Benedetta Olmi

ORCID: 0000-0002-6291-1120
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About
Contact & Profiles
Research Areas
  • EEG and Brain-Computer Interfaces
  • Neonatal and fetal brain pathology
  • Heart Rate Variability and Autonomic Control
  • Non-Invasive Vital Sign Monitoring
  • Functional Brain Connectivity Studies
  • ECG Monitoring and Analysis
  • Epilepsy research and treatment
  • Infant Health and Development

University of Florence
2021-2022

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) gold standard monitoring seizures, but it requires specialized equipment expert staff available 24/24h. purpose this study to present an overview main Seizure Detection (NSD) systems developed during last ten years that implement Artificial Intelligence techniques detect report...

10.1109/access.2021.3118227 article EN cc-by IEEE Access 2021-01-01

The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis the mitigation possible adverse effects on newborn’s neurodevelopment. In literature, several electroencephalographic (EEG) studies have been proposed parametric characterization or by artificial intelligence techniques. At same time, other sources than EEG, such as electrocardiography,...

10.3390/bioengineering8090122 article EN cc-by Bioengineering 2021-09-09

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is utmost importance for a timely clinical intervention. Over years, several seizure systems were proposed to detect automatically and speed up diagnosis, most based on EEG signal analysis. Recently, research has focused other possible markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system investigate usefulness heart rate variability (HRV) analysis in NICUs. HRV performed...

10.3390/bioengineering9040165 article EN cc-by Bioengineering 2022-04-07

Seizures represent one of the most challenging issues neonatal period's neurological emergency. Due to heterogeneity etiologies and clinical characteristics, seizures recognition is tricky time-consuming. Currently, gold standard for seizure diagnosis Electroencephalography (EEG), whose correct interpretation requires a highly specialized team. Thus, speed up facilitate detection ictal events, several EEG-based Neonatal Seizure Detectors (NSDs) have been proposed in literature. Research...

10.1109/embc46164.2021.9630841 article EN 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021-11-01

An efficient face detector could be very helpful to point out possible neurological dysfunctions such as seizure events in Neonatal Intensive Care Units. However, its development is still challenging because large public datasets of newborns' faces are missing. Over the years several studies introduced semi-automatic approaches. This study proposes a fully automated for newborns Units, based on Aggregate Channel Feature algorithm. The developed method tested dataset video recordings from 42...

10.1109/embc48229.2022.9871399 article EN 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022-07-11

In the last years, characterization of brain-heart interactions (BHIs) in epilepsy has gained great interest. For some specific seizures there is still a lack information about mechanisms occurring during or close to ictal events between central nervous system (CNS) and autonomic (ANS). This case for neonatal seizures, one most common neurological emergencies first days life. paper evaluates possible differences BHIs newborns with seizure-free ones. We applied convergent cross mapping...

10.1109/embc48229.2022.9871141 article EN 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022-07-11
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