Jennifer A Williams1, Fodé Abass Cisse2, Mike Schaekermann3, Foksouna Sakadi2, Nana Rahamatou Tassiou2, Gladia C Hotan4, Aissatou Kenda Bah2, Abdoul Bachir Djibo Hamani2, Andrew Lim5, Edward C W Leung6, Tadeu A Fantaneanu7, Tracey A Milligan8, Vidita Khatri8, Daniel B Hoch1, Manav V Vyas5, Alice D Lam1, Joseph M Cohen9, Andre C Vogel10, Edith Law3, Farrah J Mateen11. 1. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Division of Clinical Neurophysiology, Massachusetts General Hospital, Boston, MA, USA. 2. Department of Neurology, Ignace Deen Hospital, Conakry, Guinea. 3. School of Computer Science, University of Waterloo, ON, Canada. 4. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. 5. Department of Neurology, University of Toronto, ON, Canada; Department of Neurology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. 6. Division of Pediatric Neurology, Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada. 7. Division of Neurology, Department of Medicine, University of Ottawa, ON, Canada. 8. Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA. 9. Division of Clinical Neurophysiology, Massachusetts General Hospital, Boston, MA, USA. 10. Neurological Clinical Research Institute, Massachusetts General Hospital, Boston, MA, USA. 11. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Neurological Clinical Research Institute, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: fmateen@mgh.harvard.edu.
Abstract
PURPOSE: Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea. METHODS: Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth™ connection to an Android™ tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10-20 EEG recordings for identification of epileptiform and non-epileptiform abnormalities. RESULTS: 97 children meeting the International League Against Epilepsy's definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%. CONCLUSIONS: The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2+crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.
PURPOSE:Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea. METHODS:Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth™ connection to an Android™ tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10-20 EEG recordings for identification of epileptiform and non-epileptiform abnormalities. RESULTS: 97 children meeting the International League Against Epilepsy's definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%. CONCLUSIONS: The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2+crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.
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