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Research Papers

Engineering Evaluation of the Performance of an Automatic Peripheral Oxygen Controller Using a Neonatal Respiratory Model

[+] Author and Article Information
Akram Faqeeh

Mechanical and Aerospace Engineering,
University of Missouri,
E2412 Lafferre Hall,
Columbia, MO 65211
e-mail: aaf346@mail.missouri.edu

Roger Fales

Mechanical and Aerospace Engineering,
University of Missouri,
E2412 Lafferre Hall,
Columbia, MO 65211
e-mail: falesr@missouri.edu

John Pardalos

Neonatology,
University of Missouri Health,
400 N. Keene Street,
Columbia, MO 65212
e-mail: pardalosj@health.missouri.edu

Ramak Amjad

Neonatology,
University of Missouri Health,
400 N. Keene Street,
Columbia, MO 65212
e-mail: amjadr@health.missouri.edu

Isabella Zaniletti

Statistics,
University of Missouri,
146 Middlebush Hall,
Columbia, MO 65211
e-mail: zanilettii@missouri.edu

Xuefeng Hou

Mechanical and Aerospace Engineering,
University of Missouri,
E2412 Lafferre Hall,
Columbia, MO 65211
e-mail: xhtf3@mail.missouri.edu

1Corresponding author.

Manuscript received December 20, 2017; final manuscript received April 9, 2018; published online July 13, 2018. Assoc. Editor: Venketesh Dubey.

J. Med. Devices 12(3), 031005 (Jul 13, 2018) (13 pages) Paper No: MED-17-1382; doi: 10.1115/1.4040188 History: Received December 20, 2017; Revised April 09, 2018

Premature infants often require respiratory support with a varying concentration of the fraction of inspired oxygen FiO2 to keep the arterial oxygen saturation typically measured using a peripheral sensor (SpO2) within the desired range to avoid both hypoxia and hyperoxia. The widespread practice for controlling the fraction of inspired oxygen is by manual adjustment. Automatic control of the oxygen to assist care providers is desired. A novel closed-loop respiratory support device with dynamic adaptability is evaluated nonclinically by using a neonatal respiratory response model. The device demonstrated the ability to improve oxygen saturation control over manual control by increasing the proportion of time where SpO2 is within the desired range while minimizing the episodes and periods where SpO2 of the neonatal respiratory model is out of the target range.

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Figures

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Fig. 3

The developed manual algorithm for hard-ware-in-the-loop test

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Fig. 2

Diagram of the respiratory control device

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Fig. 1

Block diagram of the neonatal respiratory model

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Fig. 4

The schematic of the experimental setup of the non-clinical test

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Fig. 14

Estimated disturbance versus applied disturbance while using P-controller with estimation system

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Fig. 15

Estimated disturbance versus applied disturbance while using PI-controller with estimation system

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Fig. 5

The gains frequency of fitted transfer function

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Fig. 6

The time constants frequency of fitted transfer function

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Fig. 7

The sets of disturbances (levels: I, II, and III)

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Fig. 20

Desired versus observed encoder position during the use P-controller with estimation system

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Fig. 21

Desired versus observed encoder position during the use of PI-controller with estimation system

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Fig. 12

Estimated versus observed SpO2 for P-controller with estimation system

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Fig. 13

Estimated versus observed SpO2 for PI-controller with estimation system

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Fig. 16

The estimated gain during the use of P-controller with estimation system

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Fig. 17

The estimated gain during the use of PI-controller with estimation system

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Fig. 18

The estimated time constant during the use of P-controller with estimation system

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Fig. 19

The estimated time constant during the use of PI-controller with estimation system

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Fig. 8

Histogram of proportion of time of SpO2 within, lower, and above the target range (87−93) during 3-h period

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Fig. 9

The recording of SpO2 and FiO2 for the 3-h period while using manual control

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Fig. 10

The recording of SpO2 and FiO2 for the 3-h period while using P-controller with estimation system

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Fig. 11

The recording of SpO2 and FiO2 for the 3-h period while using PI-controller with estimation system

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