![](bg3.png)
seizure and non-seizure classes. Initial means and
standard deviations are fixed to [1, 1] (seizure) and
[0, 0.5] (non-seizure), then they are iteratively
updated for an adaptive classification.
3 RESULTS
ROC curves (true positives vs. false positives), area
under the curve (AUC), automatic thresholds and
classification results (accuracy, sensitivity,
specificity in tables) are shown in figures 1 and 2. In
both figures, a region is plotted (zoom) for better
visualizing the behaviour of automatic thresholds.
The mean and standard deviation of ROCs
(across all segments used as the pattern) for
segment´s length of 2, 4, 6 and 10 seconds; together
with the optimal cut-off point (nearest point to (0,
1)) confirm the high potential of this feature for
detection purposes.
Table 2 shows a quantitative comparison of the
goodness of the automatic thresholds derived from
the classifiers, as measured by the Euclidean
distance between their sensitivity/specificity and
those of the best cut-off of the ROC for all seizure
patterns and different segments’ length.
Table 2: Mean ± Std of Euclidean distances between
optimal cut-off and automatic thresholds.
Method 2 s 4 s 6 s 10 s
Data 1
Binary 0.27±0.14 0.34±0.23 0.28±0.16 0.31±0.19
k-Means 0.21±0.11 0.29±0.20 0.21±0.12 0.23±0.19
LDA 0.21±0.11 0.20±0.11 0.20±0.10
0.17±0.08
SVM 0.23±0.11 0.37±0.25 0.33±0.23 0.34±0.23
AZT
0.02±0.01 0.08±0.06 0.16±0.09
0.27±0.01
Data 2
Binary 0.36±0.26 0.36±0.25 0.33±0.20 0.31±0.18
k-Means 0.28±0.11 0.33±0.10 0.33±0.07 0.30±0.08
LDA
0.19±0.12
0.20±0.13 0.11±0.11 0.18±0.08
SVM 0.48±0.20 0.45±0.23 0.46±0.21 0.48±0.23
AZT 0.22±0.22
0.16±0.07 0.11±0.05 0.16±0.11
4 DISCUSSION
The results illustrated in figures 1 and 2, show that
AZT outperforms the other algorithms in terms of
sensitivity (for small segments), while generally
offering the smallest specificity. This is an attractive
property for the clinical automatic scanning, when it
is more important not to miss a seizure, although
more false positives (FP) are introduced. While
LDA, k-Means and SVM give stable results for
different segments’ length, the AZT tended to have
lower sensitivity for longer segments. AZT also
showed smaller standard deviation for the true
positive (TP) rate than the other methods, and the
nearest pair of TP/FP to those defined by the optimal
cut-off of the ROCs in average (Table 1). This
means that the implicit thresholding in AZT offers a
better compromise of sensitivity and specificity.
One important limitation of the procedure
followed here is that blind one-component
PARAFAC decomposition may not always extract
the epileptic activity. We tested that when this step
was supervised to ensure using the correct
component as the spatio-spectral pattern, the
classification results with AZT improved in the
worst cases (Table 3). This procedure is much
slower and implies training the clinician in the
correct use of the PARAFAC model.
Table 3: Classification results for one 2-s long seizure
pattern of Data 2. A) Using blind one-component
PARAFAC. B) Using one PARAFAC component (out of
3) that best characterized the seizure.
Method
A) Sen Spe B) Sen Spe
Binary
0.963 0.898
1.000 0.362
k-Means 1.000 0.751 1.000 0.806
LDA (linear) 0.838 0.976 0.813 0.991
SVM (linear) 0.438 0.999 0.688 0.999
AZT 0.963 0.518
0.938 0.941
In summary, the uniqueness of the PARAFAC
decomposition ensures the subject-specific
characterization of seizures as well as the natural
cleaning of the data by screening only for the
activity of interest. The analysis exposed here
corresponds to a segment by segment detection with
just one pattern seizure, which can be done online
and with low computational burden. The feature
extracted via one-component blind PARAFAC is a
good descriptor of pattern seizure (AUC>.97) and
the proposed adaptive zero-training (AZT) online
classification technique is a promising method for
fast unsupervised seizure detection. Better results
can be expected with visual selection of the epileptic
component by a specialist. Finally, a more complete
validation of this methodology is necessary in a
larger epilepsy EEG database.
REFERENCES
Miwakeichi F, Martínez-Montes E et al. (2004)
Decomposing EEG data into Space-Time-Frequency
Components using Parallel Factor Analysis.
Neuroimage 22: 1035-1045.
Martínez-Montes, E., Márquez-Bocalandro, Y., et al.
(2013). EEG Pattern Recognition by Multidimensional