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PRESENTED BY-

PRIYA SRIVASTAVA(090105801)

E.I.E, 4th yr.

DETECTION OF epilepsy

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OVERVIEW

There is a great

importance of the EEG as a non-invasive

diagnostic tool in a wealth of

neurological disorders,

One of them is Epilepsy.

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The segmentation procedure assumes

that the second order signal

characteristics after reaching a new state remain constant for at least a couple of

seconds.

It is therefore badly affected by the occurrence of short-time non- stationaries i.e.

transients, which are typically 100 ms or less in

duration.

These transients are of great diagnostic

values and are characteristic of EEGs of epileptic

patients.

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An example of above is in below figre, whiich shows an EEG of the burst suppression type.

Observe that the suppression period is interrupted by a sharp wave(event 1) and subsequently followed by a burst (event 2).

The linear prediction filter adapted within the suppression period.

The corresponding SEM fig. above clearly exhibits sharp jumps as the transient enters and leaves the moving window of 2 s length as indicated by the arrows 3 & 4(Fig. 4.15b).

Then this would lead to a meaningless segmentation at event 3. The reason for this behavior is seen when examining the prediction error(fig. 4.15 c).

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The transient leads to isolated high values of the prediction error.

Consequently, a constant high value of the power term SEM results as long as the transient

is contained in the moving window.

There is a clear & simple technique to remedy this situation.

We may limit the instantaneous power by clipping the prediction error at a threshold Ɵ, i.e set Ɵ is indicated by the dashed line in Fig. 4.15c. Fig 4.15d is the SEM as calculated from the clipped prediction error, the jumps are no longer present and threshold

is reached at event 2 as desired.

The signal reconstructed form the clipped prediction error is shown in Fig. 4.15e.

Within the suppression segment, only the transient is reduced in power. The rest of the

signal is unaffected.

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After we have seen how we may remove the undesirable influence of a transient on the segmentation process the natural question, if we may turn the argument around, is does eq. 4.105 yield a reasonable definition for transient behavior? Generally speaking transients are not deterministic signals. The sharp waves have to be seen in their proper context. The sharp waves in the burst phase of Fig. 4.15a are not regarded as such by the electro-encephalographer for the simple reason that they are not isolated. Instead, the “burst” is thought to reflect a new state of the brain, which we formalize by calling it a quasi-stationary segment.

Recall the prediction error is a measure of the unexpectedness of the current value of the signal, unexpected with regard to the type of activity in the adaptation window. In this way the prediction error is indeed a good indicator for non-stationary behavior.

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It will be sensitive to steep slopes & large amplitudes provided the wavelength is different from those encountered during adaptation. In this way clipping the prediction error provides us with the desired splitting of the signal into a quasi-stationary part (below threshold) and local non-stationaries(above threshold).However, experience has shown that criterion given by Eq. 4.105 with a threshold setting suitable for segmantation is far too sensitive for transient detection. EEG spikes generally have a duration of 50-100 ms. As a reasonable method for the elimination o ffalse alarm caused by random fluctuations in the prediction error it is the elimination of false alarm caused by random fluctuations in the prediction error power with this time constant. Accordingly, the following heuristic criterion is adopted as suggested in [1], i.e.

=

=

(n-1) +

(n) +

(n-1)

(n-1)+

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From those, e(k)’s for which │e(k)│≥ Ɵ. Then, if > theta cap with yet another threshold theta cap the triple {s(n-1), s(n), s(n+1)} of the signal values iss called a Spike and classified as a transient.Note that segmentation without transient elimination leads to meaningless results. If the background activity changes and the linear prediction filter does not adapt to the new signal structure it may happen that subsequently the total signal is classified as a transient as shown in fig. 4.15b. If no segmentation and correspondingly no new adaptation takes place at event 2, the whole burst phase would appear as a concatenation of sharp waves.While this is certainly not the best method from a theoretical view point(as this prediction filter is neither in frequency nor in phase with the (optimum) matched filter for the sharp waves), nevertheless it has the advantage of not consuming any additional computation time.

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For a demonstration of the detection of spikes in real life situations using the above procedure we refer to the example discussed in [1] and given in detail in Fig. 4.16.

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OVERALL PERFORMANCE

The only real way to find out would be to construct the entire algorithm which takes the EEG as input & produces a diagnosis, say healthy or sick, as output and then compare it with that given by the neurophysiologist.Nevertheless, we give an example, the most interesting, from a clinical stand point that demonstrates the effectiveness of the proposed method on four channels of an EEG with paroxysmal potentials[1] as shown in Fig. 4.17. Note how well the spike and wave patterns are separately segmented and observe that the most pronounced individual spikes are detected simultaneously in all the channels. Also the train of rhythmical delta waves in channels 1 and 3 are clearly identified.

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Another way of judging the perormance is to reconstruct the original EEG signal using the ( ) coefficients of the prediction error filter(Wiener filter), for each one of them. The O/P( of each of these filters), when excited by computer generated white noise, must mimic the original EEG segment while ignoring the phase relationship. The resemblance to the original EEG is a measure of performance of the proposed method.Fig. 4.18 shows how a simulated EEG siganl has been obtained by using the above concept and its comparison with the original signal.

Page 15Simulation of EEG signal

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This brings to a close of our discussion on how one is not only able to recognize and classify EEG waveforms but also detect paraoxysms, i.e. transients associated with abnormalities.

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