Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression

Vilella, Laura; Zhu, Liang; Rani, M.R. Sandhya; Hampson, Johnson P.; Hampson, Jaison; Hupp, Norma J.; Sainju, Rup K.; Friedman, Daniel; Nei, Maromi; Scott, Catherine; Allen, Luke; Gehlbach, Brian K.; Schuele, Stephan; Harper, Ronald M.; Diehl, Beate; Bateman, Lisa M.; Devinsky, Orrin; Richerson, George B.; Zhang, Guo Qiang; Lhatoo, Samden D.; Lacuey, Nuria

doi:10.3389/fneur.2021.669517

Cited by 5 publications

(6 citation statements)

References 30 publications

(70 reference statements)

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“…Yet our population might be more relevant to the study of PGES, given that both SUDEP and PGES are more frequent in populations with refractory seizures 7,4,26 . Furthermore, our rate of PGES in GCS (74.4%) is in accordance with that reported in the literature 27,28 . Nevertheless, a larger population size is needed to confirm our findings and obtain better insight into prolonged PGES risk factors.…”

Section: Discussionsupporting

confidence: 85%

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Progressive slowing of clonic phase predicts postictal generalized EEG suppression

et al. 2022

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Objective: Postictal generalized electroencephalography (EEG) suppression (PGES) is a surrogate marker of sudden unexpected death in epilepsy (SUDEP).It is still unclear which ictal phenomena lead to prolonged PGES and increased risk of SUDEP. Semiology features of generalized convulsive seizure (GCS type 1) have been reported as a predictor of prolonged PGES. Progressive slowing of clonic phase (PSCP) has been observed in GCSs, with gradually increasing inhibitory periods interrupting the tonic contractions. We hypothesized that PSCP is associated with prolonged PGES. Methods:We analyzed 90 bilateral convulsive seizures in 50 consecutive patients (21 female; age: 11-62 years, median: 31 years) recruited to video-EEG monitoring. Five raters, blinded to all other data, independently assessed the presence of PSCP. PGES and seizure semiology were evaluated independently. We determined inter-rater agreement (IRA) for the presence of PSCP, and we evaluated its association, as well as that of other ictal features, with the occurrence of PGES, prolonged PGES (≥20 s) and very prolonged PGES (≥50 s) using multivariate logistic regression analysis. Results: We found substantial IRA for the presence of PSCP (percent agreement: 80%; beyond-chance agreement coefficient: .655). PSCP was an independent predictor of the occurrence of PGES and prolonged PGES (p < .001). All seizures with very prolonged PGES had PSCP. GCS type 1 was an independent predictor of occurrence of PGES (p = .02) and prolonged PGES (p = .03) but not of very prolonged PGES. Only half of the seizures with very prolonged PGES were GCS type 1.Significance: PSCP predicts prolonged PGES, emphasizing the importance of gradually increasing inhibitory phenomena at the end of the seizures. Our findings shed more light on the ictal phenomena leading to increased risk of SUDEP.These phenomena may provide basis for algorithms implemented into wearable devices for identifying GCS with increased risk of SUDEP.

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Section: Discussionsupporting

confidence: 85%

“…7,4,26 Furthermore, our rate of PGES in GCS (74.4%) is in accordance with that reported in the literature. 27,28 Nevertheless, a larger population size is needed to confirm our findings and obtain better insight into prolonged PGES risk factors.…”

Section: Discussionmentioning

confidence: 74%

Progressive slowing of clonic phase predicts postictal generalized EEG suppression

et al. 2022

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“…Epilepsy patients who die from SUDEP have no obvious cause or mechanism of death that can be identified at autopsy (Okanari et al, 2020 ). In epilepsy clinic populations, the incidence of SUDEP ranges between 1.1 and 2.9 per 1,000 patient-years, whereas it is 6.3–9.3 per 1,000 patients with intractable epilepsy, posing a significant public health concern (Zhao et al, 2021 ). While several multifactorial processes have been involved including cardiac (Devinsky et al, 2016a ), respiratory (Lacuey et al, 2018 , 2019 ; Vilella et al, 2019b , 2021 ), autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood (Okanari et al, 2020 ; Petrucci et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy

Tao

Lhatoo

et al. 2022

Front. Big Data

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Epilepsy affects ~2–3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.

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“…Therefore, in practice the duration of PGES is determined manually with visual inspection of EEG signal readings by clinical experts, who can leverage additional video recordings along with signals to identify high-amplitude artifacts that are not real EEG activities (Theeranaew et al, 2017). However, such a manual task is time-consuming and labor-intensive, and the judging criteria of PGES with artifacts by each clinical expert are not standardized, which may be subjective and unreliable (Zhao et al, 2021). Automatic PGES detection tools are highly desirable to help clinical experts review and annotate PGES in EEG recordings (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…For epilepsy patients who die from SUDEP, no anatomical or toxicological causes of death can be identified at autopsy (Okanari et al, 2020 ). In epilepsy clinic populations, the incidence of SUDEP ranges between 1.1 and 5.9 per 1,000 patient-years, whereas it is between 6.3 and 9.3 per 1,000 patient-years for those with intractable epilepsy, raising a significant public health concern (Zhao et al, 2021 ). While several multifactorial processes have been involved including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood (Okanari et al, 2020 ; Petrucci et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection

Huang

Lhatoo

et al. 2022

Front. Neuroinform.

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Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.

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Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression

Cited by 5 publications

References 30 publications

Progressive slowing of clonic phase predicts postictal generalized EEG suppression

Progressive slowing of clonic phase predicts postictal generalized EEG suppression

A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy

A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection

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