We study the heartbeat activity of healthy individuals at rest and during exercise. We focus on correlation properties of the intervals formed by successive peaks in the pulse wave and find significant scaling differences between rest and exercise. For exercise the interval series is anticorrelated at short time scales and correlated at intermediate time scales, while for rest we observe the opposite crossover pattern -from strong correlations in the short-time regime to weaker correlations at larger scales. We suggest a physiologically motivated stochastic scenario to explain the scaling differences between rest and exercise and the observed crossover patterns.

Patients with Parkinson's disease exhibit tremor, involuntary movement of the limbs. The frequency spectrum of tremor typically has broad peaks at "harmonic" frequencies, much like that seen in other physical processes. In general, this type of harmonic structure in the frequency domain may be due to two possible mechanisms: a nonlinear oscillation or a superposition of (multiple) independent modes of oscillation. A broad peak spectrum generally indicates that a signal is semiperiodic with a fluctuating period. These fluctuations may posses intrinsic order that can be quantified using scaling analysis. We propose a method to extract the correlation (scaling) properties in the period dynamics of multimodal oscillations, in order to distinguish between a nonlinear oscillation and a superposition of individual modes of oscillation. The method is based on our finding that the information content of the temporal correlations in a fluctuating period of a single oscillator is contained in a finite frequency band in the power spectrum, allowing for decomposition of modes by bandpass filtering. Our simulations for a nonlinear oscillation show that harmonic modes possess the same scaling properties. In contrast, when the method is applied to tremor records from patients with Parkinson's disease, the first two modes of oscillations yield different scaling patterns, suggesting that these modes may not be simple harmonics, as might be initially assumed.

IntroductionContinuous physiological monitoring devices are often not available for monitoring high-risk neonates in low-resource settings. Easy-to-use, non-invasive, multiparameter, continuous physiological monitoring devices could be instrumental in providing appropriate care and improving outcomes for high-risk neonates in these low-resource settings.Methods and analysisThe purpose of this prospective, observational, facility-based evaluation is to provide evidence to establish whether two existing non-invasive, multiparameter, continuous physiological monitoring devices developed by device developers, EarlySense and Sibel, can accurately and reliably measure vital signs in neonates (when compared with verified reference devices). We will also assess the feasibility, usability and acceptability of these devices for use in neonates in low-resource settings in Africa. Up to 500 neonates are enrolled in two phases: (1) a verification and accuracy evaluation phase at Aga Khan University—Nairobi and (2) a clinical feasibility evaluation phase at Pumwani Maternity Hospital in Nairobi, Kenya. Both quantitative and qualitative data are collected and analysed. Agreement between the investigational and reference devices is determined using a priori-defined accuracy thresholds.Ethics and disseminationThis trial was approved by the Aga Khan University Nairobi Research Ethics Committee and the Western Institutional Review Board. We plan to disseminate research results in peer-reviewed journals and international conferences.Trial registration numberNCT03920761.

In heart rate monitoring, the Electrocardiogram

Sleep apnea is a highly prevalent yet under-diagnosed condition. This study tested a novel algorithm for sleep apnea screening with a contact-free system based on a piezo-electric sensor (PE system -EarlySense Ltd)

We studied heart rate variability in rats by power scaling spectral analysis (PSSA), autoregressive modeling (AR), and detrended fluctuation analysis (DFA), assessed stability by coefficient of variation between consecutive 6-h epochs, and then compared cross-correlation among techniques. These same parameters were checked from baseline conditions through acute and chronic disease states (streptozotocin-induced diabetes) followed by therapeutic intervention (insulin). Cross-correlation between methods over the entire time period was r = 0.94 (DFA and PSSA), r = 0.81 (DFA and AR), and r = 0.77 (AR and PSSA). Under baseline conditions the scaling parameter measured by DFA and PSSA and the high-frequency (HF) component measured by AR fluctuated around an average value, but these fluctuations were different for the three methods. After diabetes induction, a strong correlation was found between the HF power and the short-term scaling parameter. Despite their differences in methodology, DFA and PSSA assess changes in parasympathetic tone as detected by autoregressive modeling.

BackgroundGlobally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings.MethodsIn four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation.ResultsBetween 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of −0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori–defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference.ConclusionOur research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates.Trial registration numberNCT03920761.