Conference proceeding
Monitoring Lung Mechanics during Mechanical Ventilation using Machine Learning Algorithms
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vol.2018, pp.1160-1163
07/2018
Handle:
https://hdl.handle.net/2376/104235
PMID: 30440597
Abstract
Evaluation of lung mechanics is the primary component for designing lung protective optimal ventilation strategies. This paper presents a machine learning approach for bedside assessment of respiratory resistance (R) and compliance (C). We develop machine learning algorithms to track flow rate and airway pressure and estimate R and C continuously and in real-time. An experimental study is conducted, by connecting a pressure control ventilator to a test lung that simulates various R and C values, to gather sensor data for validation of the devised algorithms. We develop supervised learning algorithms based on decision tree, decision table, and Support Vector Machine (SVM) techniques to predict R and C values. Our experimental results demonstrate that the proposed algorithms achieve 90.3%, 93.1%, and 63.9% accuracy in assessing respiratory R and C using decision table, decision tree, and SVM, respectively. These results along with our ability to estimate R and C with 99.4% accuracy using a linear regression model demonstrate the potential of the proposed approach for constructing a new generation of ventilation technologies that leverage novel computational models to control their underlying parameters for personalized healthcare and context-aware interventions.
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Details
- Title
- Monitoring Lung Mechanics during Mechanical Ventilation using Machine Learning Algorithms
- Creators
- Niloofar Hezarjaribi - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USARabijit Dutta - Dept. of Mech. Eng., Univ. of Idaho, Moscow, ID, USATao Tao Xing - Dept. of Mech. Eng., Univ. of Idaho, Moscow, ID, USAGordon K Murdoch - Dept. of Animal & Veterinary Sci., Univ. of Idaho, Moscow, ID, USASepideh Mazrouee - Dept. of Electr. & Comput. Eng., Univ. of California San Diego, La Jolla, CA, USABobak J Mortazavi - Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USAHassan Ghasemzadeh - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
- Publication Details
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vol.2018, pp.1160-1163
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- IEEE
- Identifiers
- 99900546570201842
- Language
- English
- Resource Type
- Conference proceeding