Advertisement

Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

  • Sara Bufo
  • Ezio Bartocci
  • Guido Sanguinetti
  • Massimo Borelli
  • Umberto Lucangelo
  • Luca Bortolussi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8803)

Abstract

We introduce a novel approach to automatically detect ineffective breathing efforts in patients in intensive care subject to assisted ventilation. The method is based on synthesising from data temporal logic formulae which are able to discriminate between normal and ineffective breaths. The learning procedure consists in first constructing statistical models of normal and abnormal breath signals, and then in looking for an optimally discriminating formula. The space of formula structures, and the space of parameters of each formula, are searched with an evolutionary algorithm and with a Bayesian optimisation scheme, respectively. We present here our preliminary results and we discuss our future research directions.

Keywords

Temporal Logic Assist Ventilation Inspiratory Effort Statistical Model Check Bayesian Optimisation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alur, R., Feder, T., Henzinger, T.A.: The benefits of relaxing punctuality. J. ACM 43(1), 116–146 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Asarin, E., Donzé, A., Maler, O., Nickovic, D.: Parametric Identification of Temporal Properties. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 147–160. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Bartocci, E., Bortolussi, L., Nenzi, L., Sanguinetti, G.: On the robustness of temporal properties for stochastic models. In: Proc. of HSB 2013, pp. 3–19 (2013)Google Scholar
  4. 4.
    Bartocci, E., Bortolussi, L., Sanguinetti, G.: Learning temporal logical properties discriminating ECG models of cardiac arrhytmias. CoRR abs/1312.7523 (2013)Google Scholar
  5. 5.
    Bartocci, E., Bortolussi, L., Sanguinetti, G.: Data-driven statistical learning of temporal logic properties. In: Legay, A., Bozga, M. (eds.) FORMATS 2014. LNCS, vol. 8711, pp. 23–37. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Bartocci, E., Grosu, R., Karmarkar, A., Smolka, S.A., Stoller, S.D., Zadok, E., Seyster, J.: Adaptive runtime verification. In: Qadeer, S., Tasiran, S. (eds.) RV 2012. LNCS, vol. 7687, pp. 168–182. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
  8. 8.
    Blanch, L., Sales, B., Montanya, J., Lucangelo, U., Garcia-Esquirol, O., Villagra, A., Chacon, E., Estruga, A., Borelli, M., Burgueño, M., Oliva, J., Fernandez, R., Villar, J., Kacmarek, R., Murias, G.: Validation of the better care system to detect ineffective efforts during expiration in mechanically ventilated patients: A pilot study. Intensive Care Med. (in press)Google Scholar
  9. 9.
    Bortolussi, L., Sanguinetti, G.: Learning and Designing Stochastic Processes from Logical Constraints. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds.) QEST 2013. LNCS, vol. 8054, pp. 89–105. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Branson, R.: Patient-ventilator interaction: The last 40 years. Respir. Care 56(1), 15–24 (2011)CrossRefGoogle Scholar
  11. 11.
    Bujorianu, M.L., Lygeros, J.: General stochastic hybrid systems. In: IEEE Mediterranean Conference on Control and Automation MED, vol. 4, pp. 1872–1877 (2004)Google Scholar
  12. 12.
    Calzone, L., Chabrier-Rivier, N., Fages, F., Soliman, S.: Machine learning biochemical networks from temporal logic properties. In: Priami, C., Plotkin, G. (eds.) Trans. on Comput. Syst. Biol. VI. LNCS (LNBI), vol. 4220, pp. 68–94. Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Chen, C., Lin, W., Hsu, C., Cheng, K., Lo, C.: Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: Feasibility of using a computer algorithm. Crit. Care Med. 36(2), 455–461 (2008)CrossRefGoogle Scholar
  14. 14.
    Clarke, E., Donzé, A., Legay, A.: On simulation-based probabilistic model checking of mixed-analog circuits. Formal Methods in System Design 36(2), 97–113 (2010)CrossRefzbMATHGoogle Scholar
  15. 15.
    Cuvelier, A., Achour, L., Rabarimanantsoa, H., Letellier, C., Muir, J., Fauroux, B.: A noninvasive method to identify ineffective triggering in patients with noninvasive pressure support ventilation. Respiration 80(3), 198–206 (2010)CrossRefGoogle Scholar
  16. 16.
    Davis, M.: Markov Models and Optimization. Chapman & Hall (1993)Google Scholar
  17. 17.
    Donzé, A., Maler, O., Bartocci, E., Nickovic, D., Grosu, R., Smolka, S.: On temporal logic and signal processing. In: Chakraborty, S., Mukund, M. (eds.) ATVA 2012. LNCS, vol. 7561, pp. 92–106. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Georgoulas, A., Clark, A., Ocone, A., Gilmore, S., Sanguinetti, G.: A subsystems approach for parameter estimation of ode models of hybrid systems. In: Proc. of HSB 2012. EPTCS, vol. 92 (2012)Google Scholar
  19. 19.
    Grosu, R., Smolka, S.A., Corradini, F., Wasilewska, A., Entcheva, E., Bartocci, E.: Learning and detecting emergent behavior in networks of cardiac myocytes. Commun. ACM 52(3), 97–105 (2009)CrossRefGoogle Scholar
  20. 20.
    Hoos, H.H., Stützle, T.: Stochastic local search: Foundations & applications. Elsevier (2004)Google Scholar
  21. 21.
    Kalajdzic, K., Bartocci, E., Smolka, S.A., Stoller, S.D., Grosu, R.: Runtime Verification with Particle Filtering. In: Legay, A., Bensalem, S. (eds.) RV 2013. LNCS, vol. 8174, pp. 149–166. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Kondili, E., Akoumianaki, E., Alexopoulou, C., Georgopoulos, D.: Identifying and relieving asynchrony during mechanical ventilation. Expert Rev. Respir. Med. 3(3), 231–243 (2009)CrossRefGoogle Scholar
  23. 23.
    Kondili, E., Prinianakis, G., Georgopoulos, D.: Patient-ventilator interaction. Br. J. Anaesth. 91(1), 106–119 (2003)CrossRefGoogle Scholar
  24. 24.
    Kong, Z., Jones, A., Ayala, A.M., Gol, E.A., Belta, C.: Temporal Logic Inference for Classification and Prediction from Data. In: Proc. of HSCC 2014 (2014)Google Scholar
  25. 25.
    Koymans, R.: Specifying real-time properties with metric temporal logic. Real-Time Syst. 2, 255–299 (1990)CrossRefGoogle Scholar
  26. 26.
    Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Lakhnech, Y., Yovine, S. (eds.) FORMATS/FTRTFT 2004. LNCS, vol. 3253, pp. 152–166. Springer, Heidelberg (2004)Google Scholar
  27. 27.
    Mellott, K., Grap, M., Munro, C., Sessler, C., Wetzel, P., Nilsestuen, J., Ketchum, J.: Patient ventilator asynchrony in critically ill adults: Frequency and types. Heart Lung 43(3), 231–243 (2014)CrossRefGoogle Scholar
  28. 28.
    Mulqueeny, Q., Ceriana, P., Carlucci, A., Fanfulla, F., Delmastro, M., Nava, S.: Automatic detection of ineffective triggering and double triggering during mechanical ventilation. Intensive Care Med. 33(11), 2014–2018 (2007)CrossRefGoogle Scholar
  29. 29.
    Mulqueeny, Q., Redmond, S., Tassaux, D., Vignaux, L., Jolliet, P., Ceriana, P., Nava, S., Schindhelm, K., Lovell, N.: Automated detection of asynchrony in patient-ventilator interaction. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 5324–5327 (2009)Google Scholar
  30. 30.
    Sassoon, C., Foster, G.: Patient-ventilator asynchrony. Curr. Opin. Crit. Care 7(1), 28–33 (2001)CrossRefGoogle Scholar
  31. 31.
    Sinderby, C., Liu, S., Colombo, D., Camarotta, G., Slutsky, A., Navalesi, P., Beck, J.: An automated and standardized neural index to quantify patient-ventilator interaction. Critical Care 17, 239 (2013)CrossRefGoogle Scholar
  32. 32.
    Sinderby, C., Navalesi, P., Beck, J., Skrobik, Y., Comtois, N., Friberg, S., Gottfried, S.B., Lindström, L.: Neural control of mechanical ventilation in respiratory failure. Nat. Med. 5(12), 1433–1436 (1999)CrossRefGoogle Scholar
  33. 33.
    Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Information-theoretic regret bounds for gaussian process optimization in the bandit setting. IEEE Transactions on Information Theory 58(5), 3250–3265 (2012)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Stoller, S.D., Bartocci, E., Seyster, J., Grosu, R., Havelund, K., Smolka, S.A., Zadok, E.: Runtime Verification with State Estimation. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 193–207. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  35. 35.
    Thille, A., Rodriguez, P., Cabello, B., Lellouche, F., Brochard, L.: Patient-ventilator asynchrony during assisted mechanical ventilation. Intensive Care Med. 32(10), 1515–1522 (2006)CrossRefGoogle Scholar
  36. 36.
    Tobin, M.J., Jubran, A., Laghi, F.: Patient-ventilator interaction. Am. J. Respir. Crit. Care Med. 163(5), 1059–1063 (2001)CrossRefGoogle Scholar
  37. 37.
    Vignaux, L., Vargas, F., Roeseler, J., Tassaux, D., Thille, A., Kossowsky, M.P., Brochard, L., Jolliet, P.: Patient-ventilator asynchrony during non-invasive ventilation for acute respiratory failure: A multicenter study. Intensive Care Med. 35(5), 840–846 (2009)CrossRefGoogle Scholar
  38. 38.
    de Wit, M., Miller, K., Green, D., Ostman, H., Gennings, C., Epstein, S.: Ineffective triggering predicts increased duration of mechanical ventilation. Crit. Care Med. 37(10), 2740–2745 (2009)CrossRefGoogle Scholar
  39. 39.
    Wrigge, H., Reske, A.: Patient-ventilator asynchrony: Adapt the ventilator, not the patient! Crit. Care Med. 41(9), 2240–2241 (2013)CrossRefGoogle Scholar
  40. 40.
    Xiaoqing, J., Donzé, A., Deshmukh, J.V., Seshia, S.A.: Mining Requirements from Closed-loop Control Models. In: Proc. of HSCC 2013, pp. 43–52. ACM (2013)Google Scholar
  41. 41.
    Yang, H., Hoxha, B., Fainekos, G.: Querying Parametric Temporal Logic Properties on Embedded Systems. In: Nielsen, B., Weise, C. (eds.) ICTSS 2012. LNCS, vol. 7641, pp. 136–151. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  42. 42.
    Younes, H.L.S., Kwiatkowska, M., Norman, G., Parker, D.: Numerical vs. statistical probabilistic model checking: An empirical study. In: Jensen, K., Podelski, A. (eds.) TACAS 2004. LNCS, vol. 2988, pp. 46–60. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sara Bufo
    • 1
  • Ezio Bartocci
    • 2
  • Guido Sanguinetti
    • 3
    • 4
  • Massimo Borelli
    • 1
  • Umberto Lucangelo
    • 5
  • Luca Bortolussi
    • 1
    • 6
    • 7
  1. 1.Department of Mathematics and GeosciencesUniversity of TriesteItaly
  2. 2.Faculty of InformaticsVienna University of TechnologyAustria
  3. 3.School of InformaticsUniversity of EdinburghUK
  4. 4.SynthSys, Centre for Synthetic and Systems BiologyUniversity of EdinburghUK
  5. 5.Department of MedicineUniversity of TriesteItaly
  6. 6.Computer Science DepartmentSaarland UniversitySaarbrückenGermany
  7. 7.CNR/ISTIPisaItaly

Personalised recommendations