Circadian Rhythm Estimation Research Articles

Accuracy and reliability of wristband smartwatch-based estimation of circadian rhythm of heart rate in stroke patients

Background & Objective: Stroke has a high incidence, disability, and mortality rate, which has a serious impact on the patients’ quality of life. Heart rate (HR) is valuable in the prognostic assessment of stroke patients. Continuous HR tracking helps to detect abnormal indicators and provide interventions timely to avoid disease progression. However, the cuff-based or electrode-based HR monitoring tools commonly used in clinic are not suitable for continuous monitoring in daily life. The aim of this study was to verify the accuracy and reliability of a wristband smartwatch in monitoring HR over 24-hour in stroke patients, including in terms of the circadian rhythm characteristics of HR. Methods: 25 stroke patients underwent 24-hour HR monitoring by wearing a smartwatch and a clinical monitor. We use ambulatory blood pressure monitors and bedside monitors as the gold standard for clinical HR monitoring. The degree of association between the two measurements was compared using Pearson’s correlation coefficient. Bland-Altman analysis showed the accuracy of the smartwatch in HR monitoring. Intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to verify the reliability of the wristband smartwatch for repeated measurements. Results: A total of 625 heart rate pairs were acquired. The smartwatches had an average error of 0.816 bpm in 24-hour, with no difference from standard monitors (P>0.05), indicating good accuracy. The intraday repeated measures ICC was 0.969 and the interday repeated measures ICC was 0.788, indicating good reliability. Conclusions: The smartwatch is acceptable in detecting 24-hour ambulatory HR in stroke patients, and in the future, long-term HR monitoring with diurnal variations can be performed in daily life.

Estimating Day-to-Day Circadian Rhythm in Patients with Severe Acquired Brain Injury at the Beginning of In-Hospital Rehabilitation.

Patients with severe acquired brain injury (sABI) are likely to have a disturbed circadian rhythm in the early phase of neurorehabilitation. Circadian rhythm and sleep play an important role in the rehabilitation of patients with severe acquired brain injury (sABI). Research has also pointed out the importance of investigating novel ways of assessing sleep and circadian rhythm in patients with acquired brain injury. Established methods fail to apply to the heterogeneous and fluctuant biological or behavioral signals of the patients with sABI. Accelerometry (ACC) has proven a useful measure of circadian rhythm in sABI patients. However, ACC is unavailing if patients have limited motor activity due to a low consciousness level or severe paresis. Heart rate (HR) could be a viable alternative. In this study, we aim to present a novel model for the estimation of circadian rhythm and rhythm characteristics in both motor-active and -inactive patients using ACC and HR. Furthermore, we aim to present the results of the model in patients with sABI during their first three weeks of subacute in-hospital neurorehabilitation. An explorative observational study. Continuous recordings of ACC and electrocardiography were conducted. The suggested model was applied to examine circadian rhythms. This study has proven the feasibility of a novel model for the analysis of circadian rhythm. Twenty-nine patients were included, 20 motor active and nine motor inactive. Estimates of rhythm characteristics have been presented along with estimates of circadian rhythm presence or absence for both groups. The model has been successfully applied in a population of patients with sABI. The circadian rhythm of patients undergoing in-hospital neurorehabilitation is fluctuating across time and highly variant between subjects within the first three weeks after admission to sub-acute neurorehabilitation.

Different circadian rest-active rhythms in Kleine-Levin syndrome: a prospective and case-control study.

Kleine-Levin syndrome (KLS) is a rare recurrent hypersomnia. Our study aimed at monitoring the movements of patients with KLS using actigraphy and evaluating their circadian rhythm. Twenty young patients with KLS and 14 age-matched controls were recruited. Each individual wore an actigraphy for more than 6 months to monitor at least two attacks. Controls kept wearing the device for at least 7 days. The activity counts were averaged in hourly basis and the day-to-night amplitude was quantified by the differences of the averaged activity counts during daytime and nighttime. The hourly activities of different days were aligned and averaged to construct the circadian profile. Parametric and nonparametric estimation of circadian rhythm was calculated. We applied detrended fluctuation analysis to evaluate the temporal correlations beneath the activity fluctuations at multiple time scales. Circadian rhythm in asymptomatic period showed no significant difference compared to the controls. During hypersomnia attack, the amplitude of the circadian rest-active rhythms drastically decreased and decreased interdaily stability (IS) was found, as well as significant decreased M10 and short-time fractal correlation (α1). Drastically decreased mean and standard deviation of activity were noted, compared to the pre-attack phase and recovery phase. α1 and M10 increased during the late attack phase, and overcompensated IS was noted in the recovery phase. This study confirmed that circadian rest-active rhythms was affected when KLS hypersomnia attack. Several parameters including M10, IS, and α1 may be physiological markers of KLS, which can help to predict the end of hypersomnia episodes.

0325 Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) improves estimation of Circadian Rhythm

0325 Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) improves estimation of Circadian Rhythm

368 - Circadian Rhythm Estimator Using Adaptive Notch Filter and Its Application to Locomotor Activity of Drosophila Melanogaster

The traditional method of circadian rhythm estimation is based on a double actogram that provides phase estimation on a daily basis, but not continuously and also fails to effectively filter noise. Our goal is to develop a circadian rhythm estimator that accurately and continuously estimates circadian phase while filtering noise. The estimator is a modified Adaptive Notch Filter (ANF), which is commonly used in signal processing. Drosophila Activity Monitor boxes house Drosophila fruit flies previously entrained to the same phase and contain sensors that measure the activity of flies. The actogram and ANF convert the raw data to a phase response curve (PRC), which describes the relationship between a stimuli (light) and circadian rhythm phase shift. White noise-corrupted data was also introduced to determine filtering effects. The PRC obtained from the actogram and ANF methods were nearly identical and also matched with literature values. With noise-corrupted data, the ANF PRC had an average error of 0.57 hours while the actogram PRC’s average error was 1.84 hours. The modified ANF is shown to be a valid and accurate method of modeling circadian rhythm continuously. The ANF was also able to successfully reject noise and the error against noise was very small compared to that of the actogram. Continuous circadian phase estimation allows more complex study of human circadian rhythm and further opens the possibility of personalized circadian rhythm estimation and light therapy.

Circadian Rhythm Estimation by Core Body Temperature Filtered with Simultaneously Recorded Physiological Data

In field measurements, monitoring of core body temperature is influenced by physical activities; therefore, the estimation of circadian rhythm from the data may not be exact. The purpose of this study is to design a core body temperature filter in order to reduce artifacts induced by physical activities using simultaneously recorded physiological data such as heart rate data. The effects of physical activities on core body temperature and heart rate are assessed through three experiments. Based on the above knowledge, a core body temperature filter was designed. The filter removes part of rectal temperature data as artifact when heart rate rises above a predetermined threshold. As a result, most of the spike-like noise was removed and the filtered temperature data showed sinusoidal variation more than the unfiltered data. The mesor of the estimated rhythm significantly decreased. This filtering method can provide more precise information about circadian rhythm, especially in field measurements.