Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data
Autoři:
Yu-Ting Lin aff001; Yu-Lun Lo aff002; Chen-Yun Lin aff003; Martin G. Frasch aff004; Hau-Tieng Wu aff003
Působiště autorů:
Department of Anesthesiology, Taipei Veteran General Hospital, Taipei, Taiwan
aff001; Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University, College of Medicine, Taipei, Taiwan
aff002; Department of Mathematics, Duke University, Durham, NC, United States of America
aff003; Department of Obstetrics and Gynecology and Center on Human Development and Disability (CHDD), University of Washington, Seattle, WA, United States of America
aff004; Department of Statistical Science, Duke University, Durham, NC, United States of America
aff005; Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan
aff006
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0221319
Souhrn
Object
It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a reliable pulse transit time (PTT) signal.
Approach
This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit. All data sets include recordings of PPG and ECG using a commonly deployed patient monitor. We calculated the PTT signal offline.
Main results
We report a novel constant oscillatory pattern in the PTT signal and identify this pattern as a sawtooth artifact. We apply an approach based on the de-shape method to visualize, quantify and validate this sawtooth artifact.
Significance
The PPG and ECG signals not designed for the PTT evaluation may contain unwanted artifacts. The PTT signal should be calibrated before analysis to avoid erroneous interpretation of its physiological meaning.
Klíčová slova:
Research and analysis methods – Bioassays and physiological analysis – Electrophysiological techniques – Cardiac electrophysiology – Electrocardiography – Equipment preparation – Instrument calibration – Research assessment – Research monitoring – Engineering and technology – Signal processing – Instrumentation – Equipment – Medicine and health sciences – Surgical and invasive medical procedures – Vascular medicine – Blood pressure – Computer and information sciences – Data acquisition
Zdroje
1. Feldman JM. Can clinical monitors be used as scientific instruments? Anesthesia and Analgesia. 2016; 103(5), 1071–1072.
2. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential, Health Inf Sci Syst. 2014; 2(1):3
3. Johnson AEW, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific Data; 2016;3, 160035. doi: 10.1038/sdata.2016.35 27219127
4. Takla G, Petre JH, Doyle DJ, Horibe M, Gopakumaran B. The Problem of Artifacts in Patient Monitor Data During Surgery: A Clinical and Methodological Review, Anesthesia and Analgesia. 2006;103(5):1196–1204 doi: 10.1213/01.ane.0000247964.47706.5d 17056954
5. Nizami S, Green JR, McGregor C. Implementation of artifact detection in critical care: A methodological review, IEEE Reviews in Biomedical Engineering. 2013;6:127–142 doi: 10.1109/RBME.2013.2243724 23372087
6. Hravnak M, Chen L, Dubrawski AB, Eliezer C, Gilles P, Michael R. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data, J. Clin Monit Comput. 2016; 30(6):875–888 doi: 10.1007/s10877-015-9788-2 26438655
7. Chen L, Dubrawski A, Wang D, et al. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data, Crit. Care Med. 2016; 44(7):e456–e463 doi: 10.1097/CCM.0000000000001660 26992068
8. Gravina R, Alinia P, Ghasemzadeh H, Fortino G. Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges, Inf. Fusion, 2017; 35:68–80.
9. Cannesson M, Shafer SL. All boxes are black. Anesthesia and Analgesia. 2016;122(2), 309–317. doi: 10.1213/ANE.0000000000001122 26797548
10. Ruskin KJ, Shelley KH. Patent medicine and the “black box.” Anesthesia and Analgesia. 2005;100(5), 1361–1362. doi: 10.1213/01.ANE.0000148122.25862.A1 15845685
11. Shelley KH, Barker SJ. Disclosures, what is necessary and sufficient? Anesthesia and Analgesia. 2016;122(2), 307–308. doi: 10.1213/ANE.0000000000000742 26797547
12. Mukkamala R, Hahn JO, Inan OT, et al. Towards Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice, IEEE Trans Biomed Eng. 2015;62(8):1879–1901 doi: 10.1109/TBME.2015.2441951 26057530
13. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OJBZ67
14. Gesche H, Grosskurth D, Küchler G, Patzak A. Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. European journal of applied physiology. 2012; 112(1):309–315. doi: 10.1007/s00421-011-1983-3 21556814
15. Lin CY, Su L, Wu HT. Wave-shape function analysis–when cepstrum meets time-frequency analysis, Journal of Fourier Analysis and Applications. 2018; 24(2):451–505
16. http://10.6084/m9.figshare.9632063
17. Shelley KH. Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate. Anesthesia and analgesia. 2007;105(6 Suppl):S31.
18. Tang SC, Huang PW, Hung CS. Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram. Scientific Reports. 2017;7. doi: 10.1038/s41598-017-00035-9
19. Huiku M, Uutela K, Van Gils M, et al. Assessment of surgical stress during general anaesthesia. British journal of anaesthesia. 2007;98(4):447–455. doi: 10.1093/bja/aem004 17329347
20. Kim SH, Song JG, Park JH, Kim JW, Park YS, Hwang GS. Beat-to-beat tracking of systolic blood pressure using noninvasive pulse transit time during anesthesia induction in hypertensive patients. Anesthesia and analgesia. 2013;116(1):94. doi: 10.1213/ANE.0b013e318270a6d9 23223109
21. Lu Y, Wu HT, Malik J. Maximize information quantity for mobile health–recycling the cardiogenic artifact in impedance plethysmography as an example. Biomedical Signal Processing & Control. 2019; 51, 162–170
22. Su L, Wu HT. Extract fetal ECG from single-lead abdominal ECG by de-shape short time Fourier transform and nonlocal median. Frontiers in Applied Mathematics and Statistics. 2017;3:2.
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- Případ Bulovka: Jak postupují jiné nemocnice, aby podobným tragédiím zabránily?
- Jak často se u masových střelců vyskytují duševní choroby?
- Zvoní budík? Přispěte si ještě chvilku, bude vám to myslet rychleji!
- FDA varuje před selfmonitoringem cukru pomocí chytrých hodinek. Jak je to v Česku?
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy