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Muscle artifact removal in an Epilepsy Monitoring Unit. Highlighted application:. Introduction. Muscle artifact in EEG recordings is a common problem: we found that muscle artifact interferred with the interpretation of ictal EEG recordings in around 90% of cases

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introduction
Introduction
  • Muscle artifact in EEG recordings is a common problem: we found that muscle artifact interferred with the interpretation of ictal EEG recordings in around 90% of cases
  • Ictal EEGs are often unreadable due to muscle artifact1
  • Focal ictal beta discharges localize the ictal onset zone accurately and are highly predictive of excellent postsurgical outcome2. A low-pass filter with cut-off frequency of 15 Hz often removes this ictal beta activity and does not completely remove muscle artifact

1S.S. Spencer et al. Neurology 1985; 35: 1567-1575.

2G.A. Worrell et al. Epilepsia 2002; 43: 277-282

slide3
Aims
  • To study the impact of muscle artifact on the readability of ictal EEG
  • To study the impact of our new muscle artifact removal algorithm on the readability of ictal EEG.
  • To study the improvement of the new methodcompared to the existing software available for muscle artifact removal
methods
Methods

We have developed a new algorithm to remove muscle artifact from ictal recordings. The method is semi-automatic* and user-dependent.

The technique is illustrated in the next slides:

The original EEG was an ictal recording of a patient with temporal lobe epilepsy. The original EEG was unreadable due to muscle artifact. You will have to click 15 times, and at each click, muscle artifact will be removed. After 15 clicks, we thought that all muscle artifact was removed. Left temporal lobe ictal activity is now obviously present.

*A fully automated muscle artifact removal with the method is possible but still under research.

slide5

The cursor is at the bottom of the stack. At each click, it

will move upward and a part of the muscle artifact will be removed.

slide19

After muscle artifact removal, left temporal lobe

epileptic activity is now clearly visible

Muscle artifact-filtered EEG

methods20
Methods
  • We selected one ictal EEG of 26 patients with refractory partial epilepsy, who underwent a presurgical evaluation at UZ Gasthuisberg, Leuven.
      • All patients had concordant data (clinic, EEG, MRI, ictal SPECT, FDG-PET and neuropsychology).
      • We selected the ictal EEG of the seizure during which an informative ictal SPECT injection was given, in order to have another functional “gold standard” in cases where ictal EEG was not informative (but not discordant).
  • The muscle artifact-filtered EEG was compared to the original band pass filtered (0.3-35 Hz *standard clinical settings) EEG.
    • study the improvement of our method compared to the existing available software.
  • We present our preliminary findings of an unblinded neurologist.1
  • The same study with two blinded neurologist is planned in the near future.

1 These results were submitted for presentation at the 26th International Epilepsy

Congress in Paris, August 2005.

results
Results

* by the new method compared to currently available software for muscle

artifact removal

example 1
Example 1
  • Patient was a 31 year old woman with epilepsy since age 16 years.
  • Seizure frequency: 20 per month.
  • Aura: scotomata and blindness.
  • SISCOM: cfr figure: area of hyperperfusion right posterior
  • At the site of hyperperfusion, we suspected a small focal cortical dysplasia on 1.5 T MRI. A 3T MRI is planned to confirm this.
  • The ictal EEG as obtained with current available software and the muscle artifact filtered of this patient are presented in the next two slides.
slide23

Ictal onset:

R posterior

Unreadable due to muscle artifact

EEG as obtained with current available software

slide24

Muscle artifact removal allowed 1. earlier detection, 2. recognition of

low voltage fast activity, 3. more confidence in focal onset at T6-O2

Muscle artifact-filtered EEG

example 2
Example 2
  • This 38 year old woman had refractory partial epilepsy since the age of 5 years after cerebral trauma affecting the left hemisphere
  • Her right hand was functional and her language centers were on the left. Therefore, we did not consider a hemispherotomy
  • In view of the sclerotic hippocampus on the left (arrow), we considered the possibility of a left temporal lobe resection if we could establish that all her seizures started in the left temporal lobe.
  • The ictal EEG was unreadable due to muscle artifact.
  • The EEG after removal of muscle artifact clearly showed ictal onset in frontocentral regions with spread towards the temporal lobe.
  • She was not offered surgery.
slide27

Ictal onset in left frontocentral regions

Muscle artifact-filtered EEG

slide29

Muscle artifact-filtered EEG

(next 10 sec)

Later spread towards left temporal lobe

example 3
Example 3
  • This 36 year old woman suffered from refractory mesial temporal lobe epilepsy associated with left hippocampal sclerosis (white arrow).
  • Ictal EEG was contaminated with muscle artifact and did not show obvious epileptic activity
  • After muscle artifact removal, low voltage semirythmic activity over the left temporal lobe was evident.
  • She underwent a left temporal lobe resection and has been seizure free for more than two years
slide31

Right temporal

derivations

Left temporal

derivations

Ictal EEG showed muscle artifact and no clear lateralization

or epileptic activity over both temporal derivations

EEG as obtained with current available software

slide32

After removal of muscle artifact, the EEG appeared lateralized

with low voltage slower rythms over the left temporal lobe

Muscle artifact-filtered EEG

conclusion
Conclusion
  • Our new algorithm to remove muscle artifact
    • Is fast
    • Is user-friendly
    • Can be implemented on any digital EEG workstation
    • Makes interpretation of 90% of the ictal EEGs much easier
    • Allows to detect seizure onset earlier, low voltage fast activity more frequent, and to pinpoint a more focal seizure onset in around 40% of cases