EMFIT QS heart rate and respiration rate validation
- 3 January 2019
- journal article
- research article
- Published by IOP Publishing in Biomedical Physics & Engineering Express
- Vol. 5 (2), 025016
- https://doi.org/10.1088/2057-1976/aafbc8
Abstract
Objective: The EMFIT QS (Quantified Sleep) is an unobtrusive monitoring device with a state-of-the-art analysis platform that tracks heart (HR) and respiration rate (RR), as well as heart rate variability, in addition to providing a sleep stage estimation and sleep quality analysis. The device consists of a thin ferroelectret sensor that can be placed underneath a bed mattress and sleep analysis can be overviewed conveniently from the EMFIT QS web interface. With this kind of sensitive and contactless sensor subject's vital signs can be easily monitored without discomfort.Approach: We compared the EMFIT QS HR and RR to those evaluated from the electrocardiogram (ECG) and respiratory inductive plethysmography (RIP) of 33 patients measured in an overnight polysomnography. The Bland-Altman analysis was used for comparison and ±6 heart beats per minute (bpm) and ±4 respiration cycles per minute (rpm) were considered to be acceptable differences. Main results: The 95% limits of agreement (LoA) for the heart rate are -4.4 (CI95%: [-5.8, -4.2]) and 4.4 ( CI95%: [4.3, 5.9]) bpm, whereas the respiration rate LoA are -2.5 (CI95%: [-2.8, -2.4]) and 2.2 (CI95%: [2.1, 2.5]) rpm. Significance: The EMFIT QS measures reliably heart and respiration rate. It can be readily deployed for vital monitoring when a subject is lying in bed e.g. when tracking athletes' night-time recovery or consumers' well-being. It also has potential in telemedicine applicationsKeywords
This publication has 38 references indexed in Scilit:
- Reporting of method comparison studies: a review of advice, an assessment of current practice, and specific suggestions for future reportsBritish Journal of Anaesthesia, 2016
- How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables modelsStatistics in Medicine, 2016
- Ambient and Unobtrusive Cardiorespiratory Monitoring TechniquesIEEE Reviews in Biomedical Engineering, 2015
- Automatic Detection of Atrial Fibrillation in Cardiac Vibration SignalsIEEE Journal of Biomedical and Health Informatics, 2012
- Respiration rate monitoring methods: A reviewPediatric Pulmonology, 2011
- Agreement Between Methods of Measurement with Multiple Observations Per IndividualJournal of Biopharmaceutical Statistics, 2007
- Measuring agreement in method comparison studiesStatistical Methods in Medical Research, 1999
- A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurementComputers in Biology and Medicine, 1990
- STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENTThe Lancet, 1986
- Measurement in Medicine: The Analysis of Method Comparison StudiesJournal of the Royal Statistical Society: Series D (The Statistician), 1983