Peripheral Intravenous Signal Analysis Fall 2018

1. Peripheral Intravenous Signal Analysis
Md Abul Hayat
Graduate Assistant
University of Arkansas
mahayat@uark.edu
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2. Overview
• Brief Introduction of Project
• Objectives
• Results
• Collaborators
• Conclusion
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3. PIV signal
Peripheral Intravenous Signal
(PIV)
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4. Motivation & Objective
In the United States alone, dehydration affects 30 million children annually 1 and
accounts for 400,000 pediatric emergency room visits 2.
• Intravascular volume or fluid status depends on the concentration of Chloride
and Bicarbonate ion in blood, but it takes significant amount of time to find out
the concentration in laboratory. Using PIV for an alternate method to predict
intravascular volume status.
• To identify the changes in PIV signal before and after bolus.
• To define a quantitative matric for intravascular volume status, possibly from PIV
signal.
• Predict the dehydration level of a patient; i.e. to predict the amount of fluid
needed to restore the normal fluid amount.
[1] Niescierenko M and Bachur R. Advances in Pediatric Dehydration Therapy. Curr Opin Pediatr 2013; 25: 304-309.
[2] Wathern JE, Mackenzie T, Bothner JP. Usefulness of the serum electrolyte panel in the management of pediatric patients treated with intravenously administered fluids. Pediatrics. 2004; 114: 1227-34.
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5. Hypothesis
• Crosstalk between heart rate and PIV signals
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Bit Rate = 60 x Frequency = 60/T = 138
bpm
f = 2.3 Hz

6. Data
• Group 1 : Hydrated, no bolus given
• Data when they arrived at hospital
• Group 2 : Hydrated, bolus given
• Before bolus data and after bolus data
• Group 3 : Dehydrated, bolus given
• Before bolus data and after bolus data
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7. Methods of Analysis
• Time Domain
• Gram-Schmidt Orthogonalization
• Frequency Domain
• Logistic Regression
• Forward Search
• Logistic Regression with LASSO
• KL Divergence
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8. Logistic Regression
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𝑝 𝑥 =
1
1 + exp(−𝛽𝑇𝑥)
Sigmoid or Logistic Function,

9. Logistic Regression
• Two categories: Y = 1 or Y = 0 (the first element of x is 1)
• Log likelihood ratio (LLR )
• Decision boundary is a hyperplane defined by
• Decision
• If
• If

10. Logistic Regression
• Maximum likelihood (ML) is used to find the coefficients
• Log-likelihood function of the i-th training sample
• If
• If
• The Log-likelihood function can be written as

11. Logistic Regression
• Maximum likelihood estimation of
• Log-likelihood function of n samples
We want to find β that can maximize
• The first derivative of
This equation can be solved numerically

12. Results
• Hydrated = ‘0’ Dehydrated = ‘1’
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Data
0 1
Detection
0 89.5 % 20.5 %
1 10.5 % 79.5 %
MATLAB function : mnrfit

13. Results
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(a) Cross validation in LR gave poor results in
detection.
(b) New three patients were identified correctly
(c) Relevant frequencies were identified assuming
gaussian distribution. (p-value)
(d) Submitted these results in a medical conference.
Fourier transform of a 10s window with frequencies
limited up to 20 Hz with 50 sample points.

14. Results
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15. Logistic Regression LASSO
• LASSO : Least Absolute Shrinkage & Selection Operator
Cost Function : Logistic Regression
Cost Function : Logistic Regression with LASSO:

16. Logistic Regression LASSO
• For no cross validation LASSO gives the exact
same result for window detection as without
LASSO.
• With cross validation LASSO, it forces some of
the β coefficients to be zero.
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Data
0 1
Detection
0 88 % 29 %
1 12 % 71 %

17. Kullback-Leibler Divergence
• For comparing the probability distribution across groups before and after
bolus.
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18. Possible Application
• War Zone
• Dehydration of babies
• Pyloric Stenosis
• Department of Defense
• Pig Experiments
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19. Collaborators
• Dr. Jingxian Wu
• Dr. Morten Olgaard Jensen (BME, UArk)
• Dr. Hanna K. Jensen (BME, UArk)
• Dr. Kevin Sexton (UAMS)
• Dr. Patrick Bonasso (UAMS)
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20. Questions
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21. Thanks for your patience !
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