TY - JOUR
T1 - Characterization of Subdural Stimulation-Induced Afterdischarge Activity Using the Continuous Wavelet Transform
AU - Najmi, Amir Homayoon
AU - Webber, William R.S.
AU - Lesser, Helen
AU - Lesser, Ronald Peter
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/7
Y1 - 2016/7
N2 - We address the problem of characterization of afterdischarges (ADs) that often arise in patients with intractable focal epilepsy who, as part of their evaluation, undergo cortical electrical stimulation: A standard diagnostic and evaluation procedure before respective surgery. Results: A total of 1333 channels of data recorded in 17 trials of seven patients whose EEG showed ADs (on a total of 156 channels) during cortical stimulation were examined in the time-scale domain using a complex Morlet scalogram. We found excellent characterization of the AD channels based on the distribution functions of the sum of the wavelet coefficients in the two lowest scales corresponding to the frequency range [20, 80] Hz, i.e., the β and γ ranges of EEG. Conclusion: We suggest that the transient Morlet wavelet and the scale domain activity function of the EEG in the two lowest scales (as defined in this paper) could serve as a very useful decision aid in the identification of ADs during and after cortical electrical stimulation. Significance: In patients undergoing cortical electrical stimulation, AD waveforms can cause misleading test results by altering the ongoing electroencephalogram (EEG), and can become unwanted seizures. Any process to suppress the ADs rests on a reliable method to distinguish them from normal EEG channels, a task that is usually performed by visual inspection, and that is complicated by the fact that ADs have multiple distinct morphologies. The single feature of the EEG in our study resulted in average probability of detection of 0.99 with an average false alarm probability of 0.04. It is likely that the addition of one or two more features to our decision aid could improve sensitivity and selectivity to near perfection.
AB - We address the problem of characterization of afterdischarges (ADs) that often arise in patients with intractable focal epilepsy who, as part of their evaluation, undergo cortical electrical stimulation: A standard diagnostic and evaluation procedure before respective surgery. Results: A total of 1333 channels of data recorded in 17 trials of seven patients whose EEG showed ADs (on a total of 156 channels) during cortical stimulation were examined in the time-scale domain using a complex Morlet scalogram. We found excellent characterization of the AD channels based on the distribution functions of the sum of the wavelet coefficients in the two lowest scales corresponding to the frequency range [20, 80] Hz, i.e., the β and γ ranges of EEG. Conclusion: We suggest that the transient Morlet wavelet and the scale domain activity function of the EEG in the two lowest scales (as defined in this paper) could serve as a very useful decision aid in the identification of ADs during and after cortical electrical stimulation. Significance: In patients undergoing cortical electrical stimulation, AD waveforms can cause misleading test results by altering the ongoing electroencephalogram (EEG), and can become unwanted seizures. Any process to suppress the ADs rests on a reliable method to distinguish them from normal EEG channels, a task that is usually performed by visual inspection, and that is complicated by the fact that ADs have multiple distinct morphologies. The single feature of the EEG in our study resulted in average probability of detection of 0.99 with an average false alarm probability of 0.04. It is likely that the addition of one or two more features to our decision aid could improve sensitivity and selectivity to near perfection.
KW - Afterdischarge
KW - CWT
KW - EEG
KW - electrical stimulation
KW - epilepsy
KW - wavelet
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U2 - 10.1109/TBME.2015.2494522
DO - 10.1109/TBME.2015.2494522
M3 - Article
C2 - 26513776
AN - SCOPUS:84978066093
SN - 0018-9294
VL - 63
SP - 1440
EP - 1446
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 7
M1 - 7303931
ER -