Preliminary Comparison of Classifiers to Detect Spatio-spectral

Patterns of Epileptic Seizures via PARAFAC Decomposition

Marlis Ontivero-Ortega, Yalina García-Puente and Eduardo Martínez-Montes

Cuban Neuroscience Center, Havana, Cuba

1 OBJECTIVES

The automatic detection of epileptic seizures from

EEG recording is very important for clinical

diagnosis and monitoring and has become an issue

of major scientific and technological interest

(Orosco et al., 2013). In this work, we use the

spatio-spectral features extracted via multi-

dimensional Parallel Factor (PARAFAC) Analysis

of the EEG for seizure detection. This subject-

specific approach only requires extracting one

component that explains a seizure’s space-time-

frequency pattern. Then, a simple adaptive zero-

training technique (AZT) to classify the seizures,

with the additional advantages of being fast and

work online, is evaluated and compared with known

pattern recognition methodologies (LDA, SVM, k-

Means), according to its accuracy, sensitivity and

specificity on EEG recordings of two epileptic

paediatric patients.

2 METHODS

Two epileptic paediatric EEG data are used for

seizure detection. The first one (0.5h, 19 channels,

sampling frequency of 200Hz) corresponds to a

patient of the Center for Neurological Restoration, at

Havana (www.ciren.cu), while the second (4h, 23

channels, sampling frequency of 256Hz)

corresponds to patient chb01 of the epilepsy CHB-

MIT scalp EEG database, available online at

http://www.physionet.org/pn6/chbmit.

2.1 PARAFAC Model

The PARAFAC model is a multidimensional

generalization of the Principal Component Analysis,

with the advantage that the multilinear

decomposition is unique under very mild conditions,

without imposing orthogonality or statistical

independence among components (Miwakeichi et

al., 2004). In our case, the spectrograms of every

channel of an EEG segment are arranged into a 3D

array, indexed by time, frequency and electrodes

(spatial dimension). PARAFAC decomposes this

tensor into components, each with corresponding

temporal, spectral and spatial signatures

(Miwakeichi et al., 2004). The spatial and spectral

signatures obtained from PARAFAC analysis of a

pattern epileptic seizure, can be used for searching

these characteristics in new EEG segments. As the

epileptic activity can be explained by one or more

components, it is important to choose the component

better explaining the epileptic activity to be detected.

In this work, we explore the option of taking the first

component of PARAFAC “blindly”, which allows

for a faster and more automatic procedure. EEG

spectrograms and features (mean power ratio) were

found following the same procedure as in (Martínez-

Montes et al., 2013). This was done for the same

data sets using different segments’ length to test for

the effect of this practical parameter which defines

speed and computational load of the methodology.

Table 1 summarizes the data used.

Table 1: Number of seizure and non-seizure segments, for

different segments’ length.

Data Length = 2 s 4 s 6 s 10 s

Seiz/NonSeiz 30/957 19/475 14/315 11/187

Total 987 494 329 198

Seiz/NonSeiz 80/7120 42/3558 29/2371 19/1421

Total 7200 3600 2400 1440

2.2 Seizure Detection

LDA and SVM classifiers were used (10-fold cross-

validation) to detect seizures offline from the

features of all segments, using every seizure

segment as the pattern. In addition an ad-hoc binary

threshold (mean power ratio of 0.5) and k-means

clustering were used for comparison purposes. We

also introduce a simple adaptive zero-training

technique (AZT), based in the online classification

of each segment by computing the probability to

belong to two normal distributions that define the

Ontivero-Ortega M., García-Puente Y. and Martínez-Montes E..

Preliminary Comparison of Classiﬁers to Detect Spatio-spectral Patterns of Epileptic Seizures via PARAFAC Decomposition .

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Data 1 - Mean (thick) and Standard deviation

(thin) ROC curves for the mean power ratio. For the 5

classifiers the Mean and false/true positive rate Std bars

for automatic thresholds are shown. The tables show the

average Mean accuracy, sensitivity and specificity.

Figure 2: Data 2 - Mean (thick) and Standard deviation

(thin) ROC curves for the mean power ratio. For the 5

classifiers the Mean and false/true positive rate Std bars

for automatic thresholds are shown. The tables show the

average Mean accuracy, sensitivity and specificity.

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

Space-Time-Frequency Analysis. In V Latin American

Congress on Biomedical Engineering CLAIB 2011

May 16-21, 2011, Habana, Cuba (pp. 1150-1153).

Orosco, L., Correa, A. G., and Laciar, E. (2013). Review:

A Survey of Performance and Techniques for

Automatic Epilepsy Detection. Journal of Medical and

Biological Engineering, 33(6), 526-537.