Application of a Limit-Cycle Oscillator Model for Prediction of Circadian Phase in Rotating Night Shift Workers.

Practical alternatives to gold-standard measures of circadian timing in shift workers are needed. We assessed the feasibility of applying a limit-cycle oscillator model of the human circadian pacemaker to estimate circadian phase in 25 nursing and medical staff in a field setting during a transition from day/evening shifts (diurnal schedule) to 3-5 consecutive night shifts (night schedule). Ambulatory measurements of light and activity recorded with wrist actigraphs were used as inputs into the model. Model estimations were compared to urinary 6-sulphatoxymelatonin (aMT6s) acrophase measured on the diurnal schedule and last consecutive night shift. The model predicted aMT6s acrophase with an absolute mean error of 0.69 h on the diurnal schedule (SD = 0.94 h, 80% within ±1 hour), and 0.95 h on the night schedule (SD = 1.24 h, 68% within ±1 hour). The aMT6s phase shift from diurnal to night schedule was predicted to within ±1 hour in 56% of individuals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-individual variability in circadian phase shift during night shifts was limited. This study provides the basis for further adaptation and validation of models for predicting circadian phase in rotating shift workers.

Photoplethysmography beat detection and pulse morphology quality assessment for signal reliability estimation.

Photoplethysmography (PPG) is one of the key technologies for unobtrusive physiological monitoring, with ongoing attempts to use it in several medical fields, ranging from night to night sleep analysis to continuous cardiac arrhythmia monitoring. However, the PPG signals are susceptible to be corrupted by noise and artifacts, caused, e.g., by limb or sensor movement. These artifacts affect the morphology of PPG waves and prevent the accurate detection and localization of beats and subsequent cardiovascular feature extraction. In this paper a new algorithm for beat detection and pulse quality assessment is described. The algorithm segments the PPG signal in pulses, localizes each beat and grades each segment with a quality index. The obtained index results from a comparison between each pulse and a template derived from the surrounding pulses, by mean of dynamic time warping barycenter averaging. The quality index is used to discard corrupted pulse beats. The algorithm is evaluated by comparing the detected beats with annotated PPG signals and the results are published over the same data. The described method achieves an improved sensitivity and a higher predictive value.

Ambulatory estimation of human circadian phase using models of varying complexity based on non-invasive signal modalities.

In this work, we introduce a number of models for human circadian phase estimation in ambulatory conditions using various sensor modalities. Machine learning techniques have been applied to ambulatory recordings of wrist actigraphy, light exposure, electrocardiograms (ECG), and distal and proximal skin temperature to develop ARMAX models capturing the main signal dependencies on circadian phase and evaluating them versus melatonin onset times. The most accurate models extracted heart rate variability features from an ECG coupled with wrist activity information to produce phase estimations with prediction errors of ~30 minutes. Replacing the ECG features with skin temperature from the upper leg led to a slight degradation, while less accurate results, in the order of 1 hour, were obtained from wrist activity and light measurements. The trade-off between highest precision and least obtrusive configuration is discussed for applications to sleep and mood disorders caused by a misalignment of the internal phase with the external solar and social times.

Human circadian phase estimation from signals collected in ambulatory conditions using an autoregressive model.

Phase estimation of the human circadian rhythm is a topic that has been explored using various modeling approaches. The current models range from physiological to mathematical, all attempting to estimate the circadian phase from different physiological or behavioral signals. Here, we have focused on estimation of the circadian phase from unobtrusively collected signals in ambulatory conditions using a statistically trained autoregressive moving average with exogenous inputs (ARMAX) model. Special attention has been given to the evaluation of heart rate interbeat intervals (RR intervals) as a potential circadian phase predictor. Prediction models were trained using all possible combinations of RR intervals, activity levels, and light exposures, each collected over a period of 24 hours. The signals were measured without any behavioral constraints, aside from the collection of saliva in the evening to determine melatonin concentration, which was measured in dim-light conditions. The model was trained and evaluated using 2 completely independent datasets, with 11 and 19 participants, respectively. The output was compared to the gold standard of circadian phase: dim-light melatonin onset (DLMO). The most accurate model that we found made use of RR intervals and light and was able to yield phase estimates with a prediction error of 2 ± 39 minutes (mean ± SD) from the DLMO reference value.

Heart rate estimation on a beat-to-beat basis via ballistocardiography - a hybrid approach.

We present an algorithm for obtaining the heart rate from the signal of a single, contact-less sensor recording the mechanical activity of the heart. This vital parameter is required on a beat-to-beat basis for applications in sleep analysis and heart failure disease management. Our approach bundles information from various sources for first robust estimates. These estimates are further refined in a second step. An unambiguous comparison with the ECG RR-intervals taken as reference is possible for 98.5% of the heart beats. In these cases, a mean absolute error of 17 ms for the inter-beat interval lengths has been achieved, over a test corpus of 20 whole nights.

Estimation of vital signs in bed from a single unobtrusive mechanical sensor: algorithms and real-life evaluation.

A single contact-less mechanical sensor is exploited for estimating three vital signs during sleep, namely, the heart rate, the breathing rate and an activity index related to the body movements. Robust estimations are achieved over epochs of 30 seconds. The data processing is performed with standard DSP techniques leading to an integrated solution for dealing with body motion artifacts. The algorithms are described and evaluated over a one-hundred night corpus collected from real-life recordings of healthy subjects and sleep-laboratory patients. Results show that the average error of the heart rate is of 1.25 beat per minute compared to the reference values from an ECG and the coverage of the mechanically derived estimates is 83%.

Relationships between blood pressure and systolic time-intervals: a lumped-model simulation study.

A lumped model of the arterial circulation is applied to the study of the dependencies between blood pressure and systolic time-intervals (PEP, LVET). The left ventricle is handled as a pressure source directly coupled with the varying vascular conditions. Four factors are individually considered: peripheral resistance, LV contractility, end diastolic volume and heart rate. The computed dependence curves of PEP and LVET on systolic and diastolic pressures are in accordance with physiological knowledge. The relations of PEP and LVET with other hemodynamic variables are being enlightened and insight is gained into the use of pulse delays measured from the ECG for predicting non-invasively the arterial blood pressure.