Frequency-moment signatures: a method for automated seizure detection from scalp EEG
Heba Khamis1, Armin Mohamed, Steve Simpson
1School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia.
Objectives:
To investigate patient-specific automated epileptic seizure detection from scalp EEG using a new technique: frequency-moment signatures.
Methods:
Signatures were calculated from 32s blocks of data of electrode differences from the right (RH) and left hemisphere (LH). Discrete Fourier transforms of 15 data subsets were calculated per block per hemisphere. The spectral powers at a given frequency from the RH and LH were combined into a single quantity. The signature elements were found by subtracting normalised central moments of the subset distribution from the mean, to measure the consistency of the spectral power at a given frequency over all subsets. The seizure measure was the logarithm of the probability that a signature belonged to a control set of non-seizure signatures.
Results:
Following the optimisation of signature parameters using three one-day recordings from each of 12 patients, performance was tested on a separate set of data from the same patients. The method had a sensitivity of 91.0% (total 34 seizures) with 0.020 false positives per hour (total 618 h).
Conclusions:
Frequency-moment signatures promise automated seizure detection sensitivities comparable to visual identification and other published methods, with improved false detection rates.
Significance:
This technique has the potential to be used more widely in EEG analysis.
