• No results found

University of Groningen Ambulatory assessment of human circadian phase and related sleep disorders from heart rate variability and other non-invasive physiological measurements Gil Ponce, Enrique

N/A
N/A
Protected

Academic year: 2021

Share "University of Groningen Ambulatory assessment of human circadian phase and related sleep disorders from heart rate variability and other non-invasive physiological measurements Gil Ponce, Enrique"

Copied!
3
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

University of Groningen

Ambulatory assessment of human circadian phase and related sleep disorders from heart

rate variability and other non-invasive physiological measurements

Gil Ponce, Enrique

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.

Document Version

Publisher's PDF, also known as Version of record

Publication date: 2017

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):

Gil Ponce, E. (2017). Ambulatory assessment of human circadian phase and related sleep disorders from heart rate variability and other non-invasive physiological measurements. University of Groningen.

Copyright

Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

Take-down policy

If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.

(2)

141

11

L

IST OF TABLES

Table 1.1 Summary of reported results for 4 circadian phase estimation models using non-invasive data in ambulatory conditions. Models are sorted by accuracy

(standard deviation of the error). ... 32

Table 2.1 Average error (bias) defined as the difference between the measured DLMO and the predicted DLMO, and standard deviation of the error (SD error) for the ARMAX models using activity, RR intervals, and light. Pearson’s R coefficient indicating a significant correlation (p<0.05) are shown in bold. ... 50

Table 4.1 Summary of results ... 72

Table 5.1 Subject characteristics of 16 subjects with valid PPG and ECG data. ... 85

Table 5.2 Performance of original models on an older subject population. The models were applied on the ECG signals of all subjects (N=29) and shown in A. The subset of subject which in addition to having usable ECG signals also had usable PPG signals (N=16) are shown in B. The various models represent all possible input signal combinations: RR Intervals (R), activity levels (A), and light exposure (L). ... 86

Table 5.3 Performance of the models based on PPG signal from a wrist-worn optical heart rate sensor (N=16). ... 87

Table 5.4 Testing of retrained models based on ECG data targeted at the older subject population. ... 87

Table 5.5 Testing of retrained models based on PPG data targeted at the older subject population. ... 88

Table 5.6 Performance of newly ECG-trained models from older adults (N=8) when applied to PPG data from a separate sample of the older adult population (N=8). ... 88

Table 6.1 Subject characteristics and questionnaire outcomes. ... 98

Table 6.2 Sleep statistics of the SOI patients. ... 99

Table 6.3 Distribution of different sleep stages in SOI patients. ... 100

Table 6.4 Summary of studies reporting the timing of the SDNN maximum in healthy subjects... 102

Table 7.1 Demographics ... 115

Table 7.2 Accuracy of all model configurations ranked by accuracy. ... 116

Table 7.3 Differences in phase angles of entrainment (hours) between healthy subjects and sleep onset insomnia patients... 117

(3)

Referenties

GERELATEERDE DOCUMENTEN

Heart rate variability analyses of 24-hour electrocardiograms from sleep onset insomnia patients show an altered SDNN profile, with its maximum occurring shortly before

This study, however, serves as an initial investigation to pilot the assessment of circadian phase estimation models based on RR intervals, activity levels, and

Models have been presented based on heart rate and heart rate variability features which provide accurate circadian phase estimates in both healthy and sleep onset

Ambulatory assessment of human circadian phase and related sleep disorders from heart rate variability and other non-invasive physiological measurements.. Gil

The solid line labeled “Mean Diff” shows the mean difference between the predicted DLMO and the measured DLMO (bias), the dashed lines labeled “Mean Diff ± SD” show the

Ambulatory assessment of human circadian phase and related sleep disorders from heart rate variability and other non-invasive physiological measurements.. Gil

Estas limitaciones han sido la motivación para el trabajo en esta tesis de desarrollar un método para estimar con exactitud la fase circadiana que sea no-invasivo, se puede

Die aspecten zijn de aanleiding geweest om een andere, niet invasieve manier te ontwikkelen om de stand van de klok te bepalen, waarbij geen restricties worden