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] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| 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] |
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| ISSN: | 00139580 |
| DOI: | 10.1111/epi.17200 |
