- Email: [email protected]
31. Clinical evaluation versus automatic detection of interictal epileptiform discharges – Who can we trust?
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Society Proceedings et al. / Clinical Neurophysiology 126 (2015) e29–e50
networks – including task-positive, sensorimotor and salience networks, while excluding mainly rostral frontal and ventral temporal cortices, and so-called default mode network located to posterior cingulate, precuneous cortex, and lateral occipital cortices. Moreover, some striking asymmetries were observed: left claustrum connecting more to the left inferior frontal gyrus (BA44), superior frontal gyrus and left thalamus. On the other hand, the right claustrum showed stronger connections to the right superior and inferior parietal lobule. Conclusions: Our findings provide novel evidence for the connectivity of the claustrum, suggesting a more specific role in sensorimotor and task-positive systems. References Eickhoff SB. Neuroimage 2012;59:2349–61. Jenkinson M et al. Neuroimage 2012;62:782–90. Laird AR. Front Neuroinformat 2009;3. doi:10.3389/neuro.11.023.2009. Nooner KB et al. Front Neurosci 2012;6. doi:10.3389/fnins.2012.00152. doi:10.1016/j.clinph.2014.10.188
30. Automated system for quantitative analysis of the eeg background—P. Jombík a, P. Spodniak b (a Hosp Zvolen, Dept Neurol, Slovakia, b Tech Univ Zvolen, Slovakia) Objectives: This study presents an automated quantitative EEG background analysis system in Microsoft Excel that resolves certain methodological problems in usual qualitative assessments of properties of the EEG background. Methods: EEG epochs without extra-cerebral signal artifacts with duration of 2–8 s were subjected to further processing by using an automated analysis system. The trends (identified by the least square method) and the DC offsets (identified by calculating the average value of the data row) were subtracted. The onsets and the ends of the data rows were smoothed by applying the Hanning’s window and subjected to Fast Fourier Transform (FFT). The power spectra over the band-pass of 0.5–45 Hz were calculated. Both the means of the spectral powers and the spectral powers of the major peaks in the rough delta to gamma spectral bands and in the more detailed narrower sub-bands (by splitting the delta into four; theta into three; alpha into three; beta into four and gamma into 3 subbands) were analyzed. In addition to the conventional topographic spectral band analysis, global coefficients of the following indexes were calculated: the ratios of the particular rough spectral powers, the diffused background slowing (Gotman et al., 1973), the spectral moments and their centers, the topographic spectral gradients and asymmetries in the peaks powers and frequencies. The individual findings were compared with the parameters which were based on provisional database of 76 normal adult EEG (52.16 y.; 15.95; 19– 83; mean age; SD; range). Conclusions: The EEG analysis is based on assessment of the properties of the background and the events. The detection of events which are blowing out from the background is relatively easy and straightforward. However, an assessment of the background might be sometimes a difficult task even for a trained EEG specialist. This system which is based on the commonly used Excel environment offers several advantages. The main advantage is the possibility of the easy accomplishment according to the needs of the user, and the development of the EEG research.
slow waves in adult patients. Electroencephalogr Clin Neurophysiol. 1973 Sep; 35(3):225–35. doi:10.1016/j.clinph.2014.10.189
31. Clinical evaluation versus automatic detection of interictal epileptiform discharges – Who can we trust?—R. Jancˇa a, P. ˇ mejla a, P. Kršek b, P. Jiruška c, P. Marusicˇ d (a DepartJezˇdík a, R. C ment of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic, b Department of Pediatric Neurology, Charles University, 2nd Faculty of Medicine, Motol University Hospital, Czech Republic, c Department of Developmental Epileptology, Institute of Physiology, Academy of Sciences of Czech Republic, Czech Republic, d Department of Neurology, Charles University, 2nd Faculty of Medicine, Motol University Hospital, Czech Republic) Objective: Interictal epileptiform discharges (IEDs) are electrographic hallmark of epilepsy. Information about the spatiotemporal distribution of IEDs in intracranial EEG is utilized to localize epileptogenic zone during the presurgical evaluation and plan the resection. Visual evaluation of long-term multi-channel intracranial recordings is extremely difficult and prone to bias. Clinicians usually assess only high-amplitude (high signal to noise ratio) discharge and low-amplitude IEDs can be overlooked or considered clinically insignificant. The goal of our study was to develop reliable automatic IED detectors to facilitate analysis of long-term recordings and increase the information yield of intracranial recordings. Methods: Seven intracranial EEG recordings were randomly selected from our database. Samples of five minutes duration from fifteen high-rate IED channels (525 min in total) were presented to three experienced EEG specialists for spike labelling. The readers independently reviewed the data and classified IEDs into two groups: obvious and ambiguous. The inter-reader agreement was evaluated and IEDs labelled by at least two readers were considered as a gold standard (GS). We have developed, tested and optimized novel IED detector using GS datasets and compared its performance with published detectors. Our detecting approach estimates the signal envelope distribution to discriminate IEDs from background activity. Results: Readers together labelled 6518 IEDs (53 ± 21% obvious, 47 ± 21% ambiguous). The reader’s maximal match was 58% in pair and agreement of all three readers was only 30% (Cohen’s kappa 0.14 ± 0.11). Detector’s performance was characterized by sensitivity 91 ± 12% and 8 ± 7 false positives per min and per channel. Its performance was 1.4 better than published detector. Examination of false positives revealed that substantial proportion had shape of reminiscent of IEDs, but with lower amplitude. More than 50% false positives were reclassified by readers as IEDs. In addition, regression analysis showed positive relationship between IEDs marked by readers and number of false positives. Conclusion: The inter-reader agreement in visual IED evaluation is poor. Even experienced readers can identify approximately 40% of IED, especially those with high signal-to-noise ratio. In contrast, automatic detector is 2.5 more sensitive and can identify also low-amplitude IEDs. Areas generating not only high- but also lowamplitude IEDs can be crucial for epileptogenic zone localization. Supported by grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012). doi:10.1016/j.clinph.2014.10.190
References Gotman J, Skuce DR, Thompson CJ, Gloor P, Ives JR, Ray WF. Clinical applications of spectral analysis and extraction of features from electroencephalograms with
32. Perioperative monitoring of cognitive functions by eventrelated potentials and psychometric tests—I. Holeckova a, J. Kletecka b, P. Brenkus c, P. Honzikova b, S. Zidek a, J. Benes b (a Dpt.
Society Proceedings et al. / Clinical Neurophysiology 126 (2015) e29–e50
networks – including task-positive, sensorimotor and salience networks, while excluding mainly rostral frontal and ventral temporal cortices, and so-called default mode network located to posterior cingulate, precuneous cortex, and lateral occipital cortices. Moreover, some striking asymmetries were observed: left claustrum connecting more to the left inferior frontal gyrus (BA44), superior frontal gyrus and left thalamus. On the other hand, the right claustrum showed stronger connections to the right superior and inferior parietal lobule. Conclusions: Our findings provide novel evidence for the connectivity of the claustrum, suggesting a more specific role in sensorimotor and task-positive systems. References Eickhoff SB. Neuroimage 2012;59:2349–61. Jenkinson M et al. Neuroimage 2012;62:782–90. Laird AR. Front Neuroinformat 2009;3. doi:10.3389/neuro.11.023.2009. Nooner KB et al. Front Neurosci 2012;6. doi:10.3389/fnins.2012.00152. doi:10.1016/j.clinph.2014.10.188
30. Automated system for quantitative analysis of the eeg background—P. Jombík a, P. Spodniak b (a Hosp Zvolen, Dept Neurol, Slovakia, b Tech Univ Zvolen, Slovakia) Objectives: This study presents an automated quantitative EEG background analysis system in Microsoft Excel that resolves certain methodological problems in usual qualitative assessments of properties of the EEG background. Methods: EEG epochs without extra-cerebral signal artifacts with duration of 2–8 s were subjected to further processing by using an automated analysis system. The trends (identified by the least square method) and the DC offsets (identified by calculating the average value of the data row) were subtracted. The onsets and the ends of the data rows were smoothed by applying the Hanning’s window and subjected to Fast Fourier Transform (FFT). The power spectra over the band-pass of 0.5–45 Hz were calculated. Both the means of the spectral powers and the spectral powers of the major peaks in the rough delta to gamma spectral bands and in the more detailed narrower sub-bands (by splitting the delta into four; theta into three; alpha into three; beta into four and gamma into 3 subbands) were analyzed. In addition to the conventional topographic spectral band analysis, global coefficients of the following indexes were calculated: the ratios of the particular rough spectral powers, the diffused background slowing (Gotman et al., 1973), the spectral moments and their centers, the topographic spectral gradients and asymmetries in the peaks powers and frequencies. The individual findings were compared with the parameters which were based on provisional database of 76 normal adult EEG (52.16 y.; 15.95; 19– 83; mean age; SD; range). Conclusions: The EEG analysis is based on assessment of the properties of the background and the events. The detection of events which are blowing out from the background is relatively easy and straightforward. However, an assessment of the background might be sometimes a difficult task even for a trained EEG specialist. This system which is based on the commonly used Excel environment offers several advantages. The main advantage is the possibility of the easy accomplishment according to the needs of the user, and the development of the EEG research.
slow waves in adult patients. Electroencephalogr Clin Neurophysiol. 1973 Sep; 35(3):225–35. doi:10.1016/j.clinph.2014.10.189
31. Clinical evaluation versus automatic detection of interictal epileptiform discharges – Who can we trust?—R. Jancˇa a, P. ˇ mejla a, P. Kršek b, P. Jiruška c, P. Marusicˇ d (a DepartJezˇdík a, R. C ment of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic, b Department of Pediatric Neurology, Charles University, 2nd Faculty of Medicine, Motol University Hospital, Czech Republic, c Department of Developmental Epileptology, Institute of Physiology, Academy of Sciences of Czech Republic, Czech Republic, d Department of Neurology, Charles University, 2nd Faculty of Medicine, Motol University Hospital, Czech Republic) Objective: Interictal epileptiform discharges (IEDs) are electrographic hallmark of epilepsy. Information about the spatiotemporal distribution of IEDs in intracranial EEG is utilized to localize epileptogenic zone during the presurgical evaluation and plan the resection. Visual evaluation of long-term multi-channel intracranial recordings is extremely difficult and prone to bias. Clinicians usually assess only high-amplitude (high signal to noise ratio) discharge and low-amplitude IEDs can be overlooked or considered clinically insignificant. The goal of our study was to develop reliable automatic IED detectors to facilitate analysis of long-term recordings and increase the information yield of intracranial recordings. Methods: Seven intracranial EEG recordings were randomly selected from our database. Samples of five minutes duration from fifteen high-rate IED channels (525 min in total) were presented to three experienced EEG specialists for spike labelling. The readers independently reviewed the data and classified IEDs into two groups: obvious and ambiguous. The inter-reader agreement was evaluated and IEDs labelled by at least two readers were considered as a gold standard (GS). We have developed, tested and optimized novel IED detector using GS datasets and compared its performance with published detectors. Our detecting approach estimates the signal envelope distribution to discriminate IEDs from background activity. Results: Readers together labelled 6518 IEDs (53 ± 21% obvious, 47 ± 21% ambiguous). The reader’s maximal match was 58% in pair and agreement of all three readers was only 30% (Cohen’s kappa 0.14 ± 0.11). Detector’s performance was characterized by sensitivity 91 ± 12% and 8 ± 7 false positives per min and per channel. Its performance was 1.4 better than published detector. Examination of false positives revealed that substantial proportion had shape of reminiscent of IEDs, but with lower amplitude. More than 50% false positives were reclassified by readers as IEDs. In addition, regression analysis showed positive relationship between IEDs marked by readers and number of false positives. Conclusion: The inter-reader agreement in visual IED evaluation is poor. Even experienced readers can identify approximately 40% of IED, especially those with high signal-to-noise ratio. In contrast, automatic detector is 2.5 more sensitive and can identify also low-amplitude IEDs. Areas generating not only high- but also lowamplitude IEDs can be crucial for epileptogenic zone localization. Supported by grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012). doi:10.1016/j.clinph.2014.10.190
References Gotman J, Skuce DR, Thompson CJ, Gloor P, Ives JR, Ray WF. Clinical applications of spectral analysis and extraction of features from electroencephalograms with
32. Perioperative monitoring of cognitive functions by eventrelated potentials and psychometric tests—I. Holeckova a, J. Kletecka b, P. Brenkus c, P. Honzikova b, S. Zidek a, J. Benes b (a Dpt.