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1 Muscle artifact removal in an Epilepsy Monitoring Unit Highlighted application:

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Presentation on theme: "1 Muscle artifact removal in an Epilepsy Monitoring Unit Highlighted application:"— Presentation transcript:

1 1 Muscle artifact removal in an Epilepsy Monitoring Unit Highlighted application:

2 2 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 artifact 1 Focal ictal beta discharges localize the ictal onset zone accurately and are highly predictive of excellent postsurgical outcome 2. A low-pass filter with cut-off frequency of 15 Hz often removes this ictal beta activity and does not completely remove muscle artifact 1 S.S. Spencer et al. Neurology 1985; 35: G.A. Worrell et al. Epilepsia 2002; 43:

3 3 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 method compared to the existing software available for muscle artifact removal

4 4 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.

5 5 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.

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19 19 Muscle artifact-filtered EEG After muscle artifact removal, left temporal lobe epileptic activity is now clearly visible

20 20 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 ( 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.

21 21 Results Muscle artifact contamination24/26 (92%) Easier interpretation*24/24 (100%) Improved interpretation* Earlier detection of ictal-onset in EEG Better localization onset Onset pattern of higher frequency Localized onset beta activity only after removal of muscle artifact 11/26 (42%) 9/26 (35%) 7/26 (27%) 8/26 (31%) 5/26 (19%) Degradation of EEG by the method0/26 (0%) * by the new method compared to currently available software for muscle artifact removal

22 22 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.

23 23 EEG as obtained with current available software Ictal onset: R posterior Unreadable due to muscle artifact

24 24 Muscle artifact-filtered EEG Muscle artifact removal allowed 1. earlier detection, 2. recognition of low voltage fast activity, 3. more confidence in focal onset at T6-O2

25 25 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.

26 26 EEG as obtained with current available software

27 27 Ictal onset in left frontocentral regions Muscle artifact-filtered EEG

28 28 (next 10 sec) EEG as obtained with current available software

29 29 Later spread towards left temporal lobe Muscle artifact-filtered EEG (next 10 sec)

30 30 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

31 31 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

32 32 After removal of muscle artifact, the EEG appeared lateralized with low voltage slower rythms over the left temporal lobe Muscle artifact-filtered EEG

33 33 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


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