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Automatic Selector Between Cardiogenic Oscillation and Airway Secretion in Mechanical Ventilation Flow Signals Using Artificial Immune System

Automatic Selector Between Cardiogenic Oscillation and Airway Secretion in Mechanical Ventilation Flow Signals Using Artificial Immune System

Samuel Sobral dos Santos, Hatus Vianna Wanderley, Fernando Buarque de Lima Neto
Copyright: © 2018 |Volume: 9 |Issue: 4 |Pages: 14
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781522544876|DOI: 10.4018/IJSIR.2018100104
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MLA

Sobral dos Santos, Samuel, et al. "Automatic Selector Between Cardiogenic Oscillation and Airway Secretion in Mechanical Ventilation Flow Signals Using Artificial Immune System." IJSIR vol.9, no.4 2018: pp.65-78. http://doi.org/10.4018/IJSIR.2018100104

APA

Sobral dos Santos, S., Wanderley, H. V., & Neto, F. B. (2018). Automatic Selector Between Cardiogenic Oscillation and Airway Secretion in Mechanical Ventilation Flow Signals Using Artificial Immune System. International Journal of Swarm Intelligence Research (IJSIR), 9(4), 65-78. http://doi.org/10.4018/IJSIR.2018100104

Chicago

Sobral dos Santos, Samuel, Hatus Vianna Wanderley, and Fernando Buarque de Lima Neto. "Automatic Selector Between Cardiogenic Oscillation and Airway Secretion in Mechanical Ventilation Flow Signals Using Artificial Immune System," International Journal of Swarm Intelligence Research (IJSIR) 9, no.4: 65-78. http://doi.org/10.4018/IJSIR.2018100104

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Abstract

The accumulation of secretions in the airways of ventilator-dependent patients is a common problem, and if not detected and treated in due time, it greatly increases the risk of infections and asynchrony. Unfortunately, cardiogenic oscillation modifies the flow signal shape that can confuse clinical staff and modern lung ventilators. In this article, the authors use an artificial immune system algorithm in a pre-processed flow signal. The authors' approach was able to automatically detect the presence or absence of airway secretions, even if the sample contains the influence of cardiogenic oscillation. The training and validation of the algorithm was carried out using a database containing flow signals of 457 respiratory cycles, obtained from three patients in different ventilation modes. The algorithm trained with 60% of the base cycles, was able to achieve specificity and sensitivity above 0.96 in the classification of the remaining cycles of the base.

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