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Background: Continuous heart rate (HR) and oxygenation (SpO 2 ) metrics can be useful for predicting adverse events in very low birth weight (VLBW) infants. To optimize the utility of these tools, inter-site variability must be taken into account., Methods: For VLBW infants at three neonatal intensive care units (NICUs), we analyzed the mean, standard deviation, skewness, kurtosis, and cross-correlation of electrocardiogram HR, pulse oximeter pulse rate, and SpO 2 . The number and durations of bradycardia and desaturation events were also measured. Twenty-two metrics were calculated hourly, and mean daily values were compared between sites., Results: We analyzed data from 1168 VLBW infants from birth through day 42 (35,238 infant-days). HR and SpO 2 metrics were similar at the three NICUs, with mean HR rising by ~10 beats/min over the first 2 weeks and mean SpO 2 remaining stable ~94% over time. The number of bradycardia events was higher at one site, and the duration of desaturations was longer at another site., Conclusions: Mean HR and SpO 2 were generally similar among VLBW infants at three NICUs from birth through 6 weeks of age, but bradycardia and desaturation events differed in the first 2 weeks after birth. This highlights the importance of developing predictive analytics tools at multiple sites., Impact: HR and SpO 2 analytics can be useful for predicting adverse events in VLBW infants in the NICU, but inter-site differences must be taken into account in developing predictive algorithms. Although mean HR and SpO 2 patterns were similar in VLBW infants at three NICUs, inter-site differences in the number of bradycardia events and duration of desaturation events were found. Inter-site differences in bradycardia and desaturation events among VLBW infants should be considered in the development of predictive algorithms., (© 2021. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Background: Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis., Goal: Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases., Methods: We tested algorithms on the NSR, AF and arrhythmia databases., Results: When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms., Conclusion: Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: In 2006 the apnea of prematurity (AOP) consensus group identified inaccurate counting of apnea episodes as a major barrier to progress in AOP research. We compare nursing records of AOP to events detected by a clinically validated computer algorithm that detects apnea from standard bedside monitors., Study Design: Waveform, vital sign, and alarm data were collected continuously from all very low-birth-weight infants admitted over a 25-month period, analyzed for central apnea, bradycardia, and desaturation (ABD) events, and compared with nursing documentation collected from charts. Our algorithm defined apnea as > 10 seconds if accompanied by bradycardia and desaturation., Results: Of the 3,019 nurse-recorded events, only 68% had any algorithm-detected ABD event. Of the 5,275 algorithm-detected prolonged apnea events > 30 seconds, only 26% had nurse-recorded documentation within 1 hour. Monitor alarms sounded in only 74% of events of algorithm-detected prolonged apnea events > 10 seconds. There were 8,190,418 monitor alarms of any description throughout the neonatal intensive care unit during the 747 days analyzed, or one alarm every 2 to 3 minutes per nurse., Conclusion: An automated computer algorithm for continuous ABD quantitation is a far more reliable tool than the medical record to address the important research questions identified by the 2006 AOP consensus group., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

Apnea of prematurity is an important and common clinical problem, and is often the rate-limiting process in NICU discharge. Accurate detection of episodes of clinically important neonatal apnea using existing chest impedance (CI) monitoring is a clinical imperative. The technique relies on changes in impedance as the lungs fill with air, a high impedance substance. A potential confounder, however, is blood coursing through the heart. Thus, the cardiac signal during apnea might be mistaken for breathing. We report here a new filter to remove the cardiac signal from the CI that employs a novel resampling technique optimally suited to remove the heart rate signal, allowing improved apnea detection. We also develop an apnea detection method that employs the CI after cardiac filtering. The method has been applied to a large database of physiological signals, and we prove that, compared to the presently used monitors, the new method gives substantial improvement in apnea detection.

Calculating entropy rate in physiologic signals has proven very useful in many settings. Common entropy estimates for this purpose are sample entropy (SampEn) and its less robust elder cousin, approximate entropy (ApEn). Both approaches count matches within a tolerance r for templates of length m consecutive observations. When physiologic data records are long and well-behaved, both approaches work very well for a wide range of m and r. However, more attention to the details of the estimation algorithm is needed for short records and signals with anomalies. In addition, interpretation of the magnitude of these estimates is highly dependent on how r is chosen and precludes comparison across studies with even slightly different methodologies. In this paper, we summarize recent novel approaches to improve the accuracy of entropy estimation. An important (but not necessarily new) alternative to current approaches is to develop estimates that convert probabilities to densities by normalizing by the matching region volume. This approach leads to a novel concept introduced here of reporting entropy rate in equivalent Gaussian white noise units. Another approach is to allow r to vary so that a pre-specified number of matches are found, called the minimum numerator count, to ensure confident probability estimation. The approaches are illustrated using a simple example of detecting abnormal cardiac rhythms in heart rate records.

Sample entropy and approximate entropy are measures that have been successfully utilized to study the deterministic dynamics of heart rate (HR). A complementary stochastic point of view and a heuristic argument using the Central Limit Theorem suggests that the Gaussianity of HR is a complementary measure of the physiological complexity of the underlying signal transduction processes. Renyi entropy (or q-entropy) is a widely used measure of Gaussianity in many applications. Particularly important members of this family are differential (or Shannon) entropy (q = 1) and quadratic entropy (q = 2). We introduce the concepts of differential and conditional Renyi entropy rate and, in conjunction with Burg's theorem, develop a measure of the Gaussianity of a linear random process. Robust algorithms for estimating these quantities are presented along with estimates of their standard errors.

While heart rate variability has been measured in many clinical settings and has offered insights into how HR is controlled, rarely has it offered unique information that has led to changes in patient management. We review our experience in developing continuous HR characteristics monitoring to aid in the early diagnosis of sepsis in premature infants in the neonatal intensive care unit. A predictive algorithm, developed at one center and validated at another, has led to diagnosis and treatment of this subacute and potentially catastrophic illness prior to appearance of symptoms of severe illness.

Continuous electronic fetal heart rate (FHR) monitoring during labor is motivated by the clinical experience that fetal distress causes loss of FHR variation and the occurrence of decelerations late during uterine contraction. This practice is of uncertain clinical benefit, perhaps because the interpretation is qualitative. We have developed new quantitative measures and analyzed cardiotocograph records from 148 consecutive patients, 44 of whom had at least one "nonreassuring" epoch. In multivariate regression models, measures of deceleration and variability were significantly associated with the obstetrician's diagnosis (receiver operating characteristic area 0.84, p < 0.05). This approach may be useful clinically.

The clinical diagnosis of neonatal sepsis is preceded by abnormal heart rate (HR) characteristics of transient decelerations and reduced variability, which intuitively appear to be more nonstationary than normal HR variability. Our goals were to investigate stationarity of HR, and to devise measures useful for early diagnosis of neonatal sepsis. In this context, we define non-stationarity to be present when the observed data differ from surrogate data generated by stationary Gaussian noise with arbitrary linear correlations. We devised statistical methods for determining stationarity of HR data based on the two-sample Kolmogorov-Smirnov (KS) test. We compared distributions of KS distances between small sample epochs from clinical data with those of isospectral surrogates and of surrogates generated using the amplitude-adjusted Fourier transform technique, reasoning that they should differ significantly for nonstationary data. We found significant evidence of non-stationarity for records longer than 1 min. We developed new HR measures based on the empirical cumulative distribution function (ECDF) that are highly significantly associated with sepsis, but are not correlated with HR measures such as moments or sample entropy. We conclude that neonatal HR data cannot be assumed to be stationary, and become even less stationary prior to sepsis.