Determining the Spike–Wave Index Using Automated Detection Software
| Autor: | Elisabeth E.M. Reus, Fieke M.E. Cox, Gerhard H. Visser |
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| Rok vydání: | 2019 |
| Předmět: |
Male
Physiology Computer science Electroencephalography 050105 experimental psychology 03 medical and health sciences Status Epilepticus 0302 clinical medicine Software Physiology (medical) medicine Humans 0501 psychology and cognitive sciences Child medicine.diagnostic_test business.industry 05 social sciences Signal Processing Computer-Assisted Pattern recognition Neurology Index (publishing) Feature (computer vision) Child Preschool Referral center Female Spike (software development) Neurology (clinical) Artificial intelligence Sleep business Spike wave Sensitivity (electronics) Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Journal of Clinical Neurophysiology. 38:198-201 |
| ISSN: | 0736-0258 |
| Popis: | PURPOSE The spike-wave index (SWI) is a key feature in the diagnosis of electrical status epilepticus during slow-wave sleep. Estimating the SWI manually is time-consuming and is subject to interrater and intrarater variability. Use of automated detection software would save time. Thereby, this software will consistently detect a certain EEG phenomenon as epileptiform and is not influenced by human factors. To determine noninferiority in calculating the SWI, we compared the performance of a commercially available spike detection algorithm (P13 software, Persyst Development Corporation, San Diego, CA) with human expert consensus. METHODS The authors identified all prolonged EEG recordings for the diagnosis or follow-up of electrical status epilepticus during slow-wave sleep carried out from January to December 2018 at an epilepsy tertiary referral center. The SWI during the first 10 minutes of sleep was estimated by consensus of two human experts. This was compared with the SWI calculated by the automated spike detection algorithm using the three available sensitivity settings: "low," "medium," and "high." In the software, these sensitivity settings are denoted as perception values. RESULTS Forty-eight EEG recordings from 44 individuals were analyzed. The SWIs estimated by human experts did not differ from the SWIs calculated by the automated spike detection algorithm in the "low" perception mode (P = 0.67). The SWIs calculated in the "medium" and "high" perception settings were, however, significantly higher than the human expert estimated SWIs (both P < 0.001). CONCLUSIONS Automated spike detection (P13) is a useful tool in determining SWI, especially when using the "low" sensitivity setting. Using such automated detection tools may save time, especially when reviewing larger epochs. |
| Databáze: | OpenAIRE |
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