Multiparametric EEG analysis of brain network dynamics during neonatal seizures

Frassineti, Lorenzo; Parente, Angela; Manfredi, Claudia

doi:10.1016/j.jneumeth.2020.109003

Cited by 17 publications

(8 citation statements)

References 55 publications

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“…While the applications of these methodologies to the newborn are very scarce, there are many studies and results regarding the adult and the child. The reasons concern not only the peculiar physiological characteristics of the newborn, as already pointed out above [60], but also the difficulty of applying and using adequate sensors in NICUs. Even though a survey concerning adult and child monitoring would be interesting, it is out of the scope of this work.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Olmi¹,

Frassineti

Lanatà

et al. 2021

IEEE Access

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In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEGbased NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Olmi¹,

Frassineti

Lanatà

et al. 2021

IEEE Access

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“…Future studies could focus on intrinsic differences of BHI for patients with seizure events. They could focus on differences between interictal and ictal periods [20] or between pre-ictal and post-ictal periods [8]. Others could investigate BHIs among different aetiologies [7,9]; or how BHI varies after pharmacological treatments [6].…”

Section: Resultsmentioning

confidence: 99%

“…However, there is still a lack of information about the interactions between CNS and ANS during or close to several seizure events [6], such as neonatal seizures, the most common neurological emergency in the first days of life of the newborn [7]. According to a recent ILAE position paper [7], neonatal seizures deserve a particular classification among seizure events, both for the intrinsic electroclinical characteristics of the newborn (when compared to the adult [8]), and their aetiologies [9]. Seizure detection is tricky and timeconsuming, and delayed treatment can negatively affect neurodevelopment [7,10].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Brain-Heart Interactions in newborns with and without seizures using the Convergent Cross Mapping approach

Frassineti

Manfredi

Ermini

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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“…Multiscale entropy analysis could increase the information obtained with single scale analysis about the underlying physiological process [ 20 ]. Thus, applying these findings to newborns could be usefully exploited, considering the differences between ictal events in adults and infants, both for etiology and the electro-clinical characteristics [ 1 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

et al. 2021

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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 and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns.

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Multiparametric EEG analysis of brain network dynamics during neonatal seizures

Cited by 17 publications

References 55 publications

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units Through EEG, ECG and Video Recordings: A Survey

Analysis of Brain-Heart Interactions in newborns with and without seizures using the Convergent Cross Mapping approach

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

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