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Med Biol Eng Comput. 2016 Jul;54(7):1111-21. doi: 10.1007/s11517-015-1410-8. Epub 2015 Nov 4.

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

Author information

1
Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Vigilance Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. dirk.sommermeyer@iaq-hd.de.
2
Institut für Assistenzsysteme und Qualifizierung e.V., Karlsruhe, Germany. dirk.sommermeyer@iaq-hd.de.
3
Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Vigilance Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
4
Department of Respiratory Medicine, Allergology and Sleep Medicine, Klinikum Nuernberg, Nuremberg, Germany.
5
Paracelsus Medical University, Nuremberg, Germany.
6
Department of Pulmonary Medicine, Bethanien Hospital, Solingen, Germany.
7
Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.
8
Roche Diabetes Care GmbH, Mannheim, Germany.
9
Department of Pulmonary Medicine, Agaplesion Bethesda Krankenhaus Wuppertal, Wuppertal, Germany.

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.

KEYWORDS:

Cardiovascular risk; Matching pursuit; Neuro-fuzzy system; Photoplethysmography; Sleep

PMID:
26538425
DOI:
10.1007/s11517-015-1410-8
[Indexed for MEDLINE]

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