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Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates.
Mader, Johannes; Hartmann, Manfred; Dressler, Anastasia; Oberdorfer, Lisa; Rona, Zsofia; Glatter, Sarah; Czaba-Hnizdo, Christine; Herta, Johannes; Kluge, Tilmann; Werther, Tobias; Berger, Angelika; Koren, Johannes; Klebermass-Schrehof, Katrin; Giordano, Vito.
Afiliación
  • Mader J; Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
  • Hartmann M; Center for Health & Bioresources, AIT (Austrian Institute of Technology), Vienna, Austria.
  • Dressler A; Center for Health & Bioresources, AIT (Austrian Institute of Technology), Vienna, Austria.
  • Oberdorfer L; Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
  • Rona Z; Member of the ERN EoiCare, Lyon, France.
  • Glatter S; Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
  • Czaba-Hnizdo C; Department of Paediatric and Adolescent Medicine, LK Baden-Moedling, Baden, Austria.
  • Herta J; Member of the ERN EoiCare, Lyon, France.
  • Kluge T; Department of Paediatric and Adolescent Medicine, LK Baden-Moedling, Baden, Austria.
  • Werther T; Department of Paediatric and Adolescent Medicine, Division of Neonatology, Clinic Favoriten, Vienna, Austria.
  • Berger A; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
  • Koren J; Center for Health & Bioresources, AIT (Austrian Institute of Technology), Vienna, Austria.
  • Klebermass-Schrehof K; Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
  • Giordano V; Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
Physiol Meas ; 45(9)2024 Oct 01.
Article en En | MEDLINE | ID: mdl-39288793
ABSTRACT
Objective. This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data.Approach. We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario.Main results. Interrater agreement was substantial at a kappa of 0.73 (0.68-0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67-0.76) and a sensitivity and specificity of 0.90 (0.82-0.94) and 0.95 (0.93-0.97), respectively.Significance. The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Electroencefalografía / Recien Nacido Extremadamente Prematuro Límite: Humans / Newborn Idioma: En Revista: Physiol Meas Asunto de la revista: BIOFISICA / ENGENHARIA BIOMEDICA / FISIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Electroencefalografía / Recien Nacido Extremadamente Prematuro Límite: Humans / Newborn Idioma: En Revista: Physiol Meas Asunto de la revista: BIOFISICA / ENGENHARIA BIOMEDICA / FISIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Reino Unido