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F83. Full 10–20 EEG application in hospitalised infants is not associated with an increase in stress hormone levels
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Abstracts / Clinical Neurophysiology 129 (2018) e66–e141
hemisphere electrode coverage is important. Increased temporal sampling frequency can be expected to improve display resolution, while increased spatial sampling may not, unless signal magnitude is favorable against background.
F84. Sensitivity of persyst seizure detection for different electrographic seizure patterns in patients with status epilepticus— Musab Zorlu, David Chuang, Hind Kettani, Reza Zarnegar * (USA) ⇑
Presenting author.
doi:10.1016/j.clinph.2018.04.245
F83. Full 10–20 EEG application in hospitalised infants is not associated with an increase in stress hormone levels—Kimberley Whitehead *, Laura Jones, Pureza Laudiano-Dray, Judith Meek, Lorenzo Fabrizi (United Kingdom) ⇑
Presenting author.
Introduction: Neonatal EEG monitoring in pre-term and full-term infants offers a window onto neurological functioning, and full 10/20 electrode coverage captures important regional organisation. Nevertheless, 10/20 EEG application involves a relatively high level of handling and could be a potential stressor, especially in those infants who are already exhibiting higher stress levels. Salivary cortisol reflects activity of the hypothalamic pituitary axis: increased cortisol concentration indicates increased acute stress. To assess the impact of 10/20 EEG set-up on systemic stress levels in hospitalised infants, we measured salivary cortisol immediately before EEG application, and approximately 15 min after application (median time between swabs: 46 min), based on the latency of stressorinduced changes in cortisol concentration (Ramsay and Lewis, 2003). Methods: Subjects included 33 un-sedated infants with gestational age 26 + 4 41 + 6 weeks + days (median 36 + 4), corrected age 32 + 3 47 + 6 weeks + days (median 37 + 5), and postnatal age 0.5–95 days (median 5 days). Cortisol concentration pre- and post-EEG application was compared with paired Wilcoxon tests. Statistical significance was set at 0.05. Results: Median cortisol concentration pre-EEG application was 0.27 lg/dL (range 0.09–2.91), and post-EEG application was 0.37 lg/dL (range 0.03–1.74), values similar to those found in previous studies. There was no difference between cortisol concentration pre- and post-EEG application (p = .201). In particular, this was also the case for vulnerable sub-groups (corrected age: pre-term, n = 13; ward location: special care or high dependency unit, n = 14) (p P .432). Finally, we investigated whether baseline stress level influenced the effect of EEG application by splitting infants into those with higher pre-EEG cortisol concentration (P.25 lg/dL, n = 17) and lower pre-EEG cortisol concentration (<.25 lg/dL, n = 16). EEG application was associated with a decrease in cortisol concentration in infants who had a higher concentration at baseline (p = .023; median 0.31 lg/dL decrease, which is a median 41% decrease in the preEEG value), and no change in infants who had a lower cortisol concentration at baseline (p = .120). Conclusion: In this study we have shown that EEG application does not necessarily increase stress levels in hospitalised infants. Our findings are reassuring and support our clinical impression (based on behavioural observation, and review of heart rate and pulse oximetry acquired by the neonatal unit monitors where available) that EEG application can be well tolerated by both pre-term and full-term infants. Interestingly, we show that EEG application is actually associated with decreased stress levels in infants with higher cortisol concentration pre-EEG application. In line with this, tactile stimulation of the head and body is associated with decreased stress behaviours in neonates (Hernandez-Reif et al., 2007). doi:10.1016/j.clinph.2018.04.246
Introduction: With the rising use of continuous EEG monitoring, the ability to detect seizures rapidly and treat is becoming a standard of care. The issue for many institutions is the availability of an EEG reader to review EEG studies continuously. Persyst is a seizure detection software that allows health care professionals not trained in EEG to identify seizures rapidly at the bedside. We aim to determine if different electrographic patterns of status epilepticus (SE) affects detection rate by Persyst. Methods: Continuous EEG monitoring reports at New York Presbyterian Queens Hospital from January 1st, 2015 to December 31st, 2016 with the phrase ‘‘status epilepticus” were selected. EEG patterns were categorized by board certified epileptologists as (1) SE associated with repetitive focal seizures (SERFS) or (2) SE associated with the Ictal-Interictal continuum (SEIIC). SERFS were defined as focal origin with a clear ictal onset and offset and a definable inter-seizure interval. SEIIC were defined as having association with characteristic features in the ictal-interictal continuum such as periodic discharges (generalized, lateralized, bilateral independent or stimulus-induced) and/or rhythmic delta activity. The archived raw EEG for each case was analyzed using Persyst Software. If at least one seizure was detected, it was classified as positive. If there were zero seizure detections, then it was classified as negative. Cases which had insufficient raw EEG data available were excluded from this study. Statistical analysis was performed using Chi-Square test. Results: A total of 11 patients were identified with status epilepticus. Of the 11 patients, 5 were classified as SERFS and 6 were classified as SEIIC. Following analysis using Persyst, 5/5 patients with SERFS and 2/6 patients with SEIIC had positive seizure detections. Persyst was significantly more sensitive in detecting seizures in SERFS than in SIIC (p = 0.02). Conclusion: This study showed that Persyst is highly sensitive in detecting SERFS but ineffective in SEIIC. These findings are consistent with prior study on this topic. This study, although small in sample size, further adds to the literature showing that the electrographic pattern of status epilepticus may influence the sensitivity of seizure detection using automated seizure detection software such as Persyst. Future studies with a larger sample size would be helpful to further clarify this hypothesis. doi:10.1016/j.clinph.2018.04.247
F85. Deep learning for detection of epileptiform discharges from scalp EEG recordings—Michel J. van Putten *, Rafael de Carvalho, Marleen C. Tjepkema-Cloostermans (Netherlands) ⇑
Presenting author.
Introduction: Clinical use of computer assisted detection of epileptiform discharges is limited, and visual assessment generally outperforms current computer algorithms. Deep learning is a promising novel approach for the detection of epileptiform discharges, being able to process huge amounts of data and to learn by example. Here, we show pilot results of detecting interictal epileptiform discharges using different deep neural networks. Methods: We selected 50 routine 19-channel scalp EEGs from patients with focal epilepsies from our digital database. All epileptiform discharges (n = 1815) were annotaded by an experienced clinical neurophysiologist. Additionally, we added 50 EEGs that were
Abstracts / Clinical Neurophysiology 129 (2018) e66–e141
hemisphere electrode coverage is important. Increased temporal sampling frequency can be expected to improve display resolution, while increased spatial sampling may not, unless signal magnitude is favorable against background.
F84. Sensitivity of persyst seizure detection for different electrographic seizure patterns in patients with status epilepticus— Musab Zorlu, David Chuang, Hind Kettani, Reza Zarnegar * (USA) ⇑
Presenting author.
doi:10.1016/j.clinph.2018.04.245
F83. Full 10–20 EEG application in hospitalised infants is not associated with an increase in stress hormone levels—Kimberley Whitehead *, Laura Jones, Pureza Laudiano-Dray, Judith Meek, Lorenzo Fabrizi (United Kingdom) ⇑
Presenting author.
Introduction: Neonatal EEG monitoring in pre-term and full-term infants offers a window onto neurological functioning, and full 10/20 electrode coverage captures important regional organisation. Nevertheless, 10/20 EEG application involves a relatively high level of handling and could be a potential stressor, especially in those infants who are already exhibiting higher stress levels. Salivary cortisol reflects activity of the hypothalamic pituitary axis: increased cortisol concentration indicates increased acute stress. To assess the impact of 10/20 EEG set-up on systemic stress levels in hospitalised infants, we measured salivary cortisol immediately before EEG application, and approximately 15 min after application (median time between swabs: 46 min), based on the latency of stressorinduced changes in cortisol concentration (Ramsay and Lewis, 2003). Methods: Subjects included 33 un-sedated infants with gestational age 26 + 4 41 + 6 weeks + days (median 36 + 4), corrected age 32 + 3 47 + 6 weeks + days (median 37 + 5), and postnatal age 0.5–95 days (median 5 days). Cortisol concentration pre- and post-EEG application was compared with paired Wilcoxon tests. Statistical significance was set at 0.05. Results: Median cortisol concentration pre-EEG application was 0.27 lg/dL (range 0.09–2.91), and post-EEG application was 0.37 lg/dL (range 0.03–1.74), values similar to those found in previous studies. There was no difference between cortisol concentration pre- and post-EEG application (p = .201). In particular, this was also the case for vulnerable sub-groups (corrected age: pre-term, n = 13; ward location: special care or high dependency unit, n = 14) (p P .432). Finally, we investigated whether baseline stress level influenced the effect of EEG application by splitting infants into those with higher pre-EEG cortisol concentration (P.25 lg/dL, n = 17) and lower pre-EEG cortisol concentration (<.25 lg/dL, n = 16). EEG application was associated with a decrease in cortisol concentration in infants who had a higher concentration at baseline (p = .023; median 0.31 lg/dL decrease, which is a median 41% decrease in the preEEG value), and no change in infants who had a lower cortisol concentration at baseline (p = .120). Conclusion: In this study we have shown that EEG application does not necessarily increase stress levels in hospitalised infants. Our findings are reassuring and support our clinical impression (based on behavioural observation, and review of heart rate and pulse oximetry acquired by the neonatal unit monitors where available) that EEG application can be well tolerated by both pre-term and full-term infants. Interestingly, we show that EEG application is actually associated with decreased stress levels in infants with higher cortisol concentration pre-EEG application. In line with this, tactile stimulation of the head and body is associated with decreased stress behaviours in neonates (Hernandez-Reif et al., 2007). doi:10.1016/j.clinph.2018.04.246
Introduction: With the rising use of continuous EEG monitoring, the ability to detect seizures rapidly and treat is becoming a standard of care. The issue for many institutions is the availability of an EEG reader to review EEG studies continuously. Persyst is a seizure detection software that allows health care professionals not trained in EEG to identify seizures rapidly at the bedside. We aim to determine if different electrographic patterns of status epilepticus (SE) affects detection rate by Persyst. Methods: Continuous EEG monitoring reports at New York Presbyterian Queens Hospital from January 1st, 2015 to December 31st, 2016 with the phrase ‘‘status epilepticus” were selected. EEG patterns were categorized by board certified epileptologists as (1) SE associated with repetitive focal seizures (SERFS) or (2) SE associated with the Ictal-Interictal continuum (SEIIC). SERFS were defined as focal origin with a clear ictal onset and offset and a definable inter-seizure interval. SEIIC were defined as having association with characteristic features in the ictal-interictal continuum such as periodic discharges (generalized, lateralized, bilateral independent or stimulus-induced) and/or rhythmic delta activity. The archived raw EEG for each case was analyzed using Persyst Software. If at least one seizure was detected, it was classified as positive. If there were zero seizure detections, then it was classified as negative. Cases which had insufficient raw EEG data available were excluded from this study. Statistical analysis was performed using Chi-Square test. Results: A total of 11 patients were identified with status epilepticus. Of the 11 patients, 5 were classified as SERFS and 6 were classified as SEIIC. Following analysis using Persyst, 5/5 patients with SERFS and 2/6 patients with SEIIC had positive seizure detections. Persyst was significantly more sensitive in detecting seizures in SERFS than in SIIC (p = 0.02). Conclusion: This study showed that Persyst is highly sensitive in detecting SERFS but ineffective in SEIIC. These findings are consistent with prior study on this topic. This study, although small in sample size, further adds to the literature showing that the electrographic pattern of status epilepticus may influence the sensitivity of seizure detection using automated seizure detection software such as Persyst. Future studies with a larger sample size would be helpful to further clarify this hypothesis. doi:10.1016/j.clinph.2018.04.247
F85. Deep learning for detection of epileptiform discharges from scalp EEG recordings—Michel J. van Putten *, Rafael de Carvalho, Marleen C. Tjepkema-Cloostermans (Netherlands) ⇑
Presenting author.
Introduction: Clinical use of computer assisted detection of epileptiform discharges is limited, and visual assessment generally outperforms current computer algorithms. Deep learning is a promising novel approach for the detection of epileptiform discharges, being able to process huge amounts of data and to learn by example. Here, we show pilot results of detecting interictal epileptiform discharges using different deep neural networks. Methods: We selected 50 routine 19-channel scalp EEGs from patients with focal epilepsies from our digital database. All epileptiform discharges (n = 1815) were annotaded by an experienced clinical neurophysiologist. Additionally, we added 50 EEGs that were