Development of a stereo-EEG based seizure matching system for
clinical decision making in epilepsy surgery

J 2024

Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery

THOMAS, John; Chifaou ABDALLAH; Kassem JABER; Mays KHWEILEH; Olivier ARON et al.

Základní údaje

Originální název

Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery

Autoři

Vydání

JOURNAL OF NEURAL ENGINEERING, BRISTOL, IOP PUBLISHING LTD, 2024, 1741-2560

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30210 Clinical neurology

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.800

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14110/24:00137435

Organizační jednotka

Lékařská fakulta

Klíčová slova anglicky

epilepsy surgery; seizure matching; stereo-electroencephalography; precision medicine; phenotyping; seizure onset zone; interrater agreement

Štítky

14110127, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 10. 2024 12:49, Mgr. Tereza Miškechová

Anotace

V originále

Objective. The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity. Approach. We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity. Main results. The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75, p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers. Significance. We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.

Citovat

THOMAS, John; Chifaou ABDALLAH; Kassem JABER; Mays KHWEILEH; Olivier ARON; Irena DOLEŽALOVÁ; Vadym GNATKOVSKY; Daniel MANSILLA; Paivi NEVALAINEN; Raluca PANA; Stephan SCHUELE; Jaysingh SINGH; Ana SULLER-MARTI; Alexandra URBAN; Jeffery HALL; Francois DUBEAU; Louis MAILLARD; Philippe KAHANE; Jean GOTMAN a Birgit FRAUSCHER. Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery. JOURNAL OF NEURAL ENGINEERING. BRISTOL: IOP PUBLISHING LTD, 2024, roč. 21, č. 5, s. 1-13. ISSN 1741-2560. Dostupné z: https://doi.org/10.1088/1741-2552/ad7323.

@article{2446262,
   author = {Thomas, John and Abdallah, Chifaou and Jaber, Kassem and Khweileh, Mays and Aron, Olivier and Doležalová, Irena and Gnatkovsky, Vadym and Mansilla, Daniel and Nevalainen, Paivi and Pana, Raluca and Schuele, Stephan and Singh, Jaysingh and SullerandMarti, Ana and Urban, Alexandra and Hall, Jeffery and Dubeau, Francois and Maillard, Louis and Kahane, Philippe and Gotman, Jean and Frauscher, Birgit},
   article_location = {BRISTOL},
   article_number = {5},
   doi = {https://doi.org/10.1088/1741-2552/ad7323},
   keywords = {epilepsy surgery; seizure matching; stereo-electroencephalography; precision medicine; phenotyping; seizure onset zone; interrater agreement},
   language = {eng},
   issn = {1741-2560},
   journal = {JOURNAL OF NEURAL ENGINEERING},
   title = {Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery},
   url = {https://iopscience.iop.org/article/10.1088/1741-2552/ad7323},
   volume = {21},
   year = {2024}
}

TY  - JOUR
ID  - 2446262
AU  - Thomas, John - Abdallah, Chifaou - Jaber, Kassem - Khweileh, Mays - Aron, Olivier - Doležalová, Irena - Gnatkovsky, Vadym - Mansilla, Daniel - Nevalainen, Paivi - Pana, Raluca - Schuele, Stephan - Singh, Jaysingh - Suller-Marti, Ana - Urban, Alexandra - Hall, Jeffery - Dubeau, Francois - Maillard, Louis - Kahane, Philippe - Gotman, Jean - Frauscher, Birgit
PY  - 2024
TI  - Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery
JF  - JOURNAL OF NEURAL ENGINEERING
VL  - 21
IS  - 5
SP  - 1-13
EP  - 1-13
PB  - IOP PUBLISHING LTD
SN  - 17412560
KW  - epilepsy surgery
KW  - seizure matching
KW  - stereo-electroencephalography
KW  - precision medicine
KW  - phenotyping
KW  - seizure onset zone
KW  - interrater agreement
UR  - https://iopscience.iop.org/article/10.1088/1741-2552/ad7323
N2  - Objective. The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity. Approach. We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity. Main results. The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75, p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers. Significance. We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.
ER  -

THOMAS, John; Chifaou ABDALLAH; Kassem JABER; Mays KHWEILEH; Olivier ARON; Irena DOLEŽALOVÁ; Vadym GNATKOVSKY; Daniel MANSILLA; Paivi NEVALAINEN; Raluca PANA; Stephan SCHUELE; Jaysingh SINGH; Ana SULLER-MARTI; Alexandra URBAN; Jeffery HALL; Francois DUBEAU; Louis MAILLARD; Philippe KAHANE; Jean GOTMAN a Birgit FRAUSCHER. Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery. \textit{JOURNAL OF NEURAL ENGINEERING}. BRISTOL: IOP PUBLISHING LTD, 2024, roč.~21, č.~5, s.~1-13. ISSN~1741-2560. Dostupné z: https://doi.org/10.1088/1741-2552/ad7323.

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