Nanoparticles for the Treatment of Alzheimer’s Disease_102718.pptx
Sigma Xi Presentation: Implementation of Time Frequency Analysis for Seizure Localization
1. Implementation of Time Frequency Analysis for
Seizure Localization: Phase II
Asha Reddy
Lake Highland Preparatory School, Orlando, Florida, USA
2. Research Question and Goal
Can EEG analysis of absence seizures and modelling of overtone harmonics be improved by replacing
spectrogram features with Cohen-Posch features?
The goal of this phase of the project is to investigate whether Cohen-Posch time-frequency analysis can
provide improved modelling of EEG scans, especially relating to the strength of overtones which are
necessary for the accurate localization of absence seizures.
4. Absence Epilepsy
- Absence seizures are caused by brief abnormal electrical activity in a person’s brain
- When people have absence seizures, they are not aware of what is happening around
them
- Absence seizures are mostly seen in young children
- An observer may not see the beginning or end of a seizure
- Because of the unpredictability and irregularity of absence seizure, prediction and
detection methods are vital
5. Electroencephalograms
- Epilepsy is generally modelled using electroencephalograms, or EEG scans, which are tests that
detect electrical activity in a person’s brain and consist of measurements of a set of potential
differences between pairs of scalp electrodes. (Baillet, 2011)
- Signals recorded from living neurological tissue are extremely noisy at all scales from individual
ion channels through collections of one or more neurons up to scalp-recorded EEG scans. The
noise recorded in these EEG’s causes uncertainty in the analysis of seizure data.
- It is vital to find an alternative method of analyzing and modeling EEG data to improve the
technique of epilepsy diagnosis.
Figure 1:
https://www.epilepsyqueensland.com.au/about-
epilepsy-epilepsy-queensland/seizure-
types/what-are-the-different-types-of-seizures
6. An Introduction to Time Frequency Analysis and the Cohen-Posch
Method
- Though the spectrogram is the most well known method of TFA for seizure analysis as well as
most other applications, there are multiple limitations.
- The spectrogram is “window dependent” making it difficult to separate the effects of the
window from the inherent information and characteristics of the signal.
- The Cohen-Posch method has the properties of being positive as well as having correct
marginals.
- Positive time-frequency dimensions are used in various applications such as the analysis of
multicomponent signals, biomedical engineering, and speech processing.
- Due to the mathematical advantages of this time-frequency method compared to older
methods, it is logical and necessary to extend the Cohen-Posch methodology to seizure data.
Figure 2:
A spectrogram plot from
MATLAB eeglab
Figure 3:
A Cohen-Posch positive
time frequency plot (not
seizure data).
7. Overtone Harmonics
- Overtone harmonics emphasize the fundamental or dominant tone in EEG frequencies
- This dominant tone acts as the primary seizure generator as it forces oscillations in
other parts of the brain
- It is a good marker of where absence epilepsy starts in the brain
- Comparing overtone harmonics between the Spectrogram and Cohen Posch method is
important in deciding whether the Cohen Posch method has improved qualities of EEG
detection
8. Phase I Summary
- In the first phase of this project, public EEG data of absence seizures was converted from a bipolar
montage to a monopolar montage, which was necessary in order to test various methods of time-
frequency analysis on this data.
- The main problem with multipolar montages is that they lose important data that is needed for
interpreting the signals. For this reason, the first phase of this project was necessary.
- This converted data was used in Phase II.
Figure 4:
A bipolar montage of absence seizure EEG
data
Figure 5:
A monopolar montage of absence seizure
EEG data
10. Methodology Overview
Investigate the mathematics behind
signal processing and learn basic
Matlab coding
Extract absence seizure EEG datasets
from the CHB-MIT database and run
spectrogram and Cohen-Posch code.
Beta test the spectrogram and Cohen-
Posch code in Matlab and debug when
necessary
Compare overtone harmonic
entropies of each method to
demonstrate enhanced performance
13. Results: Spectrogram vs Cohen Posch Methods on an Absence
Seizure
The Cohen-Posch method
rescales the spectrogram to
emphasize the overtone
harmonics of the fundamental
seizure frequency (f0)
The presence of the strong
fundamental seizure
frequency and overtones is
diagnostic of an absence
seizure.
The emphasized harmonics
allow for a more precise
detection of the seizure and
more accurate estimate of f0. Figure 6:
Spectrogram vs Cohen-Posch figures. The pink and red
frequencies are overtone harmonics.
14. Results: Comparing Overtones Using Entropy
The strength of the overtone harmonics at various times throughout the seizure was compared
between the Spectrogram and Cohen-Posch methods using a measure of entropy. A higher
entropy represents a higher certainty that the fundamental frequency will be detected. This
method of data analysis provided the following figures.
15. Pre-Ictal Entropies
Figure 7: Pre-Ictal (before seizure)
entropies. Top graph shows the overtone
entropy of the spectrogram. Bottom
graph shows the overtone entropy of the
Cohen-Posch method. The entropy ratio is
1.0625. This means there is not a
significant difference of overtone strength
before the seizure taking place.
16. Beginning Ictus Entropies
Figure 8: Beginning ictus (start of seizure)
entropies. Top graph shows the overtone
entropy of the spectrogram. Bottom graph
shows the overtone entropy of the Cohen-
Posch method. The entropy ratio is
3.8087. The Cohen-Posch is now providing
almost 4 times the overtone entropy of the
spectrogram.
17. Ictal Entropies
Figure 9: Ictal (during seizure) entropies. Top
graph shows the overtone entropy of the
spectrogram. Bottom graph shows the
overtone entropy of the Cohen-Posch
method. The entropy ratio is 5.4797. The
Cohen-Posch is now providing about 5 times
the overtone entropy of the spectrogram.
18. Interpretation: Comparing Overtones Using Entropy
Figure 10: This is a comparison of the overtone entropies between a spectrogram and the Cohen-
Posch method. It is clear that between time 2455 and 2470 seconds, the ratio of overtone strength
between the two methods clearly increases proving that the Cohen-Posch method does provide
enhanced modelling of overtone harmonics.
20. Conclusions and Possible Applications
- The results show that the overtone strength of the Cohen-Posch method is enhanced in
comparison to that of the Spectrogram.
- The emphasized harmonics allow for a more precise detection of the seizure and more accurate
detection of this fundamental frequency.
- These results may also be used for improving predictive models of absence seizures. If overtone
strength is more clearly modelled using the Cohen-Posch methodology, it could be an essential
discovery to absence seizure research. This method may be helpful in developing an objective
program which can detect active or upcoming absence seizures.
- The next phase of this project will be comparing other features of the spectrogram and Cohen-
Posch methods to determine whether the Cohen-Posch method can serve as an alternative
method of analyzing and modeling EEG data to improve the technique of epilepsy diagnosis.
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Https://www.epilepsyqueensland.com.au/about-epilepsy-epilepsy-queensland/seizure-types/what-are-the-different-
types-of-seizures
