Abstract

Automated analysis of premature electroencephalogram (EEG) for diagnosis is a crucial step to reduce the workload of neurologists. The grade of gives important information about the maturation [1]. For normal maturation, the discontinuous pattern gradually evolves into a more continuous pattern. This means, interburst intervals (IBI), periods of low activity, become shorter. We have defined the suppression curve (SC), which is a measure of discontinuity [2] (Figure ​(Figure1A).1A). All data for this study were recorded at the Neonatal Intensive Care Unit, University Hospital Gasthuisberg, Leuven, Belgium. The dataset consisted of 170 EEG recordings (8 channels, 250 Hz) of 93 preterm infants with a postmenstrual age (PMA) of 24-40 weeks. Some maturational features are extracted from the discontinuous periods, like the IBI length and the synchrony index. However, the SC on itself gives also relevant information about the maturation. Taking the mean of every SC, we can find a correlation with the age till 34 weeks PMA (Figure ​(Figure1B).1B). Few outliers (abnormal EEG) are excluded. After that age, the patient is called late preterm or even term, and the EEG pattern is in normal condition mostly continuous (low values of the SC). Figure 1 A Suppression curve example, containing 2 periods of 20-30 minutes of discontinuous pattern, B Evolution of the mean of the suppression curve in function of the age, · represents a patient with normal EEG, * patient with abnormal EEG In conclusion, this research adds another valuable feature for the automated analysis of premature background EEG, which would improve the overall assessment in the NICU for EEG diagnosis

Highlights

  • Automated analysis of premature electroencephalogram (EEG) for diagnosis is a crucial step to reduce the workload of neurologists

  • * Correspondence: ninah.koolen@esat.kuleuven.be 1Division STADIUS, Department of Electrical Engineering (ESAT), University of Leuven, Leuven, Belgium Full list of author information is available at the end of the article which is a “measure of discontinuity” [2] (Figure 1A)

  • All data for this study were recorded at the Neonatal Intensive Care Unit, University Hospital Gasthuisberg, Leuven, Belgium

Read more

Summary

Introduction

Automated analysis of premature electroencephalogram (EEG) for diagnosis is a crucial step to reduce the workload of neurologists. The grade of discontinuity gives important information about the maturation [1]. The discontinuous pattern gradually evolves into a more continuous pattern.

Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.