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Using the features of the time and volumetric capnogram for classification and prediction

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Abstract

Quantitative features derived from the time-based and volumetric capnogram such as respiratory rate, end-tidal PCO2, dead space, carbon dioxide production, and qualitative features such as the shape of capnogram are clinical metrics recognized as important for assessing respiratory function. Researchers are increasingly exploring these and other known physiologically relevant quantitative features, as well as new features derived from the time and volumetric capnogram or transformations of these waveforms, for: (a) real-time waveform classification/anomaly detection, (b) classification of a candidate capnogram into one of several disease classes, (c) estimation of the value of an inaccessible or invasively determined physiologic parameter, (d) prediction of the presence or absence of disease condition, (e) guiding the administration of therapy, and (f) prediction of the likely future morbidity or mortality of a patient with a presenting condition. The work to date with respect to these applications will be reviewed, the underlying algorithms and performance highlighted, and opportunities for the future noted.

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Notes

  1. The continuous graphical time tracing of carbon dioxide from the breath.

References

  1. Barrett WF. On a physical analysis of the human breath. Phil. Mag. 1864; XXVIII:108–21.

  2. Rose H. A practical treatise of chemical analysis—Vol. II Quantitative. London: William Tegg and Co.; 1849. p. 534.

    Google Scholar 

  3. Wells DA, editor. Year-book of facts in science and art for 1865. Boston: Gould and Lincoln; 1865. p. 245–6.

    Google Scholar 

  4. Aiken RS, Clark-Kennedy AE. On the fluctuation in the composition of the alveolar air during the respiratory cycle in muscular exercise. J Physiol. 1928;65:389–411.

    Article  Google Scholar 

  5. Jaffe MB. Infrared measurement of carbon dioxide in the human breath: “breathe-through” devices from Tyndall to the present day. Anesth Analg. 2008;107:890–904. doi:10.1213/ane.0b013e31817ee3b3.

    Article  PubMed  Google Scholar 

  6. Jaffe MB. Time and volumetric capnography. In: Ehrenfeld JM, Cannesson M, editors. Monitoring technologies in acute care environments. Berlin: Springer; 2014. p. 179–91.

    Chapter  Google Scholar 

  7. Bellville JW, Seed JC. Respiratory carbon dioxide response curve computer. Science. 1959;130:1079–83.

    Article  CAS  PubMed  Google Scholar 

  8. Berengo A, Cutillo A. Single-breath analysis of carbon dioxide concentration records. J Appl Physiol. 1961;16:522–30.

    Google Scholar 

  9. Murphy TW. Analogue-digital data processing of respiratory parameters, AFIPS, managing requirements knowledge, international workshop on, managing requirements knowledge, international workshop on 1965, p. 253–57. 1965. doi:10.1109/AFIPS.1965.24.

  10. Noe FE. Computer analysis of curves from an infrared CO2 analyzer and screen-type airflow meter. J Appl Physiol. 1963;18:149–57.

    Google Scholar 

  11. Fletcher R. Volumetric capnography: the early days. In: Gravenstein JS, et al., editors. Capnography: clinical aspects. Cambridge: Cambridge University Press; 2004. p. 381–4.

    Google Scholar 

  12. Bao W, King P, Zheng J, Smith BE. Expert capnogram analysis. IEEE Eng Med Biol Mag. 1992;11:62–6.

    Article  Google Scholar 

  13. Van Genderingen HR, Gravenstein N, van der Aa JJ, Gravenstein JS. Computer-assisted capnogram analysis. J Clin Monit. 1987;3:194–200.

    Article  PubMed  Google Scholar 

  14. Ventzas D. CAPNEX: an expert system for capnography (CO2 respiration analysis). Trans Institute Measurement Control. 1994;16:233–44.

    Article  Google Scholar 

  15. Kelsey JE, Oldham EC, Horvath SM. Expiratory carbon dioxide concentration curve. A test of pulmonary function. Dis Chest. 1962;41:498–503.

    Article  CAS  PubMed  Google Scholar 

  16. Smalhout B, Kalenda Z. An atlas of capnography. 2nd ed. Amsterdam: Kerckebosche Zeist Press; 1981.

    Google Scholar 

  17. Arsowa S, Schmalisch G, Wauer RR. Techniques and clinical application of capnography in newborn infants and infants. Padiatr Grenzgeb. 1993;31:295–311.

    CAS  PubMed  Google Scholar 

  18. Thompson JE, Jaffe MB. Capnographic waveforms in the mechanically ventilated patient. Respir Care. 2005;50:100–8.

    PubMed  Google Scholar 

  19. Technical Staff. A History of Innovation from Novametrix to Philips, Philips Publication. http://www.oem.respironics.com/wp/A_History_of_InnovationFromNovametrix_to_Philips.pdf. 2011. Accessed on 5/29/2015.

  20. McSwain SD, Hamel DS, Smith PB, Gentile MA, Srinivasan S, Meliones JN, Cheifetz IM. End-tidal and arterial carbon dioxide measurements correlate across all levels of physiologic dead space. Respir Care. 2010;55:288–93.

    PubMed  PubMed Central  Google Scholar 

  21. Levine RL, Wayne MA, Miller CC. End-tidal carbon dioxide and outcome of out-of-hospital cardiac arrest. N Engl J Med. 1997;337:301–6.

    Article  CAS  PubMed  Google Scholar 

  22. Wiegand UK, Kurowski V, Giannitsis E, Katus HA, Djonlagic H. Effectiveness of end-tidal carbon dioxide tension for monitoring thrombolytic therapy in acute pulmonary embolism. Crit Care Med. 2000;28:3588–92.

    Article  CAS  PubMed  Google Scholar 

  23. You B, Peslin R, Duvivier C, Vu VD, Grilliat JP. Expiratory capnography in asthma: evaluation of various shape indices. Eur Respir J. 1994;7:318–23.

    Article  CAS  PubMed  Google Scholar 

  24. Kean TT, Teo AH, Malarvili MB. Feature extraction of capnogram for asthmatic patient. In: Second international conference on computer engineering and applications (ICCEA) p. 251–55. 2010.

  25. Herry CL, Townsend D, Green GC, Bravi A, Seely AJE. Segmentation and classification of capnograms: application in respiratory variability analysis. Physiol Meas. 2014;35:2343–58.

    Article  CAS  PubMed  Google Scholar 

  26. Bhavani-Shankar K, Philip JH. Defining segments and phases of a time capnogram. Anesth Analg. 2000;91:973–7.

    Article  CAS  PubMed  Google Scholar 

  27. Galia F, Brimioulle S, Bonnier F, Vandenbergen N, Dojat M, Vincent JL, Brochard LJ. Use of maximum end-tidal CO2 values to improve end-tidal CO2 monitoring accuracy. Respir Care. 2011;56:278–83. doi:10.4187/respcare.00837.

    Article  PubMed  Google Scholar 

  28. Jaffe, MB. What is a valid breath? Methodological Issues. Annual Meeting of the Society for Technology in Anesthesia, abstract 5, 2011.

  29. Colman J, Cohen J, Lain D. Smart Alarm Respiratory Analysis (SARA™) used in capnography to reduce alarms during spontaneous breathing. In: Annual meeting of the Society for Technology in Anesthesia, 2008.

  30. Orr JA, Brewer LM, Westenskow DR, Johnson KB. Evaluation of breath rate measurement by capnometry in non-intubated sedated volunteers. In: Anesthesiology, annual meeting of American Society of Anesthesiologists, A1292, 2009.

  31. Breen PH, Serina ER, Barker SJ. Measurement of pulmonary CO2 elimination must exclude inspired CO2 measured at the capnometer sampling site. J Clin Monit. 1996;12:231–6.

    Article  CAS  PubMed  Google Scholar 

  32. Rayburn DB, Fitzpatrick TM, Van Albert SA. Neural network evaluation of slopes from sequential volume segments of expiratory carbon dioxide curves. In: IEEE international conference on neural networks, IEEE world congress on computational intelligence, p. 3530–3, vol. 6. 1994.

  33. Tusman G, Scandurra A, Böhm SH, Suarez-Sipmann F, Clara F. Model fitting of volumetric capnograms improves calculations of airway dead space and slope of phase III. J Clin Monit Comput. 2009;23:197–206. doi:10.1007/s10877-009-9182-z.

    Article  PubMed  Google Scholar 

  34. Fletcher R, Jonson B, Cumming G, Brew J. The concept of deadspace with special reference to the single breath test for carbon dioxide. Br J Anaesth. 1981;53:77–88.

    Article  CAS  PubMed  Google Scholar 

  35. Fowler WS. Lung function studies; the respiratory dead space. Am J Physiol. 1948;154:405–16.

    CAS  PubMed  Google Scholar 

  36. Brewer LM, Orr JA, Pace NL. Anatomic dead space cannot be predicted by body weight. Respir Care. 2008;7:885–91.

    Google Scholar 

  37. Tang Y, Turner MJ, Baker AB. Systematic errors and susceptibility to noise of four methods for calculating anatomical dead space from the CO2 expirogram. Br J Anaesth. 2007;98:828–34.

    Article  CAS  PubMed  Google Scholar 

  38. Hjorth Bo, Elema-Schönander AB. EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol. 1970;2(2):306–10.

    Article  Google Scholar 

  39. Fletcher R, Jonson B. Deadspace and the single breath test for carbon dioxide during anaesthesia and artificial ventilation. Effects of tidal volume and frequency of respiration. Br J Anaesth. 1984;56:109–19.

    Article  CAS  PubMed  Google Scholar 

  40. Romero PV, Lucangelo U, Lopez Aguilar J, Fernandez R, Blanch L. Physiologically based indices of volumetric capnography in patients receiving mechanical ventilation. Eur Respir J. 1997;10:1309–15.

    Article  CAS  PubMed  Google Scholar 

  41. Kazemi M. New prognostic index to detect the severity of asthma automatically using signal processing techniques of capnogram. Dissertation. [Malaysia]: Universiti Teknologi. 2013.

  42. Pomares Betancourt J, Tangel ML, Yan F, Diaz MO, Portela Otaño AE, Dong F, Hirota K. Segmented wavelet decomposition for capnogram feature extraction in asthma classification. J Adv Comput Intell Intell Inf. 2014;18:480–8.

    Google Scholar 

  43. Brown RH, Brooker A, Wise RA, Reynolds C, Loccioni C, Russo A, Risby TH. Forced expiratory capnography and chronic obstructive pulmonary disease (COPD). J Breath Res. 2013;7:017108. doi:10.1088/1752-7155/7/1/017108.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bravi A, Longtin A, Seely AJ. Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online. 2011;10(10):90. doi:10.1186/1475-925X-10-90.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Alpaydin E. Introduction to machine learning. 3rd ed. Cambridge: MIT Press; 2014.

    Google Scholar 

  46. Flach P. Machine learning: the art and science of algorithms that make sense of data. Cambridge: Cambridge University Press; 2012.

    Book  Google Scholar 

  47. Ling CX, Huang J, Zhang H. AUC: a statistically consistent and more discriminating measure than accuracy. Proceedings of the 18th international joint conference on Artificial intelligence (IJCAI’03). San Francisco, CA: Morgan Kaufmann Publishers Inc.; 2003. p. 519–24.

    Google Scholar 

  48. 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. Anesth Analg. 2006;103:1196–204.

    Article  PubMed  Google Scholar 

  49. Rader CD, Crowe VM, Marcot BG. CAPS: a pattern recognition expert system prototype for respiratory and anesthesia monitoring. WESTEX-87. In: Proceedings of the western conference on expert systems, pp. 162–68. 1987.

  50. Smith TC, Green A, Hutton P. Recognition of cardiogenic artifact in pediatric capnograms. J Clin Monit. 1994;10:270–5.

    Article  CAS  PubMed  Google Scholar 

  51. Bleil M, Opp A, Linder R, Boye S, Gehring H, Hofmann U. Online-classification of capnographic curves using artificial neural networks. In: Vander Sloten J, Verdonck P, Nyssen M, Haueisen J, editors. 4th European conference of the international federation for medical and biological engineering [Internet]. Berlin: Springer. p. 1096–99. 2009. doi:10.1007/978-3-540-89208-3_262.

  52. Goldman JM, Dietrich BH. Neural network analysis of physiologic waveforms. Ann Int Conf IEEE Eng Med Biol Soc. 1991;13:1660–1.

    Google Scholar 

  53. Navabi-Shirazi MJ. Integration of operating room monitors for development of a smart alarm system. University of Arizona, PhD Dissertation—Electrical and Computer Engineering Department. 1990.

  54. Jiang A, King P, Smith B. Information interpretation in a real-time knowledge-based respiratory monitoring system. In: Computing and monitoring in anesthesia and intensive care, p 47–9. 1993.

  55. Orr JA, Westenskow DR. A breathing circuit alarm system based on neural networks. J Clin Monit. 1994;10:101–9.

    Article  CAS  PubMed  Google Scholar 

  56. Beatty, PCW, Pohlmann A, Dimarki T. Shape-only identification of breathing system failure engineering in medicine and biology society. In: Proceedings of the 22nd annual international conference of the IEEE 2, p. 982–4. 2000.

  57. Ahmad F, Stein N, Kondra S, Hofman U, Matz H, Gehring H. Identifying different patterns of capnographic curves on spontaneously breathing patients. Biomedizinische Technik. 2005;50 Suppl v1 Part 2:1541–42.

  58. Bleil M. Klassifikation von Kapnogrammen mit Künstlichen Neuronalen Netzen (Classification of the Capnogram with Artificial Neural Nets). Master of Computer Science, Fernuniversität Hagen (University of Hagen, Germany). 2008.

  59. Galgóczy G, Mága R, Mándi A. Differential capnographic diagnosis of various ventilation disorders. Pneumonologie. 1972;147:21–8.

    Article  PubMed  Google Scholar 

  60. Vulterini S, Bianco MR, Galmacci G, Pellicciotti L. The capnogram in the study of chronic obstructive lung disease. Ric Clin Lab. 1976;6:149–55.

    Article  CAS  PubMed  Google Scholar 

  61. Druck J, Rubio PM, Valley M, Jaffe MB, Yaron M. Evaluation of the slope of phase III from the volumetric capnogram as a non-effort dependent surrogate of peak expiratory flow rate in acute asthma exacerbation. Ann Emerg Med. 2007;50:130.

    Article  Google Scholar 

  62. Yaron M, Padyk P, Hutsinpiller M, Cairns CB. Utility of the expiratory capnogram in the assessment of bronchospasm. Ann Emerg Med. 1996;28:403–7.

    Article  CAS  PubMed  Google Scholar 

  63. Kazemi M, Malarvili MB. Investigation of capnogram signal characteristics using statistical methods. In: 2012 IEEE EMBS conference on biomedical engineering and sciences (IECBES), p. 343–8. 2012.

  64. Addison P. The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. 1st ed. Boca Raton: CRC Press; 2002.

    Book  Google Scholar 

  65. Brown LH, Gough JE, Seim RH. Can quantitative capnometry differentiate between cardiac and obstructive causes of respiratory distress? Chest. 1998;113:323–6.

    Article  CAS  PubMed  Google Scholar 

  66. Mieloszyk RJ, Verghese GC, Deitch K, Cooney B, Khalid A, Mirre-Gonzalez MA, Heldt T, Krauss BS. Automated quantitative analysis of capnogram shape for COPD-normal and COPD-CHF classification. IEEE Trans Biomed Eng. 2014;61:2882–90. doi:10.1109/TBME.2014.2332954.

    Article  PubMed  Google Scholar 

  67. Hamrick JL, Hamrick JT, Lee JK, Lee BH, Koehler RC, Shaffner DH. Efficacy of chest compressions directed by end-tidal CO2 feedback in a pediatric resuscitation model of basic life support. J Am Heart Assoc. 2014;14(3):e000450.

    Article  Google Scholar 

  68. Nuckton TJ, Alonso JA, Kallet RH, Daniel BM, Pittet J-F, Eisner MD, Matthay MA. Pulmonary dead-space fraction as a risk factor for death in the acute respiratory distress syndrome. N Engl J Med. 2002;346:1281–6.

    Article  PubMed  Google Scholar 

  69. Hubble CL, Gentile MA, Tripp DS, Craig DM, Meliones JN, Cheifetz IM. Deadspace to tidal volume ratio predicts successful extubation in infants and children. Crit Care Med. 2000;28:2034–40.

    Article  CAS  PubMed  Google Scholar 

  70. Akça O. Optimizing the intraoperative management of carbon dioxide concentration. Curr Opin Anaesthesiol. 2006;19:19–25.

    Article  PubMed  Google Scholar 

  71. Cherniack NS, Longobardo GS, Staw I, Heymann M. Dynamics of carbon dioxide stores changes following an alteration in ventilation. J Appl Physiol. 1966;21:785–93.

    CAS  PubMed  Google Scholar 

  72. Banner MJ. Partial pressure end-tidal carbon dioxide (PetCO2) monitoring for patients with acute respiratory distress syndrome: effects of physiologic deadspace volume. In: Gravenstein JS, et al., editors. Capnography: clinical aspects. Cambridge: Cambridge University Press; 2004. p. 213–22.

    Google Scholar 

  73. Engoren M, Plewa M, O’Hara D, Kline JA. Evaluation of capnography using a genetic algorithm to predict PaCO2. Chest. 2005;127:579–84.

    Article  PubMed  Google Scholar 

  74. Rayburn DB. Non-invasive estimation of arterial blood gases. United States Patent. 5,632,281. May 27, 1997.

  75. Chuang ML, Lin IF, Vintch JR, Tien EH. Using statistical techniques to predict dynamic arterial PCO2 in patients with COPD during maximum exercise. Respir Care. 2012;57:1106–14.

    Article  PubMed  Google Scholar 

  76. Smelt WL, de Lange JJ, Baerts WD, Booij LH. The capnograph, a reliable non-invasive monitor for the detection of pulmonary embolism of various origin. Acta Anaesthesiol Belg. 1987;38:217–24.

    CAS  PubMed  Google Scholar 

  77. Kline JA, Arunachlam M. Preliminary study of the capnogram waveform area to screen for pulmonary embolism. Ann Emerg Med. 1998;32:289–96.

    Article  CAS  PubMed  Google Scholar 

  78. Patel MM, Rayburn DB, Browning JA, Kline JA. Neural network analysis of the volumetric capnogram to detect pulmonary embolism. Chest. 1999;116:1325–32.

    Article  CAS  PubMed  Google Scholar 

  79. Kline JA, Kubin AK, Patel MM, Easton EJ, Seupal RA. Alveolar dead space as a predictor of severity of pulmonary embolism. Acad Emerg Med. 2000;7:611–7.

    Article  CAS  PubMed  Google Scholar 

  80. Kline JA, Meek S, Boudrow D, Warner D, Colucciello S. Use of the alveolar dead space fraction (Vd/Vt) and plasma D-dimers to exclude acute pulmonary embolism in ambulatory patients. Acad Emerg Med. 1997;4:856–63.

    Article  CAS  PubMed  Google Scholar 

  81. Eriksson L, Wollmer P, Olsson CG, Albrechtsson U, Larusdottir H, Nilsson R, Sjögren A, Jonson B. Diagnosis of pulmonary embolism based upon alveolar deadspace analysis. Chest. 1989;96:357–62.

    Article  CAS  PubMed  Google Scholar 

  82. Verschuren F, Sanchez O, Righini M, Heinonen E, Le Gal G, Meyer G, Perrier A, Thys F. Volumetric or time-based capnography for excluding pulmonary embolism in outpatients? J Thromb Haemost. 2010;8:60–7. doi:10.1111/j.1538-7836.2009.03667.

    Article  CAS  PubMed  Google Scholar 

  83. Rumpf TH, Krizmaric M, Grmec S. Capnometry in suspected pulmonary embolism with positive D-dimer in the field. Crit Care. 2009;13:R196.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Kline JA, Hogg MM, Courtney DM, Miller CD, Jones AE, Smithline HA, Klekowski N, Lanier R. D-dimer and exhaled CO2/O2 to detect segmental pulmonary embolism in moderate-risk patients. Am J Respir Crit Care Med. 2010;182:669–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Anderson JA. Embolism. In: Gravenstein JS, et al., editors. Capnography: clinical aspects. Cambridge: Cambridge University Press; 2004. p. 187–98.

    Google Scholar 

  86. Verschuren F, Perrier A. Splendors and miseries of expired CO2 measurement in the suspicion of pulmonary embolism. Crit Care. 2010;14:110. doi:10.1186/cc8838.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Manara A, D’hoore W, Thys F. Capnography as a diagnostic tool for pulmonary embolism: a meta-analysis. Ann Emerg Med. 2013;62:584–91. doi:10.1016/j.

    Article  PubMed  Google Scholar 

  88. Affeldt JE, Austin E, Bower AG, Crane MG. Alveolar carbon dioxide levels in acute poliomyelitis. J Appl Physiol. 1956;9:11–8.

    CAS  PubMed  Google Scholar 

  89. Kalenda Z. The capnogram as a guide to the efficacy of cardiac massage. Resuscitation. 1978;6:259–63.

    Article  CAS  PubMed  Google Scholar 

  90. White RD, Goodman BW, Svoboda MA. Neurologic recovery following prolonged out-of-hospital cardiac arrest with resuscitation guided by continuous capnography. Mayo Clin Proc. 2011;86:544–8.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Touma O, Davies M. The prognostic value of end tidal carbon dioxide during cardiac arrest: a systematic review. Resuscitation. 2013;84:1470–9.

    Article  PubMed  Google Scholar 

  92. Einav S, Bromiker R, Weiniger CF, Matot I. Mathematical modeling for prediction of survival from resuscitation based on computerized continuous capnography: proof of concept. Acad Emerg Med. 2011;18:468–75.

    Article  PubMed  Google Scholar 

  93. Johnson G, Silver A, Freeman GA. Defibrillator display including CPR depth information. United States Patent 8,725,253, May 13, 2014.

  94. Joo TH, Stickney RE, Jayne CP, Lank P, O’Hearn P, Hampton DR, Taylor JW, Crone WE, Yerkovich D. Pulse detection apparatus, software, and methods using patient physiological signals. United States Patent 7,917,209, March 29, 2011.

  95. Krizmaric M, Verlic M, Stiglic G, Grmec S, Kokol P. Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest. Comput Methods Programs Biomed. 2009;95:S22–32.

    Article  PubMed  Google Scholar 

  96. Shandilya S, Ward K, Kurz M, Najarian K. Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning. BMC Med Inform Decis Mak. 2012;15(12):116.

    Article  Google Scholar 

  97. Sheak K, Wiebe DJ, Babaeizadeh S, Yuen TC, Zive D, Owens PC, Edelson DP, Daya M, Idris AH, Abella BS, Leary M. Increasing compression rate and depth positively correlate with end-tidal carbon dioxide during actual CPR performance circulation. (Resuscitation Science Symposium) 130(suppl 2):A15. 2014.

  98. Babaeizadeh S, Helfenbein E, Zhou SH. Real-time airway check status indicator. United States Patent Application 20130324872. December 5, 2013.

  99. Gonzalez-Castro A, Suarez-Lopez V, Gomez-Marcos V, Gonzalez-Fernandez C, Iglesias-Posadilla D, Buron-Mediavilla J, Rodriguez-Borregan JC, Minambres E, Llorca J. Utility of the dead space fraction (Vd/Vt) as a predictor of extubation success Med. Intensiva. 2011;35:529–38.

    Article  CAS  Google Scholar 

  100. Rasera CC, Gewehr PM, Domingues AMT. PETCO2 measurement and feature extraction of capnogram signals for extubation outcomes from mechanical ventilation. Physiol Meas. 2015;36:231–42.

    Article  PubMed  Google Scholar 

  101. Kartal M, Eray O, Rinnert S, Goksu E, Bektas F, Eken C. ETCO2: a predictive tool for excluding metabolic disturbances in nonintubated patients. Am J Emerg Med. 2011;29:65–9.

    Article  PubMed  Google Scholar 

  102. Gravenstein JS, Jaffe MB, Paulus DA, editors. Capnography: clinical aspects. Cambridge: Cambridge University Press; 2004.

    Google Scholar 

  103. Taft A, Ronen M, Epps C, Waugh J, Wales R. A Novel Integrated Pulmonary Index (IPI) quantifies heart rate, Etco2, respiratory rate and SpO2% annual meeting of the American Society of Anesthesiologists, 2008.

  104. Berkenstadt H, Ben-Menachem E, Herman A, Dach R. An evaluation of the Integrated Pulmonary Index (IPI) for the detection of respiratory events in sedated patients undergoing colonoscopy. J Clin Monit Comput. 2012;26:177–81.

    Article  PubMed  Google Scholar 

  105. Banner MJ, Euliano NR, Macintyre NR, Layon AJ, Bonett S, Gentile MA, Bshouty Z, Peters C, Gabrielli A. Ventilator advisory system employing load and tolerance strategy recommends appropriate pressure support ventilation settings: multisite validation study. Chest. 2008;133:697–703.

    Article  PubMed  Google Scholar 

  106. Rose L, Schultz MJ, Cardwell CR, Jouvet P, McAuley DF, Blackwood B. Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children. Cochrane Database Syst Rev. 2014; 10(6): CD009235.

  107. Dojat M, Harf A, Touchard D, Lemaire F, Brochard L. Clinical evaluation of a computer-controlled pressure support mode. Am J Respir Crit Care Med. 2000;161(4 Pt 1):1161–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Lellouche F, Bouchard PA, Simard S, L’Her E, Wysocki M. Evaluation of fully automated ventilation: a randomized controlled study in post-cardiac surgery patients. Intensive Care Med. 2013;39:463–71. doi:10.1007/s00134-012-2799-2.

    Article  PubMed  Google Scholar 

  109. Pambianco DJ, Vargo JJ, Pruitt RE, Hardi R, Martin JF. Computer-assisted personalized sedation for upper endoscopy and colonoscopy: a comparative, multicenter randomized study. Gastrointest Endosc. 2011;73:765–72.

    Article  PubMed  Google Scholar 

  110. Bargeton D. Analysis of capnigram and oxygram in man. Bull Physio-pathol Respir. 1967;3:503–26.

    Google Scholar 

  111. Den Buijs JO, Warner L, Chbat NW, Roy TK. Bayesian tracking of a nonlinear model of the capnogram. In: 28th annual international conference of the IEEE engineering in medicine and biology society. EMBS’06, p. 2871–4. 2006.

  112. Chbat N, Lord W. Integration of physiological models in medical decision support systems. United States Patent 8,521,556. August 27, 2013.

  113. Karlen W, Turner M, Cooke E, Dumont GA, Ansermino JM. CapnoBase: signal database and tools to collect, share and annotate respiratory signals. In: Annual meeting of the society for technology in anesthesia, p. 25. 2010.

  114. Orr J, Long C, Brewer LA. CO2 waveform generator use in evaluating capnometer performance using previously recorded clinical data. In: Annual meeting of the society for technology in anesthesia, 2012.

  115. Brochard L, Martin GS, Blanch L, Pelosi P, Belda FJ, Jubran A, Gattinoni L, Mancebo J, Ranieri VM, Richard JC, Gommers D, Vieillard-Baron A, Pesenti A, Jaber S, Stenqvist O, Vincent JL. Clinical review: respiratory monitoring in the ICU—a consensus of 16. Crit Care. 2012;12(16):219. doi:10.1186/cc11146.

    Article  Google Scholar 

  116. Sinha P, Flower O, Soni N. Deadspace ventilation: a waste of breath. In: Pinsky MR, Brochard L, Mancebo J, Antonelli M, editors. Applied physiology in intensive care medicine 2: physiological reviews and editorials, 3rd edn. Springer, Berlin, p. 303–14.

  117. Suarez-Sipmann F, Bohm SH, Tusman G. Volumetric capnography: the time has come. Curr Opin Crit Care. 2014;20:333–9.

    Article  PubMed  Google Scholar 

  118. Tusman G, Sipmann FS, Borges JB, Hedenstierna G, Bohm SH. Validation of Bohr dead space measured by volumetric capnography. Intensive Care Med. 2011;37(5):870–4. doi:10.1007/s00134-011-2164-x.

    Article  PubMed  Google Scholar 

  119. Jaffe MB. A computer program for the interpretation of exercise tolerance tests. Comput Methods Programs Biomed. 1986;23:133–43.

    Article  CAS  PubMed  Google Scholar 

  120. Ronen M, Davidpur K, Einav S. Weaning from ventilation using capnography. United States Patent 8,695,596, April 15, 2014.

  121. Krauss BS, Hampton DR, Carlebach E. Automated interpretive medical care system and methodology. United States Patent 8,679,029, March 25, 2014.

  122. HeartStart MRx with Airway Confirmation Assist Defibrillator/Monitor, Philips Medical Systems, FDA 510(k) Summary (K130153), cleared 3/12/13. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm. Accessed 25 July 2015.

  123. Lukic KZ, Urch B, Fila M, Faughnan ME, Silverman F. A novel application of capnography during controlled human exposure to air pollution. Biomed Eng Online. 2006;18(5):54.

    Article  Google Scholar 

  124. Mayer A, Zholkover A, Keidan I. A functional test for the detection of infusion lines extravasation. In: Conference proceedings on IEEE engineering in medicine and biology society, p. 4180–3. 2014.

  125. Pinskiy V, Mori N, Harsh Shah, Dudhat P, Atlas G. Capnography-guided intubation bioengineering conference. In: Proceedings of the IEEE 32nd annual northeast, p. 101–2. 2006.

  126. Böhm SH, Maisch S, von Sandersleben A, Thamm O, Passoni I, Martinez Arca J, Tusman G. The effects of lung recruitment on the Phase III slope of volumetric capnography in morbidly obese patients. Anesth Analg. 2009;109:151–9. doi:10.1213/ane.0b013e31819bcbb5.

    Article  PubMed  Google Scholar 

  127. Dziewas R, Hopmann B, Humpert M, Böntert M, Dittrich R, Lüdemann P, Young P, Ringelstein EB, Nabavi DG. Capnography screening for sleep apnea in patients with acute stroke. Neurol Res. 2005;27:83–7.

    Article  PubMed  Google Scholar 

  128. Cundrle I Jr, Somers VK, Johnson BD, Scott CG, Olson LJ. Exercise end-tidal CO2 predicts central sleep apnea in patients with heart failure. Chest. 2015;147:1566–73.

    Article  PubMed  PubMed Central  Google Scholar 

  129. Arena R, Guazzi M, Myers J, Chase P, Bensimhon D, Cahalin LP, Peberdy MA, Ashley E, West E, Forman DE. Prognostic value of capnography during rest and exercise in patients with heart failure. Congest Heart Fail. 2012;18:302–7.

    Article  PubMed  Google Scholar 

  130. Mieloszyk RJ, Guo MG, Verghese GC, Andolfatto G, Heldt T, Krauss BS. Clustering of capnogram features to track state transitions during procedural sedation. In: Engineering in medicine and biology society (EMBC), 37th annual international conference of the IEEE, pp. 1699–702. 2015.

  131. Hoare SW, Beatty PC. Automatic artifact identification in anaesthesia patient record keeping: a comparison of techniques. Med Eng Phys. 2000;22:547–53.

    Article  CAS  PubMed  Google Scholar 

  132. Romero PV, Rodriguez B, de Oliveira D, Blanch L, Manresa F. Volumetric capnography and chronic obstructive pulmonary disease staging. Int J Chron Obstruct Pulmon Dis. 2007;2:381–91.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgments

I would like to thank the following researchers and clinicians for their feedback during the preparation of this paper: John Anderson, Wanqun Bao, Neil Euliano, Julian Goldman, Nik Gravenstein, Mohsen Kazemi, Becky Mieloszyk, Joseph Orr, Adam Seiver, Franck Verschuren, and Kevin Ward.

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  1. Cardiorespiratory Consulting, LLC, 410 Mountain Road, Cheshire, CT, 06410, USA

    Michael B. Jaffe

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Correspondence to Michael B. Jaffe.

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The author worked previously for Philips Healthcare and currently works as a consultant as Cardiorespiratory Consulting, LLC.

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Jaffe, M.B. Using the features of the time and volumetric capnogram for classification and prediction. J Clin Monit Comput 31, 19–41 (2017). https://doi.org/10.1007/s10877-016-9830-z

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