OBJECTIVE: The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. METHODS: We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation. RESULTS: The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour. CONCLUSIONS: Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms. SIGNIFICANCE: The proposed algorithm significantly improves neonatal seizure detection and monitoring.
OBJECTIVE: The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. METHODS: We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation. RESULTS: The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour. CONCLUSIONS: Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms. SIGNIFICANCE: The proposed algorithm significantly improves neonatal seizure detection and monitoring.
Authors: Andriy Temko; Gordon Lightbody; Eoin M Thomas; Geraldine B Boylan; William Marnane Journal: IEEE Trans Biomed Eng Date: 2011-12-07 Impact factor: 4.538
Authors: Timothy J Mitchell; Jeffrey J Neil; John M Zempel; Liu Lin Thio; Terrie E Inder; G Larry Bretthorst Journal: Clin Neurophysiol Date: 2012-09-24 Impact factor: 3.708
Authors: Ivana Despotovic; Perumpillichira J Cherian; Maarten De Vos; Hans Hallez; Wouter Deburchgraeve; Paul Govaert; Maarten Lequin; Gerhard H Visser; Renate M Swarte; Ewout Vansteenkiste; Sabine Van Huffel; Wilfried Philips Journal: Hum Brain Mapp Date: 2012-04-21 Impact factor: 5.038
Authors: J G Bogaarts; E D Gommer; D M W Hilkman; V H J M van Kranen-Mastenbroek; J P H Reulen Journal: Med Biol Eng Comput Date: 2016-03-31 Impact factor: 2.602
Authors: Andriy Temko; Achintya Kr Sarkar; Geraldine B Boylan; Sean Mathieson; William P Marnane; Gordon Lightbody Journal: IEEE J Transl Eng Health Med Date: 2017-09-11 Impact factor: 3.316