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.

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.

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.

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.

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.

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.

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.