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An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors

BACKGROUND: Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noi...

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Autores principales: Foussier, Jerome, Teichmann, Daniel, Jia, Jing, Misgeld, Berno, Leonhardt, Steffen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029942/
https://www.ncbi.nlm.nih.gov/pubmed/24886253
http://dx.doi.org/10.1186/1472-6947-14-37
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author Foussier, Jerome
Teichmann, Daniel
Jia, Jing
Misgeld, Berno
Leonhardt, Steffen
author_facet Foussier, Jerome
Teichmann, Daniel
Jia, Jing
Misgeld, Berno
Leonhardt, Steffen
author_sort Foussier, Jerome
collection PubMed
description BACKGROUND: Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. METHODS: We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. RESULTS: Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min (−1) (0.3 min (−1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min (−1) (0.4 min (−1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. CONCLUSIONS: It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.
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spelling pubmed-40299422014-06-06 An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors Foussier, Jerome Teichmann, Daniel Jia, Jing Misgeld, Berno Leonhardt, Steffen BMC Med Inform Decis Mak Technical Advance BACKGROUND: Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. METHODS: We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. RESULTS: Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min (−1) (0.3 min (−1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min (−1) (0.4 min (−1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. CONCLUSIONS: It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals. BioMed Central 2014-05-09 /pmc/articles/PMC4029942/ /pubmed/24886253 http://dx.doi.org/10.1186/1472-6947-14-37 Text en Copyright © 2014 Foussier et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Foussier, Jerome
Teichmann, Daniel
Jia, Jing
Misgeld, Berno
Leonhardt, Steffen
An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
title An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
title_full An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
title_fullStr An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
title_full_unstemmed An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
title_short An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
title_sort adaptive kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029942/
https://www.ncbi.nlm.nih.gov/pubmed/24886253
http://dx.doi.org/10.1186/1472-6947-14-37
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