Model number


(Matlab) A synthesizer framework for multimodal cardiorespiratory signals. Model from Hoog Antink et. al. 2017 paper.


(Abstract) Multimodal biosignals play an increasing role in medical signal processing. While many novel methods for acquisition and sensorfusion are developed and evaluated, the modeling aspect sees relatively little attention. To overcome this, a synthesizer framework for the generation of multimodal cardiorespiratory signals is presented. The first part of the model consists of a dynamic system of six coupled nonlinear ordinary differential equations. The resulting oscillators generate modalityindependent cardiac and respiratory phase signals, which are coupled by respiratory sinus arrhythmia. Moreover, Mayer oscillations of the heart rate are simulated. The second part of the model consists of modality-dependent waveform generators. For each modality, these waveform generators operate with a specific cardiac template that is modulated depending on the respiratory phase signal. The approach is validated qualitatively and quantitatively on three different databases for a variety of biosignals, namely standard electrocardiography (ECG), blood pressure signals, ballistocardiography, photoplethysmography, capacitively coupled ECG as well as respiratory flow and effort. Finally, the possibility to simulate a multimodal recording by combining templates obtained from different databases is demonstrated. The resulting multimodal biosignals mimic important physiological aspects such as modulation due to respiration and exhibit plausible phase relationships. An application of the model to evaluate an algorithm for sensor fusion is demonstrated.

fig 1

Figure: (Figure 14 of Hoog Antink et. al. 2017 paper) Virtual combination of conductive and capacitive ECG (rows one and two, record one of the UnoViS_Auto2012 database), a blood pressure signal (row three, subject ‘f2o01’ from the Fantasia database) and BCG as well as respiratory signal (rows four to six, one subject of the polysomnography database). Resulting RR-interval series generated by the coupled oscillator approach (row seven).

More details:

Matlab Model

To run:

  • Unzip file into local directory.
  • Open and run file synthesizer_demo.m with Matlab. Model should reproduce figure 14 of Hoog Antink et. al. 2017 publication. Note: you will need the Matlab Signal Processing Toolbox for the code to run.
C. Hoog Antink, S. Leonhardt, and M. Walter: "A Synthesizer Framework for Multimodal Cardiorespiratory Signals". Biomedical Engineering and Physics Express, June 2017
Key terms
signal synthesis
multimodal biosignals
dynamic systems
heart rate variability
respiratory sinus arrhythmia
Mayer waves
sensor fusion

Please cite in any publication for which this software is used and send one reprint to the address given below:
The National Simulation Resource, Director J. B. Bassingthwaighte, Department of Bioengineering, University of Washington, Seattle WA 98195-5061.

Model development and archiving support at provided by the following grants: NIH U01HL122199 Analyzing the Cardiac Power Grid, 09/15/2015 - 05/31/2020, NIH/NIBIB BE08407 Software Integration, JSim and SBW 6/1/09-5/31/13; NIH/NHLBI T15 HL88516-01 Modeling for Heart, Lung and Blood: From Cell to Organ, 4/1/07-3/31/11; NSF BES-0506477 Adaptive Multi-Scale Model Simulation, 8/15/05-7/31/08; NIH/NHLBI R01 HL073598 Core 3: 3D Imaging and Computer Modeling of the Respiratory Tract, 9/1/04-8/31/09; as well as prior support from NIH/NCRR P41 RR01243 Simulation Resource in Circulatory Mass Transport and Exchange, 12/1/1980-11/30/01 and NIH/NIBIB R01 EB001973 JSim: A Simulation Analysis Platform, 3/1/02-2/28/07.