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Ischemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive electroencephalogram (EEG) biomarkers of corticothalamic communication under normal, sedation and hypoxic/ischemic conditions with ensuing recovery. We provide technical validation using EEGLAB. We also delineate the corresponding changes in the electrocardiogram (ECG)-derived heart rate variability (HRV) with the potential for future in-depth analyses of joint EEG-ECG dynamics. We review an open-source methodology to derive signatures of coupling between the ECoG and electrothalamogram (EThG) signals contained in the presented data set to better characterize the dynamics of thalamocortical communication during these clinically relevant states. The data set is presented in full band sampled at 2000 Hz, so the additional potential exists for insights from the full-band EEG and high-frequency oscillations under the bespoke experimental conditions. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries., Comment: See manuscript PDF for online visualization links and the data set shared on FigShare: 10.6084/m9.figshare.7834442

An electrocardiogram (ECG)-derived heart rate variability (HRV) index reliably tracks the inflammatory response induced by low-dose lipopolysaccharide (LPS) in near-term sheep fetuses. We evaluated the effect of vagus nerve stimulation (VNS) on vagus nerve electroneurogram (VENG) and the systemic inflammatory response induced by a high dose of LPS in neonatal piglets to mimic late-onset neonatal sepsis. We tested if our HRV inflammatory index tracks inflammation in piglets and its relationship to VENG. Following anesthesia, electrodes were attached to the left vagal nerve; ECG and blood pressure (BP) were recorded throughout the experiment. Following baseline, the piglets were administered LPS as 2mg/kg IV bolus. In the VNS treated piglet, the vagus nerve was stimulated for 10 minutes prior to and 10 min after the injection of LPS. In both groups, every 15 min post LPS, the arterial blood sample was drawn for blood gas, metabolites, and inflammatory cytokines. At the end of the experiment, the piglets were euthanized. BP and HRV measures were calculated. The piglets developed a potent inflammatory response to the LPS injection with TNF-alpha, IL-1beta, IL-6 and IL-8 peaking between 45 and 90 min post-injection. VNS diminished the LPS-induced systemic inflammatory response varying across the measured cytokines from two to ten-fold. The HRV index tracked accurately the temporal profile of cytokines and VENG changes. This novel model allows manipulating and tracking neonatal sepsis: The HRV inflammatory index 1) applies across species pre- and postnatally and 2) performs well at different degrees of sepsis (i.e., nanogram and milligram doses of LPS); 3) the present VNS paradigm effectively suppresses LPS-induced inflammation, even at high doses of LPS. The potential of early postnatal VNS to counteract sepsis and of HRV monitoring to early detect and track it deserve further study., Comment: updated manuscript with VENG analyses

We present a novel computerized fetal heart rate intrapartum algorithm for early and individualized prediction of fetal cardiovascular decompensation, a key event in the causal chain leading to brain injury. This real-time machine learning algorithm performs well on noisy fetal heart rate data and requires ~2 hours to train on the individual fetal heart rate tracings in the first stage of labor; once trained, the algorithm predicts the event of fetal cardiovascular decompensation with 92% sensitivity. We show that the algorithm's performance suffers reducing sensitivity to 67% when the fetal heart rate is acquired at the sampling rate of 4 Hz used in ultrasound cardiotocographic monitors compared to the electrocardiogram(ECG)-derived signals as can be acquired from maternal abdominal ECG.

Fetal heart rate variability (fHRV) is an important indicator of health and disease, yet its physiological origins, neural contributions in particular, are not well understood. We aimed to develop novel experimental and data analytical approaches to identify fHRV measures reflecting the vagus nerve contributions to fHRV. In near-term ovine fetuses, a comprehensive set of 46 fHRV measures was computed from fetal pre-cordial electrocardiogram recorded during surgery and 72 hours later without (n=24) and with intra-surgical bilateral cervical vagotomy (n=15). The fetal heart rate did not change due to vagotomy. We identify fHRV measures specific to the vagal modulation of fHRV: Multiscale time irreversibility asymmetry index (AsymI), Detrended fluctuation analysis (DFA) alpha1, Kullback-Leibler permutation entropy (KLPE) and Scale dependent Lyapunov exponent slope (SDLE alpha). We provide a systematic delineation of vagal contributions to fHRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the vagus code. Our findings also have clinical significance for in utero monitoring of fetal health during surgery.

Although Zika virus (ZIKV) seems to be prominently neurotropic, there are some reports of involvement of other organs, particularly the heart. Of special concern are those children exposed prenatally to ZIKV and born with no microcephaly or other congenital anomaly. Electrocardiogram (ECG) - derived heart rate variability (HRV) metrics represent an attractive, low cost, widely deployable tool for early identification of such children. We hypothesized that HRV in such children would yield a biomarker of fetal ZIKV exposure. We investigated the HRV properties of 21 infants aged 4 to 25 months from Brazil. The infants were divided in two groups, the ZIKV-exposed (n=13) and controls (n=8). Single channel ECG was recorded in each child at ~15 months of age and HRV was analyzed in 5 min segments to provide a comprehensive characterization of the degree of variability and complexity of the heart rate. Using a cubic Support Vector Machine (SVM) classifier we identified babies as Zika cases or controls with negative predictive value of 92% and positive predictive value of 86%. Our results show that HRV metrics can help differentiate between ZIKV-affected, yet asymptomatic, and non-ZIKV exposed babies. We identified the Grid Count as the best HRV measure in this study allowing such differentiation, regardless the presence of microcephaly. We show that it is feasible to measure HRV in infants and toddlers using a small non-invasive portable ECG device and that such approach may uncover memory of in utero exposure to ZIKV. This approach may be useful for future studies and low-cost screening tools involving this challenging to examine population.

The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fetal ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modeled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sensitivities and positive predictive values comparable to other published algorithms using multiple leads and many more features.