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S127. Computer aided evaluation of intracerebral depth electrode recordings by automatic seizure detection
Abstracts / Clinical Neurophysiology 129 (2018) e142–e212
S126. A study on the electrophysiological mechanism of human hippocampus in propofol-induced amnesia: An ECoG study— Seung-Hyun Jin *, Chun Kee Chung (Republic of Korea) ⇑
Presenting author.
Introduction: General anesthesia is known to be a drug-induces, reversible condition that includes specific behavioral and physiological traits, such as unconsciousness, amnesia, analgesia, and akinesia (Brown et al., 2010). Among those characteristic features, the underlying mechanisms of propofol-induces amnesia still remains unknown. Propofol is a commonly used anesthetic drug and is known to induce anesthetic state through enhancing inhibition at GABAA receptors. The hippocampal formation occupies a central position for generation of memory (Perouansky and Pearce, 2011). From an electrophysiological perspective, hippocampal formation is a central theta rhythm generator controlled by GABAergic hippocampal interneurons (Buzsaki, 2006). Taken together, we hypothesized that enhancement of inhibition at GABAA receptors induced by propofol influences on theta oscillations of hippocampal formation which leads to propofol-induced amnesia. To address this hypothesis, we investigated an electrocorticography (ECoG) signals obtained from mesial temporal lobe epilepsy patients (MTLE) with hippocampal sclerosis (HS). Methods: We recruited five MTLE with HS. Because we are especially interested in the hippocampal theta oscillations, we only included the patients who had depth-electrodes over hippocampal formation. Two patients had the right HS and 3 patients had the left HS. ECoG signals were recorded during 3 different awaked states such as, resting-state, sleep state and word memory task. In addition, ECoG were recorded under general anesthesia with 3 different propofol concentrations of 5 lg/ml, 4 lg/ml and 3 lg/ml. ECoG signals were epoched for 2 min. Bad channel removal, drift removal, re-referencing (common average reference), and low-pass filter of 100 Hz were applied. We evaluated relative theta power, theta/beta ratio, and cross-frequency phase-amplitude coupling analysis between theta (4–7 Hz) and beta (13–30 Hz) frequency band. Modulation index representing theta phase and beta amplitude coupling was derived. Results: Hippocampal formation consistently showed the reduced relative theta power during propofol-induced state compared with those in the resting-state, sleep and memory task conditions. The theta/beta ratios of hippocampal formation during propofolinduced state and during memory task showed similar levels between them, but, lower than those during resting-state and sleep conditions. It is notable that the modulation index of theta phase and beta amplitude at hippocampal formation was higher during propofol-induced state than other conditions. Conclusion: Our results suggest that this specific theta-beta coupling occurred at hippocampal formation during propofol-induced state could be a possible electrophysiological mechanism for explaining propofol-induced amnesia. Acknowledgements: This study was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2015R1D1A1A02061486).
Introduction: Preoperative evaluation of patients with medically intractable epilepsies requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters. Methods: The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate. Results: The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity. Conclusion: We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic. doi:10.1016/j.clinph.2018.04.487
S128. Oscillatory responses evoked by single-pulse electrical stimulation in human cerebral cortex – A Cortico-Cortical Evoked Potential (CCEP) study—Takuro Nakae *, Riki Matsumoto, Masaya Togo, Hirofumi Takeyama, Katsuya Kobayashi, Akihiro Shimotake, Masao Matsuhashi, Yukihiro Yamao, Takayuki Kikuchi, Kazumichi Yoshida, Takeharu Kunieda, Akio Ikeda, Susumu Miyamoto (Japan) ⇑
doi:10.1016/j.clinph.2018.04.486
S127. Computer aided evaluation of intracerebral depth electrode recordings by automatic seizure detection—Franz Fürbass 1,*, Pauly Ossenblok 2, Albert J. Colon 2, Gerhard Gritsch 1, Tilmann Kluge 1 (1 Austria, 2 Netherlands) ⇑
Presenting author.
e189
Presenting author.
Introduction: Human brain operates with oscillatory activity in different frequency bands. For example, mu rhythm is well known as an idling rhythm observed in the central area related to motor function. We observed some rhythmic pattern in the averaged waveforms in the CCEP investigation (called ‘‘oscillatory response” here), which is originally a method to trace connectivity by single electrical
S126. A study on the electrophysiological mechanism of human hippocampus in propofol-induced amnesia: An ECoG study— Seung-Hyun Jin *, Chun Kee Chung (Republic of Korea) ⇑
Presenting author.
Introduction: General anesthesia is known to be a drug-induces, reversible condition that includes specific behavioral and physiological traits, such as unconsciousness, amnesia, analgesia, and akinesia (Brown et al., 2010). Among those characteristic features, the underlying mechanisms of propofol-induces amnesia still remains unknown. Propofol is a commonly used anesthetic drug and is known to induce anesthetic state through enhancing inhibition at GABAA receptors. The hippocampal formation occupies a central position for generation of memory (Perouansky and Pearce, 2011). From an electrophysiological perspective, hippocampal formation is a central theta rhythm generator controlled by GABAergic hippocampal interneurons (Buzsaki, 2006). Taken together, we hypothesized that enhancement of inhibition at GABAA receptors induced by propofol influences on theta oscillations of hippocampal formation which leads to propofol-induced amnesia. To address this hypothesis, we investigated an electrocorticography (ECoG) signals obtained from mesial temporal lobe epilepsy patients (MTLE) with hippocampal sclerosis (HS). Methods: We recruited five MTLE with HS. Because we are especially interested in the hippocampal theta oscillations, we only included the patients who had depth-electrodes over hippocampal formation. Two patients had the right HS and 3 patients had the left HS. ECoG signals were recorded during 3 different awaked states such as, resting-state, sleep state and word memory task. In addition, ECoG were recorded under general anesthesia with 3 different propofol concentrations of 5 lg/ml, 4 lg/ml and 3 lg/ml. ECoG signals were epoched for 2 min. Bad channel removal, drift removal, re-referencing (common average reference), and low-pass filter of 100 Hz were applied. We evaluated relative theta power, theta/beta ratio, and cross-frequency phase-amplitude coupling analysis between theta (4–7 Hz) and beta (13–30 Hz) frequency band. Modulation index representing theta phase and beta amplitude coupling was derived. Results: Hippocampal formation consistently showed the reduced relative theta power during propofol-induced state compared with those in the resting-state, sleep and memory task conditions. The theta/beta ratios of hippocampal formation during propofolinduced state and during memory task showed similar levels between them, but, lower than those during resting-state and sleep conditions. It is notable that the modulation index of theta phase and beta amplitude at hippocampal formation was higher during propofol-induced state than other conditions. Conclusion: Our results suggest that this specific theta-beta coupling occurred at hippocampal formation during propofol-induced state could be a possible electrophysiological mechanism for explaining propofol-induced amnesia. Acknowledgements: This study was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2015R1D1A1A02061486).
Introduction: Preoperative evaluation of patients with medically intractable epilepsies requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters. Methods: The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate. Results: The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity. Conclusion: We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic. doi:10.1016/j.clinph.2018.04.487
S128. Oscillatory responses evoked by single-pulse electrical stimulation in human cerebral cortex – A Cortico-Cortical Evoked Potential (CCEP) study—Takuro Nakae *, Riki Matsumoto, Masaya Togo, Hirofumi Takeyama, Katsuya Kobayashi, Akihiro Shimotake, Masao Matsuhashi, Yukihiro Yamao, Takayuki Kikuchi, Kazumichi Yoshida, Takeharu Kunieda, Akio Ikeda, Susumu Miyamoto (Japan) ⇑
doi:10.1016/j.clinph.2018.04.486
S127. Computer aided evaluation of intracerebral depth electrode recordings by automatic seizure detection—Franz Fürbass 1,*, Pauly Ossenblok 2, Albert J. Colon 2, Gerhard Gritsch 1, Tilmann Kluge 1 (1 Austria, 2 Netherlands) ⇑
Presenting author.
e189
Presenting author.
Introduction: Human brain operates with oscillatory activity in different frequency bands. For example, mu rhythm is well known as an idling rhythm observed in the central area related to motor function. We observed some rhythmic pattern in the averaged waveforms in the CCEP investigation (called ‘‘oscillatory response” here), which is originally a method to trace connectivity by single electrical