Multiscale entropy analysis of heart rate variability in neonatal patients with and without seizures

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

Network dynamics in the healthy and epileptic developing brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarised along different dimensions using network-level quantitative measures, e.g. channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different time scales and can be tracked dynamically. Here we describe the dynamics of network-state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n=8, age: 1-8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable based on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in future inform the clinical use of quantitative EEG for diagnosis

Informatics for EEG biomarker discovery in clinical neuroscience

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed

First evidence that intrinsic fetal heart rate variability exists and is affected by hypoxic pregnancy.

KEY POINTS: We introduce a technique to test whether intrinsic fetal heart rate variability (iFHRV) exists and we show the utility of the technique by testing the hypothesis that iFHRV is affected by chronic fetal hypoxia, one of the most common adverse outcomes of human pregnancy complicated by fetal growth restriction. Using an established late gestation ovine model of fetal development under chronic hypoxic conditions, we identify iFHRV in isolated fetal hearts and show that it is markedly affected by hypoxic pregnancy. Therefore, the isolated fetal heart has intrinsic variability and carries a memory of adverse intrauterine conditions experienced during the last third of pregnancy. ABSTRACT: Fetal heart rate variability (FHRV) emerges from influences of the autonomic nervous system, fetal body and breathing movements, and from baroreflex and circadian processes. We tested whether intrinsic heart rate variability (iHRV), devoid of any external influences, exists in the fetal period and whether it is affected by chronic fetal hypoxia. Chronically catheterized ewes carrying male singleton fetuses were exposed to normoxia (n = 6) or hypoxia (10% inspired O2 , n = 9) for the last third of gestation (105-138 days of gestation (dG); term ∼145 dG) in isobaric chambers. At 138 dG, isolated hearts were studied using a Langendorff preparation. We calculated basal intrinsic FHRV (iFHRV) indices reflecting iFHRV's variability, predictability, temporal symmetry, fractality and chaotic behaviour, from the systolic peaks within 15 min segments in each heart. Significance was assumed at P < 0.05. Hearts of fetuses isolated from hypoxic pregnancy showed approximately 4-fold increases in the Grid transformation as well as the AND similarity index (sgridAND) and a 4-fold reduction in the scale-dependent Lyapunov exponent slope. We also detected a 2-fold reduction in the Recurrence quantification analysis, percentage of laminarity (pL) and recurrences, maximum and average diagonal line (dlmax, dlmean) and the Multiscale time irreversibility asymmetry index. The iHRV measures dlmax, dlmean, pL and sgridAND correlated with left ventricular end-diastolic pressure across both groups (average R2  = 0.38 ± 0.03). This is the first evidence that iHRV originates in fetal life and that chronic fetal hypoxia significantly alters it. Isolated fetal hearts from hypoxic pregnancy exhibit a time scale-dependent higher complexity in iFHRV.British Heart Foundatio

fNIRS complexity analysis for the assessment of motor imagery and mental arithmetic tasks

Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis

Nonlinear complexity analysis of brain fMRI signals in schizophrenia

Peer reviewedPublisher PD

Electroencephalogram data platform for application of reduction methods

Long-term electroencephalogram (EEG) monitoring (≥24-h) is a resourceful tool for properly diagnosis sparse life-threatening events like non-convulsive seizures and status epilepticus in Intensive Care Unit (ICU) inpatients. Such EEG data requires objective methods for data reduction, transmission and analysis. This work aims to assess specificity and sensibility of HaEEG and aEEG methods in combination with conventional multichannel EEG when achieving seizure detection. A database architecture was designed to handle the interoperability, processing, and analysis of EEG data. Using data from CHB-MIT public EEG database, the reduced signal was obtained by EEG envelope segmentation, with 10 and 90 percentiles obtained for each segment. The use of asymmetrical filtering (2-15 Hz) and overall clinical band (1-70 Hz) was compared. The upper and lower margins of compressed segments were used to classify ictal and non-ictal epochs. Such classification was compared with the corresponding specialist seizure annotation for each patient. The difference between medians of instantaneous frequencies of ictal and non-ictal periods were assessed using Wilcoxon Rank Sum Test, which was significant for signals filtered from 2 to 15 Hz (p = 0.0055) but not for signals filtered from 1 to 70 Hz (p = 0.1816).O eletroencefalograma (EEG) de longa duração (≥24-h) em monitoramento contínuo é diferencial no diagnóstico e classificação de eventos epileptiformes, como crises não convulsivas e status epilepticus, em pacientes de Unidades de Tratamento Intensivo (UTI). Este exame requer métodos objetivos de análise, redução e transmissão de dados. O objetivo desse trabalho é avaliar a especificidade e a sensibilidade dos métodos HaEEG e aEEG em combinação com EEG multicanal convencional na detecção de eventos epileptiformes. Uma arquitetura de integração de dados foi projetada para gerir o armazenamento, processamento e análise de dados de EEG. Foram utilizados dados do banco de dados de EEG público do CHB-MIT. O sinal reduzido foi obtido pela segmentação do envelope do EEG, com percentis 10 e 90 obtidos para cada segmento. A aplicação do filtro assimétrico (2-15 Hz) e em bandas clínicas (1-70 Hz) foi comparada. Os limiares superiores e inferiores dos segmentos do aEEG e HaEEG foram usados para classificar épocas ictais e não ictais. A classificação foi comparada com as anotações feitas por um especialista para cada paciente. As medianas das frequências instantâneas para períodos ictais e não ictais foram analisadas com Wilcoxon Rank Sum Test com significância para filtragem assimétrica (p = 0,0055), mas não nas bandas clínicas (p = 0,1816)

Brain Dynamics Based Automated Epileptic Seizure Detection

abstract: Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. However, this process still requires that seizures are visually detected and marked by experienced and trained electroencephalographers. The motivation for the development of an automated seizure detection algorithm in this research was to assist physicians in such a laborious, time consuming and expensive task. Seizures in the EEG vary in duration (seconds to minutes), morphology and severity (clinical to subclinical, occurrence rate) within the same patient and across patients. The task of seizure detection is also made difficult due to the presence of movement and other recording artifacts. An early approach towards the development of automated seizure detection algorithms utilizing both EEG changes and clinical manifestations resulted to a sensitivity of 70-80% and 1 false detection per hour. Approaches based on artificial neural networks have improved the detection performance at the cost of algorithm's training. Measures of nonlinear dynamics, such as Lyapunov exponents, have been applied successfully to seizure prediction. Within the framework of this MS research, a seizure detection algorithm based on measures of linear and nonlinear dynamics, i.e., the adaptive short-term maximum Lyapunov exponent (ASTLmax) and the adaptive Teager energy (ATE) was developed and tested. The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) and a total of 56 seizures, producing a mean sensitivity of 93% and mean specificity of 0.048 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free and patient-independent. It is expected that this algorithm will assist physicians in reducing the time spent on detecting seizures, lead to faster and more accurate diagnosis, better evaluation of treatment, and possibly to better treatments if it is incorporated on-line and real-time with advanced neuromodulation therapies for epilepsy.Dissertation/ThesisM.S. Electrical Engineering 201