Improving staff response to seizures on the epilepsy monitoring unit with online EEG seizure detection algorithms
User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two ele...
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| Vydané v: | Epilepsy & behavior Ročník 84; s. 99 - 104 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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United States
Elsevier Inc
01.07.2018
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| Abstract | User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time.
EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU.
EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h.
EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures.
•The added value of EEG seizure detection algorithms was assessed.•Two-thirds of missed seizures were detected by Encevis and 39% by BESA Epilepsy.•Algorithm detection preceded staff in 82% for Encevis and 84% for BESA Epilepsy.•Median false positive rate was 2.1 (BESA Epilepsy) and 4.9 (Encevis) per 24 h.•EEG seizure detection can improve detection time and number of detected seizures. |
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| AbstractList | User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time.
EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU.
EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h.
EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures.
•The added value of EEG seizure detection algorithms was assessed.•Two-thirds of missed seizures were detected by Encevis and 39% by BESA Epilepsy.•Algorithm detection preceded staff in 82% for Encevis and 84% for BESA Epilepsy.•Median false positive rate was 2.1 (BESA Epilepsy) and 4.9 (Encevis) per 24 h.•EEG seizure detection can improve detection time and number of detected seizures. User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time. EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU. EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h. EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures. User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time.OBJECTIVEUser safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time.EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU.METHODSEEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU.EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h.RESULTSEEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h.EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures.CONCLUSIONSEEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures. |
| Author | Jansen Holleboom, Lisanne Geertsema, Evelien Visser, Gerhard Rommens, Nicole Cox, Fieke |
| Author_xml | – sequence: 1 givenname: Nicole surname: Rommens fullname: Rommens, Nicole organization: Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands – sequence: 2 givenname: Evelien orcidid: 0000-0001-6676-2620 surname: Geertsema fullname: Geertsema, Evelien email: egeertsema@sein.nl organization: Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands – sequence: 3 givenname: Lisanne surname: Jansen Holleboom fullname: Jansen Holleboom, Lisanne organization: Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands – sequence: 4 givenname: Fieke surname: Cox fullname: Cox, Fieke email: fxoc@sein.nl organization: Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands – sequence: 5 givenname: Gerhard orcidid: 0000-0001-6708-6396 surname: Visser fullname: Visser, Gerhard email: gvisser@sein.nl organization: Stichting Epilepsie Instellingen Nederland (SEIN), Postbus 540, Hoofddorp 2130 AM, The Netherlands |
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| CitedBy_id | crossref_primary_10_1016_j_seizure_2020_06_002 crossref_primary_10_1016_j_yebeh_2023_109571 crossref_primary_10_3390_a12090176 crossref_primary_10_1017_cjn_2023_58 crossref_primary_10_1109_ACCESS_2019_2944273 crossref_primary_10_1016_j_seizure_2022_01_009 crossref_primary_10_1109_TCDS_2018_2868121 crossref_primary_10_3390_brainsci12091194 crossref_primary_10_1016_j_yebeh_2023_109518 crossref_primary_10_1016_j_heliyon_2024_e35973 crossref_primary_10_1016_j_eplepsyres_2022_106869 crossref_primary_10_1017_cjn_2020_268 crossref_primary_10_1002_epi4_70047 crossref_primary_10_3389_fnhum_2024_1484593 |
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| Title | Improving staff response to seizures on the epilepsy monitoring unit with online EEG seizure detection algorithms |
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