Abstract
Based on a model of three coupled oscillators describing the influence of respiration, namely respiratory sinus arrhythmia (RSA), and so-called Mayer waves on the heart rate, an unscented Kalman filter (UKF) is designed to perform sensor fusion of multimodal cardiorespiratory sensor signals. The aim is to implicitly use redundancy between the sensor signals to improve the estimated heart rate while utilising model knowledge. The effectiveness of the approach is shown by estimations of the heart rate on synthesised data as well as patient data from the Fantasia dataset and a Sleep laboratory which provide two, three or six sensor channels for resting individuals. It could be shown that the approach is able to fuse multimodal sensor signals on signal level to achieve more accurate estimations. For real data, errors in mean heart rate as small as 1.56 % were achieved.
Zusammenfassung
Basierend auf einem Modell, das den Einfluss von Atmung, d. h. respiratorische Sinusarrhythmie, und den sogenannten Mayer-Wellen auf die Herzfrequenz beschreibt, wird ein Unscented Kalman-Filter (UKF) zur Fusion von multimodalen kardiorespiratorischen Sensorsignalen entworfen. Das Ziel ist es, implizit Redundanz zwischen den Sensorsignalen auszunutzen, um die Schätzung der Herzfrequenz durch das Ausnutzen von Modellwissen zu verbessern. Die Effektivität dieses Ansatzes wird anhand der Schätzung der Herzfrequenz aus synthetisierten und echten Daten aus dem Fantasiadatensatz und einem Schlaflabor mit zwei, drei bzw. sechs Sensorenkanälen gezeigt. Es konnte gezeigt werden, dass der Ansatz tatsächlich dafür genutzt werden kann, multimodale Sensorsignale auf der Signalebene zu fusionieren und dadurch genauere Schätzungen zu erzielen. Für Echtdaten konnten Fehler bezüglich der mittleren Herzfrequenz von nur 1.56 % erreicht werden
Funding source: Deutsche Forschungsgemeinschaft
Award Identifier / Grant number: LE 817/26-1
Funding statement: The authors gratefully acknowledge the financial support provided by the German Research Foundation [Deutsche Forschungsgemeinschaft (DFG), LE 817/26-1].
About the authors
Onno Linschmann was born in Herzberg am Harz, Germany, in 1995. In 2016 he received the B. Sc. degree in electrical engineering with a major in Information and Communications Engineering and in 2019 the M. Sc. degree in electrical engineering with a major in System Engineering and Automation at RWTH Aachen University, Aachen. He is currently a Ph. D. candidate at the Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany. His research interests include the field of sensor fusion, nonlinear estimation and scenario detection for medical applications.
Steffen Leonhardt (SM’06) was born in Frankfurt, Germany, in 1961. He received the M. S. degree in computer engineering from the State University of New York at Buffalo, Buffalo, NY, USA, the Dr.-Ing. degree (Ph. D.) in electrical engineering from the Technical University of Darmstadt, Darmstadt, Germany, and the M. D. degree in medicine from J. W. Goethe University, Frankfurt, Germany. He has almost five years of R&D management experience working for Dräger Medical AG & Company, KGaA., Lübeck, Germany, and was appointed Full Professor and Head of the Philips endowed Chair of Medical Information Technology at RWTH Aachen University, Aachen, Germany, in 2003. Among others, Dr. Leonhardt serves as an associate Editor of the IEEE Journal of Biomedical and Health Informatics and IEEE Transactions on Biomedical Circuits and Systems. In 2014, he became a fellow of the NRW Academy of Sciences, Humanities and the Arts in Düsseldorf, Germany. In 2015, he was appointed a distinguished lecturer by the IEEE EMBS. He holds a honorary degree from CTU Prague (Dr. h. c., 2018) and has been appointed distinguished professor at IIT Madras, India, in 2018.
Christoph Hoog Antink (M’14) was born in Lohne (Oldenburg), Germany in 1985. He obtained a M. S. degree in mechanical engineering from the University at Buffalo, Buffalo, New York in 2011. He also holds a Dipl.-Ing. (2012) and a Dr.-Ing. (Ph. D., 2018) degree in electrical engineering from the RWTH Aachen University, Aachen, Germany. He is currently heading the Medical Signal Processing Group at RWTH Aachen University’s Medical Information Technology. His research interests include unobtrusive sensing of vitals signs, sensor fusion, and machine learning in medicine.
Acknowledgment
The authors thank Tarun Singh for helping with the CUT implementation.
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