Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

Sara Bufo; Ezio Bartocci; Guido Sanguinetti; Massimo Borelli; Umberto Lucangelo; Luca Bortolussi

doi:10.1007/978-3-662-45231-8_30

Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

Sara Bufo, Ezio Bartocci, Guido Sanguinetti, Massimo Borelli, Umberto Lucangelo, Luca Bortolussi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications
Subtitle of host publication	6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II
Editors	Tiziana Margaria, Bernhard Steffen
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	391-403
Number of pages	13
ISBN (Electronic)	978-3-662-45231-8
ISBN (Print)	978-3-662-45230-1
DOIs	https://doi.org/10.1007/978-3-662-45231-8_30
Publication status	Published - 2014

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	8803
ISSN (Print)	0302-9743

Access to Document

10.1007/978-3-662-45231-8_30

afc91f167f0e4466dad1c3d231e281c0f8a8Accepted author manuscript, 408 KB

http://link.springer.com/chapter/10.1007%2F978-3-662-45231-8_30

QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)
Hillston, J. (Principal Investigator) & Gilmore, S. (Co-investigator)
EU government bodies
1/04/13 → 31/03/17
Project: Research
MLCS - Machine learning for computational science statistical and formal modeling of biological systems
Sanguinetti, G. (Principal Investigator)
EU government bodies
1/10/12 → 30/09/17
Project: Research

Cite this

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 T. Margaria, & B. Steffen (Eds.), Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications: 6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II (pp. 391-403). (Lecture Notes in Computer Science; Vol. 8803). Springer. https://doi.org/10.1007/978-3-662-45231-8_30

Bufo, Sara ; Bartocci, Ezio ; Sanguinetti, Guido et al. / Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients. Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications: 6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II. editor / Tiziana Margaria ; Bernhard Steffen. Berlin, Heidelberg : Springer, 2014. pp. 391-403 (Lecture Notes in Computer Science).

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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.",

author = "Sara Bufo and Ezio Bartocci and Guido Sanguinetti and Massimo Borelli and Umberto Lucangelo and Luca Bortolussi",

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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 T Margaria & B Steffen (eds), Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications: 6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II. Lecture Notes in Computer Science, vol. 8803, Springer, Berlin, Heidelberg, pp. 391-403. https://doi.org/10.1007/978-3-662-45231-8_30

Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients. / Bufo, Sara; Bartocci, Ezio; Sanguinetti, Guido et al.
Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications: 6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II. ed. / Tiziana Margaria; Bernhard Steffen. Berlin, Heidelberg: Springer, 2014. p. 391-403 (Lecture Notes in Computer Science; Vol. 8803).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

AU - Bufo, Sara

AU - Bartocci, Ezio

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AU - Borelli, Massimo

AU - Lucangelo, Umberto

AU - Bortolussi, Luca

PY - 2014

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N2 - 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.

AB - 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.

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DO - 10.1007/978-3-662-45231-8_30

M3 - Conference contribution

SN - 978-3-662-45230-1

T3 - Lecture Notes in Computer Science

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Bufo S, Bartocci E, Sanguinetti G, Borelli M, Lucangelo U, Bortolussi L. Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients. In Margaria T, Steffen B, editors, Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications: 6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II. Berlin, Heidelberg: Springer. 2014. p. 391-403. (Lecture Notes in Computer Science). doi: 10.1007/978-3-662-45231-8_30

Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

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Projects

QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)

MLCS - Machine learning for computational science statistical and formal modeling of biological systems

Cite this