{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detection-of-tonic-clonic-seizures-in","title":"Detection of tonic-clonic seizures in children with epilepsy","arxiv_id":null,"date":"2023-11-30","proceeding":"University of South America 2023 11","authors":["Diego Romero","Claudia Zavaleta","Eisel Pinado","Jamila Vitella"],"abstract":"Epilepsy, a neurological condition characterized by recurrent, unprovoked seizures, profoundly impacts the lives of approximately 50 million individuals worldwide. Pediatric epilepsy, affecting around 470,000 children under 14 years old in the United States alone, has unique challenges encompassing health, education, and overall quality of life. Tonic-clonic seizures are particularly significant in pediatric epilepsy, this particular type of seizure needs effective detection mechanisms due to its inherent risks. This project proposes a digital signal processing algorithm for a portable, lightweight device for real-time monitoring of tonic-clonic seizures in children aged 6 to 14. The proposed algorithm extracts the most relevant features of an EEG signal and is capable of counting  each seizure episode during the monitoring time. This project has the potential to enhance healthcare professionals' decision-making processes, thereby directly influencing and improving the quality of life of patients and their families.","url_abs":"https://docs.google.com/document/d/1Lvu1CYJ6rvKXLj1K0g2A8eMRAt6NvyUm/edit","url_pdf":"https://docs.google.com/document/d/1Lvu1CYJ6rvKXLj1K0g2A8eMRAt6NvyUm/edit","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detection-of-tonic-clonic-seizures-in","repo_url":"https://github.com/jaamiila/IntroSe-ales","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"eeg-1","task_name":"EEG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}