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

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.

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

“…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].…”

“…For 22 patients, the experts did not find any seizures, and we considered them seizure-free patients. In this study, we analyzed only the patients with unanimous consensus [19,20]. Thus, the remaining 18 patients were excluded.…”

Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life

“…MSE has been used on other biological signals, for example heart rate variability with the aim of distinguishing patients with congestive heart failure from healthy subjects (Tsai et al 2020), or even to identify newborns at high risk of seizures (Frassineti et al 2021). In these examples, MSE was lower in pathologic conditions.…”

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

“…The efforts towards the establishment of a standard technique for the processing and analysis of FHR signals brought the development of different software solutions, methodological approaches, and indicators that could assist the clinical examination of CTG recordings, with particular regard to the FHR signals [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. As also happened in the analysis of adult and newborn heart rate signals [ 25 , 26 , 27 , 28 ], most of the newer computerized tools for FHR processing and analysis are based on Artificial Intelligence (AI) algorithms aimed at extracting novel features from the FHR signals, and achieve a more accurate classification of the traces according to the fetal health status [ 29 , 30 , 31 , 32 ]. Among the proposed tools, machine learning algorithms and, in particular, Artificial Neural Networks (ANN) showed promising results in terms of predictability and classification capabilities [ 31 , 32 , 33 , 34 , 35 , 36 ].…”