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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alur, R., Feder, T., Henzinger, T.A.: The benefits of relaxing punctuality. J. ACM 43(1), 116–146 (1996)
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)
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)
Bartocci, E., Bortolussi, L., Sanguinetti, G.: Learning temporal logical properties discriminating ECG models of cardiac arrhytmias. CoRR abs/1312.7523 (2013)
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)
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)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
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)
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)
Branson, R.: Patient-ventilator interaction: The last 40 years. Respir. Care 56(1), 15–24 (2011)
Bujorianu, M.L., Lygeros, J.: General stochastic hybrid systems. In: IEEE Mediterranean Conference on Control and Automation MED, vol. 4, pp. 1872–1877 (2004)
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)
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)
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)
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)
Davis, M.: Markov Models and Optimization. Chapman & Hall (1993)
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)
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)
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)
Hoos, H.H., Stützle, T.: Stochastic local search: Foundations & applications. Elsevier (2004)
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)
Kondili, E., Akoumianaki, E., Alexopoulou, C., Georgopoulos, D.: Identifying and relieving asynchrony during mechanical ventilation. Expert Rev. Respir. Med. 3(3), 231–243 (2009)
Kondili, E., Prinianakis, G., Georgopoulos, D.: Patient-ventilator interaction. Br. J. Anaesth. 91(1), 106–119 (2003)
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)
Koymans, R.: Specifying real-time properties with metric temporal logic. Real-Time Syst. 2, 255–299 (1990)
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)
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)
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)
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)
Sassoon, C., Foster, G.: Patient-ventilator asynchrony. Curr. Opin. Crit. Care 7(1), 28–33 (2001)
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)
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)
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)
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)
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)
Tobin, M.J., Jubran, A., Laghi, F.: Patient-ventilator interaction. Am. J. Respir. Crit. Care Med. 163(5), 1059–1063 (2001)
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)
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)
Wrigge, H., Reske, A.: Patient-ventilator asynchrony: Adapt the ventilator, not the patient! Crit. Care Med. 41(9), 2240–2241 (2013)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bufo, S., Bartocci, E., Sanguinetti, G., Borelli, M., Lucangelo, U., Bortolussi, L. (2014). Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications. ISoLA 2014. Lecture Notes in Computer Science, vol 8803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45231-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-662-45231-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45230-1
Online ISBN: 978-3-662-45231-8
eBook Packages: Computer ScienceComputer Science (R0)