Bsp ppt


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Detection of Epilepsy

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Bsp ppt

  1. 1. Page 1
  2. 2. OVERVIEW There is a great importance of the EEG as a non-One of them invasiveis Epilepsy. diagnostic tool in a wealth of neurological disorders, Page 2
  3. 3. The segmentation procedure assumes that the secondThese transients are order signal of great diagnostic characteristics after values and are reaching a new state characteristic of remain constant for EEGs of epileptic at least a couple of patients. 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. Page 3
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  6. 6. An example of above is in below figre, whiichshows 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). Page 6
  7. 7. 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 thetransient is contained in the moving window.There is a clear & simple technique to remedy this situation. We may limit the instantaneous power by clipping theprediction error at a threshold Ɵ, i.e set Ɵ is indicated by thedashed 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 thetransient is reduced in power. The rest of the signal is unaffected. Page 7
  8. 8. After we have seen how we may remove the undesirable influence of atransient on the segmentation process the natural question, if we may turn theargument around, is does eq. 4.105 yield a reasonable definition for transientbehavior? Generally speaking transients are not deterministic signals. Thesharp waves have to be seen in their proper context. The sharp waves in theburst 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 bycalling it a quasi-stationary segment.Recall the prediction error is a measure of the unexpectedness of the currentvalue of the signal, unexpected with regard to the type of activity in theadaptation window. In this way the prediction error is indeed a good indicator fornon-stationary behavior. Page 8
  9. 9. (n-1) +(n-1)= It will be sensitive to steep slopes & large amplitudes provided the wavelength(n) + 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)+ Page 9
  10. 10. From those, e(k)’s for which │e(k)│≥ Ɵ. Then, if > theta cap with yetanother threshold theta cap the triple {s(n-1), s(n), s(n+1)} of the signalvalues iss called a Spike and classified as a transient.Note that segmentation without transient elimination leads to meaninglessresults. If the background activity changes and the linear prediction filterdoes not adapt to the new signal structure it may happen thatsubsequently the total signal is classified as a transient as shown in fig.4.15b. If no segmentation and correspondingly no new adaptation takesplace at event 2, the whole burst phase would appear as a concatenationof sharp waves.While this is certainly not the best method from a theoretical view point(asthis prediction filter is neither in frequency nor in phase with the (optimum)matched filter for the sharp waves), nevertheless it has the advantage ofnot consuming any additional computation time. Page 10
  11. 11. For a demonstration of the detection of spikes in real life situations using theabove procedure we refer to the example discussed in [1] and given in detail inFig. 4.16. Page 11
  12. 12. 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. Page 12
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  14. 14. Another way of judging the perormance is to reconstruct the original EEGsignal using the ( ) coefficients of the prediction error filter(Wienerfilter), for each one of them. The O/P( of each of these filters), when excitedby computer generated white noise, must mimic the original EEG segmentwhile ignoring the phase relationship. The resemblance to the original EEGis a measure of performance of the proposed method.Fig. 4.18 shows how a simulated EEG siganl has been obtained by usingthe above concept and its comparison with the original signal. Page 14
  15. 15. Simulation of EEG signal Page 15
  16. 16. This brings to a close of our discussion on how one is not only able torecognize and classify EEG waveforms but also detect paraoxysms,i.e. transients associated with abnormalities. Page 16
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