King's College London
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Simulated electrocardiogram and photoplethysmogram signals modulated by respiration

posted on 2021-09-13, 18:33 authored by Peter H. Charlton, Timothy Bonnici, Richard Beale, Lionel Tarassenko, David A. Clifton, Peter J. Watkinson
This dataset is a collection of simulated electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals modulated by respiration. It was designed for assessment of algorithms for estimation of respiratory rate (RR) from the ECG and PPG. It serves two purposes. Firstly, it allows one to determine whether an RR algorithm has been implemented reasonably (i.e. whether it estimates RR accurately in ideal conditions). Secondly, it allows one to assess the limitations of RR algorithms, such as whether they perform accurately in the presence of different types of respiratory modulation, and whether their performance is dependent on the underlying heart rate (HR) or RR. The dataset was generated as follows. Firstly, exemplary ECG and PPG beats were measured, as part of the Vortal Clinical Trial (National Clinical Trial 01472133). Ethical approval was obtained from the London Westminster Research Ethics Committee (11/LO/1667), and informed consent was obtained. These beats were anonymised, and modified to last exactly one second. They were then repeated 210 times, to provide a simulated train of beats, 210 s in duration. This signal was then modulated by each of the three respiratory modulations in turn (baseline wander, amplitude modulation, and frequency modulation), producing three separate signals. This process was repeated for a range of HRs (30 - 200 beats per minute) and RRs (4 - 60 breaths per minute). When the HR was varied, the RR was fixed at 20 bpm. When the RR was varied, the HR was fixed at 80 bpm. Signals are sampled at 100 Hz. The data is provided in three formats: (i) comma-separated value format (.csv); (ii) WaveForm DataBase (WFDB) format; (iii) and Matlab ® format (.mat). In addition, the Matlab ® scripts used to generate the dataset and convert it into multiple formats are provided, allowing the user to reproduce the dataset and create similar datasets to their own specification. This version of the dataset (v.1.0) is provided to allow users to perform similar analyses to those performed in the following associated publication, where the dataset was first described: Charlton, P. H., Bonnici, T., Tarassenko, L., Clifton, D. A., Beale, R., & Watkinson, P. J. (2016). An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiological Measurement, 37(4), 610–626. DOI: 10.1088/0967-3334/37/4/610 Further details of the dataset are provided in the file (a text file). ORCID IDs: Peter H. Charlton: 0000-0003-3836-8655; Timothy Bonnici: 0000-0003-3113-1188


Engineering and Physical Sciences Research Council (EPSRC, Grant EP/H019944/1)

The National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London

The NIHR Oxford Biomedical Research Centre Programme

The Oxford Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grant no. WT88877/Z/09/Z

A Royal Academy of Engineering (RAEng) Research Fellowship awarded to David A. Clifton

An EPSRC Challenge Award to David A. Clifton

King's College London Medical Engineering Centre.

Wellcome Trust

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Collection method

Synthetic data



Copyright owner

Copyright - Peter H. Charlton