{"title":"用于检测极早产新生儿脑电图爆发间期的自适应阈值算法。","authors":"Johannes Caspar Mader,Manfred Hartmann,Anastasia Dressler,Lisa Oberdorfer,Zsofia Rona,Sarah Glatter,Christine Czaba-Hnizdo,Johannes Herta,Tilmann Kluge,Tobias Werther,Angelika Berger,Johannes Koren,Katrin Klebermass-Schrehof,Vito Giordano","doi":"10.1088/1361-6579/ad7c05","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"30 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates.\",\"authors\":\"Johannes Caspar Mader,Manfred Hartmann,Anastasia Dressler,Lisa Oberdorfer,Zsofia Rona,Sarah Glatter,Christine Czaba-Hnizdo,Johannes Herta,Tilmann Kluge,Tobias Werther,Angelika Berger,Johannes Koren,Katrin Klebermass-Schrehof,Vito Giordano\",\"doi\":\"10.1088/1361-6579/ad7c05\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":20047,\"journal\":{\"name\":\"Physiological measurement\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physiological measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6579/ad7c05\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physiological measurement","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6579/ad7c05","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates.
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.
期刊介绍:
Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation.
Papers are published on topics including:
applied physiology in illness and health
electrical bioimpedance, optical and acoustic measurement techniques
advanced methods of time series and other data analysis
biomedical and clinical engineering
in-patient and ambulatory monitoring
point-of-care technologies
novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems.
measurements in molecular, cellular and organ physiology and electrophysiology
physiological modeling and simulation
novel biomedical sensors, instruments, devices and systems
measurement standards and guidelines.