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Published on: June 27, 2013
Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures
Lorenzo Frassineti1,2, Antonio Lanatà1, Benedetta Olmi1
1Department of Information Engineering, Università degli Studi di Firenze, Via Santa Marta 3, 50139 Firenze, Italy.
Insights
Multiscale heart rate variability (HRV) entropy analysis can detect abnormal heart rate dynamics in newborns with seizures. This method shows promise as a tool for early seizure detection when electroencephalography is unavailable.
Area of Science:
- Biomedical Engineering
- Neonatal Medicine
- Computational Physiology
Background:
- Neonatal seizures are challenging to detect due to complex physiological dynamics.
- Early diagnosis and treatment are crucial for neurodevelopmental outcomes.
- Electroencephalography (EEG) is a common but not always accessible method for seizure detection.
Purpose of the Study:
- To investigate if multiscale heart rate variability (HRV) entropy indexes can detect abnormal heart rate dynamics in newborns with seizures.
- To assess the utility of entropy measures in differentiating between seizure and seizure-free newborns.
- To explore HRV as a potential alternative or complementary tool to EEG for neonatal seizure detection.
Main Methods:
- Analysis of a cohort of 52 newborns (33 with seizures) from a public dataset.
- Calculation and comparison of multiscale sample entropy and fuzzy entropy indexes.
- Statistical analysis, including Bonferroni correction and Mann-Whitney test, to compare groups.
Main Results:
- Multiscale sample and fuzzy entropy showed significant differences between newborns with and without seizures (p < 0.05).
- Interictal HRV activity also differed significantly between seizure and seizure-free patients (p < 0.05).
- HRV entropy measures demonstrated effectiveness in discriminating between the two groups.
Conclusions:
- Multiscale HRV entropy analysis is a promising method for detecting abnormal heart rate dynamics associated with neonatal seizures.
- This approach could serve as a valuable pre-screening tool for timely seizure detection in newborns.
- HRV analysis offers a non-invasive, potentially more accessible alternative or adjunct to EEG in neonatal intensive care settings.
Abstract:
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.
