Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing.
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| Title: | Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing. |
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| Authors: | Japaridze, Giorgi (AUTHOR), Loeckx, Dirk (AUTHOR), Buckinx, Tim (AUTHOR), Armand Larsen, Sidsel (AUTHOR), Proost, Renée (AUTHOR), Jansen, Katrien (AUTHOR), MacMullin, Paul (AUTHOR), Paiva, Natalia (AUTHOR), Kasradze, Sofia (AUTHOR), Rotenberg, Alexander (AUTHOR), Lagae, Lieven (AUTHOR), Beniczky, Sándor (AUTHOR) |
| Source: | Epilepsia (Series 4). Dec2023 Supplement 4, Vol. 64 Issue 4, pS40-S46. 7p. |
| Subjects: | Electroencephalography, Convolutional neural networks, Seizures (Medicine), Clinical trials, Artificial intelligence |
| Abstract: | Summary: Objective: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. Methods: We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one‐channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full‐array video‐EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video‐EEGs. Results: We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%–88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%–100%). The average false detection rate was.53/h (95% CI =.32–.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was.823 (IQR =.57–1). For the total recording duration, F1 score was.74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. Significance: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices. [ABSTRACT FROM AUTHOR] |
| Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 174472458 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Japaridze%2C+Giorgi%22">Japaridze, Giorgi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Loeckx%2C+Dirk%22">Loeckx, Dirk</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Buckinx%2C+Tim%22">Buckinx, Tim</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Armand+Larsen%2C+Sidsel%22">Armand Larsen, Sidsel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Proost%2C+Renée%22">Proost, Renée</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jansen%2C+Katrien%22">Jansen, Katrien</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22MacMullin%2C+Paul%22">MacMullin, Paul</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paiva%2C+Natalia%22">Paiva, Natalia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kasradze%2C+Sofia%22">Kasradze, Sofia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rotenberg%2C+Alexander%22">Rotenberg, Alexander</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lagae%2C+Lieven%22">Lagae, Lieven</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Beniczky%2C+Sándor%22">Beniczky, Sándor</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Epilepsia+%28Series+4%29%22">Epilepsia (Series 4)</searchLink>. Dec2023 Supplement 4, Vol. 64 Issue 4, pS40-S46. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Seizures+%28Medicine%29%22">Seizures (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+trials%22">Clinical trials</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Summary: Objective: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. Methods: We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one‐channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full‐array video‐EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video‐EEGs. Results: We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%–88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%–100%). The average false detection rate was.53/h (95% CI =.32–.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was.823 (IQR =.57–1). For the total recording duration, F1 score was.74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. Significance: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/epi.17200 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: S40 Subjects: – SubjectFull: Electroencephalography Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Seizures (Medicine) Type: general – SubjectFull: Clinical trials Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Japaridze, Giorgi – PersonEntity: Name: NameFull: Loeckx, Dirk – PersonEntity: Name: NameFull: Buckinx, Tim – PersonEntity: Name: NameFull: Armand Larsen, Sidsel – PersonEntity: Name: NameFull: Proost, Renée – PersonEntity: Name: NameFull: Jansen, Katrien – PersonEntity: Name: NameFull: MacMullin, Paul – PersonEntity: Name: NameFull: Paiva, Natalia – PersonEntity: Name: NameFull: Kasradze, Sofia – PersonEntity: Name: NameFull: Rotenberg, Alexander – PersonEntity: Name: NameFull: Lagae, Lieven – PersonEntity: Name: NameFull: Beniczky, Sándor IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 12 Text: Dec2023 Supplement 4 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00139580 Numbering: – Type: volume Value: 64 – Type: issue Value: 4 Titles: – TitleFull: Epilepsia (Series 4) Type: main |
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