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Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm

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Abstract

Cardiovascular disease is the main cause of death in Europe, and early detection of increased cardiovascular risk (CR) is of clinical importance. Pulse wave analysis based on pulse oximetry has proven useful for the recognition of increased CR. The current study provides a detailed description of the pulse wave analysis technology and its clinical application. A novel matching pursuit-based feature extraction algorithm was applied for signal decomposition of the overnight photoplethysmographic pulse wave signals obtained by a single-pulse oximeter sensor. The algorithm computes nine parameters (pulse index, SpO2 index, pulse wave amplitude index, respiratory-related pulse oscillations, pulse propagation time, periodic and symmetric desaturations, time under 90 % SpO2, difference between pulse and SpO2 index, and arrhythmia). The technology was applied in 631 patients referred for a sleep study with suspected sleep apnea. The technical failure rate was 1.4 %. Anthropometric data like age and BMI correlated significantly with measures of vascular stiffness and pulse rate variability (PPT and age r = −0.54, p < 0.001, PR and age r = −0.36, p < 0.01). The composite biosignal risk score showed a dose–response relationship with the number of CR factors (p < 0.001) and was further elevated in patients with sleep apnea (AHI ≥ 15n/h; p < 0.001). The developed algorithm extracts meaningful parameters indicative of cardiorespiratory and autonomic nervous system function and dysfunction in patients suspected of SDB.

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Abbreviations

AHI:

Apnea–Hypopnea Index

Arryth:

Irregular heart rate

BMI:

Body mass index

CV:

Cardiovascular

CPAP:

Continuous positive airway pressure

MAD:

Mean absolute deviation

MP:

Matching pursuit

OSA:

Obstructive sleep apnea

PASD:

Periodic and symmetric desaturations

PR:

Pulse rate

PWA:

Pulse wave amplitude

PPT:

Pulse propagation time

PR_SpO2_I:

Difference between pulse index and SpO2 index

RRPO:

Respiratory-related pulse oscillation

SDB:

Sleep-disordered breathing

SpO2 :

Oxygen saturation

TimeBelow90:

Time under 90 % SpO2

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Acknowledgments

Funding

The study was supported by Weinmann Geräte für Medizin GmbH & Co.KG, MCC GmbH & Co.KG, the German Ministry for Education and Science (BMBF), the Swedish Heart and Lung Foundation, the Gothenburg Medical Society and the Sahlgrenska Academy at the University of Gothenburg, Sweden.

Other contributions

The authors would like to express their gratitude for the support throughout the study to Jeanette Norum (Sahlgrenska), Lena Engelmark (Sahlgrenska), Martina Bögel (Weinmann, Hamburg, Germany), Matthias Schwaibold (MCC, Karlsruhe, Germany), and Bernd Schöller (MCC, Karlsruhe, Germany) for their technical and intellectual input during different parts of the study.

Author information

Authors and Affiliations

  1. Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Vigilance Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Dirk Sommermeyer, Ding Zou, Jan Hedner & Ludger Grote

  2. Institut für Assistenzsysteme und Qualifizierung e.V., Karlsruhe, Germany

    Dirk Sommermeyer

  3. Department of Respiratory Medicine, Allergology and Sleep Medicine, Klinikum Nuernberg, Nuremberg, Germany

    Joachim H. Ficker

  4. Paracelsus Medical University, Nuremberg, Germany

    Joachim H. Ficker

  5. Department of Pulmonary Medicine, Bethanien Hospital, Solingen, Germany

    Winfried Randerath

  6. Interdisciplinary Center of Sleep Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Christoph Fischer & Thomas Penzel

  7. Roche Diabetes Care GmbH, Mannheim, Germany

    Christoph Fischer

  8. Department of Pulmonary Medicine, Agaplesion Bethesda Krankenhaus Wuppertal, Wuppertal, Germany

    Bernd Sanner

Authors
  1. Dirk Sommermeyer
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  2. Ding Zou
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  3. Joachim H. Ficker
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  4. Winfried Randerath
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  6. Thomas Penzel
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  7. Bernd Sanner
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  8. Jan Hedner
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  9. Ludger Grote
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Corresponding author

Correspondence to Dirk Sommermeyer.

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Sommermeyer, D., Zou, D., Ficker, J.H. et al. Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm. Med Biol Eng Comput 54, 1111–1121 (2016). https://doi.org/10.1007/s11517-015-1410-8

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  • DOI: https://doi.org/10.1007/s11517-015-1410-8

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