Identifying an optimal epoch length for spectral analysis of heart rate of critically-ill infants

Infant emotion regulation in the context of stress: Effects of heart rate variability and temperament

Stressful events are inherently emotional. As a result, the ability to regulate emotions is critical in responding effectively to stressors. Differential abilities in the management of stress appear very early in life, compelling a need to better understand factors that may shape the capacity for emotion regulation (ER). Variations in both biologic and behavioural characteristics are thought to influence individual differences in ER development. We sought to determine the differential contributions of temperament and heart rate variability (HRV; an indicator of autonomic nervous system function) to infant resting state emotionality and emotional reactivity in response to a stressor at 6 months of age. Participants included 108 mother-infant dyads. Mothers completed a measure of infant temperament at 6 months postnatal. Mother and infant also participated in a standardized stressor (the Repeated Still Face Paradigm) at that time. Electrocardiographic data were acquired from the infant during a baseline resting state and throughout the stressor. Fast Fourier Transformation was used to analyse the high frequency (HF) domain of HRV, a measure of parasympathetic nervous system activity. Infant ER was measured via standardized coding of emotional distress behaviours from video-records at baseline and throughout the stressor. Severity of mothers' depressive symptoms was included as a covariate in analyses. Results of linear regression indicate that neither temperament nor HRV were associated significantly with an infant's emotional resting state, although a small effect size was found for the relationship between infant negative affectivity and greater emotional distress (β = 0.23, p = 0.08) prior to the stressor. Higher HF-HRV (suggesting parasympathetic dominance) was related to greater emotional distress in response to the stressor (β = 0.34, p = 0.009). This greater emotional reactivity may reflect a more robust capacity to mount an emotional response to the stressor when infants encounter it from a bedrock of parasympathetic activation. Findings may inform eventual markers for assessment of ER in infancy and areas for intervention to enhance infant management of emotions, especially during stressful events.

Prediction of outcome of hypoxic-ischemic encephalopathy in newborns undergoing therapeutic hypothermia using heart rate variability

OBJECTIVE

To assess the use of continuous heart rate variability (HRV) as a predictor of brain injury severity in newborns with moderate to severe HIE that undergo therapeutic hypothermia.

STUDY DESIGN

Two cohorts of newborns (n1 = 55, n2 = 41) with moderate to severe hypoxic-ischemic encephalopathy previously treated with therapeutic hypothermia. HRV was characterized by root mean square in the short time scales (RMSS) during therapeutic hypothermia and through completion of rewarming. A logistic regression and Naïve Bayes models were developed to predict the MRI outcome of the infants using RMSS. The encephalopathy grade and gender were used as control variables.

RESULTS

For both cohorts, the predicted outcomes were compared with the observed outcomes. Our algorithms were able to predict the outcomes with an area under the receiver operating characteristic curve of about 0.8.

CONCLUSIONS

HRV assessed by RMSS can predict severity of brain injury in newborns with HIE.

Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence

Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

BACKGROUND

Neonatal heart rate variability (HRV) is widely used as a research tool. However, HRV calculation methods are highly variable making it difficult for comparisons between studies.

OBJECTIVES

To describe the different types of investigations where neonatal HRV was used, study characteristics, and types of analyses performed.

ELIGIBILITY CRITERIA

Human neonates ≤1 month of corrected age.

SOURCES OF EVIDENCE

A protocol and search strategy of the literature was developed in collaboration with the McGill University Health Center's librarians and articles were obtained from searches in the Biosis, Cochrane, Embase, Medline and Web of Science databases published between 1 January 2000 and 1 July 2020.

CHARTING METHODS

A single reviewer screened for eligibility and data were extracted from the included articles. Information collected included the study characteristics and population, type of HRV analysis used (time domain, frequency domain, non-linear, heart rate characteristics (HRC) parameters) and clinical applications (physiological and pathological conditions, responses to various stimuli and outcome prediction).

RESULTS

Of the 286 articles included, 171 (60%) were small single centre studies (sample size <50) performed on term infants (n=136). There were 138 different types of investigations reported: physiological investigations (n=162), responses to various stimuli (n=136), pathological conditions (n=109) and outcome predictor (n=30). Frequency domain analyses were used in 210 articles (73%), followed by time domain (n=139), non-linear methods (n=74) or HRC analyses (n=25). Additionally, over 60 different measures of HRV were reported; in the frequency domain analyses alone there were 29 different ranges used for the low frequency band and 46 for the high frequency band.

CONCLUSIONS

Neonatal HRV has been used in diverse types of investigations with significant lack of consistency in analysis methods applied. Specific guidelines for HRV analyses in neonates are needed to allow for comparisons between studies.

Heart rate variability in neonatal seizures: Investigation and implications for management

Many factors acting during the neonatal period can affect neurological development of the infant. Neonatal seizures (NS) that frequently occur in the immature brain may influence autonomic maturation and lead to detectable cardiovascular signs. These autonomic manifestations can also have significant diagnostic and prognostic value. The analysis of Heart Rate Variability (HRV) represents the most used and feasible method to evaluate cardiac autonomic regulation. This narrative review summarizes studies investigating HRV dynamics in newborns with seizures, with the aim of highlighting the potential utility of HRV measures for seizure detection and management. While HRV analysis in critically ill newborns is influenced by many potential confounders, we suggest that it can enhance the ability to better diagnose seizures in the clinical setting. We present potential applications of the analysis of HRV, which could have a useful future role, beyond the research setting.

Heart rate variability as possible marker of brain damage in neonates with hypoxic ischemic encephalopathy: a systematic review

Heart rate variability (HRV) is currently considered the most valuable non-invasive test to investigate the autonomic nervous system function, based on the fact that fast fluctuations might specifically reflect changes of sympathetic and vagal activity. An association between abnormal values of HRV and brain impairment has been reported in the perinatal period, although data are still fragmentary. Considering such association, HRV has been suggested as a possible marker of brain damage also in case of hypoxic-ischemic encephalopathy following perinatal asphyxia. The aim of the present manuscript was to review systematically the current knowledge about the use of HRV as marker of cerebral injury in neonates suffering from hypoxic-ischemic encephalopathy. Findings reported in this paper were based on qualitative analysis of the reviewed data. Conclusion: A growing body of research supports the use of HRV as non-invasive, bedside tool for the monitoring of hypoxic-ischemic encephalopathy. The currently available data about the role of HRV as prognostic tool in case of hypoxic ischemic encephalopathy are promising but require further validation by future studies. What is Known: • Heart rate variability (HRV) is a non-invasive monitoring technique to assess the autonomic nervous system activity. • A correlation between abnormal HRV and cerebral injury has been reported in the perinatal period, and HRV has been suggested as possible marker of brain damage in case of hypoxic-ischemic encephalopathy. What is New: • HRV might provide precocious information about the entity of brain injury in asphyxiated neonates and be of help to design early, specific, and personalized treatments according to severity. • Further investigations are required to confirm these preliminary data.

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.