technique and its very low computation make it an ideal tool for spike detection. In this approach selection of the threshold value is crucial for the accuracy of the technique. This paper is aimed at improving the technique using a new approach for the threshold selection using the histogram of the smoothed nonlinear energy operator. The efficiency of the presented spike detection method has been evaluated using both synthetic signals and real newborn EEG. Results in this paper show that the proposed technique is superior to the original technique both in terms of sensitivity and selectivity.
UR - http://www.scopus.com/inward/record.url?scp=33847328713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847328713&partnerID=8YFLogxK
U2 - 10.1109/ISSPA.2005.1580232
DO - 10.1109/ISSPA.2005.1580232
M3 - Conference contribution
AN - SCOPUS:33847328713
SN - 0780392434
SN - 9780780392434
VL - 1
SP - 207
EP - 210
BT - Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005
ER -