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. 2014 Jul;4(3):181-93.

Specification and Verification of Medical Monitoring System Using Petri-nets

Negar Majma  1 , Seyed Morteza Babamir  2
Affiliations

Specification and Verification of Medical Monitoring System Using Petri-nets

Negar Majma et al. J Med Signals Sens. 2014 Jul.

Abstract

To monitor the patient behavior, data are collected from patient's body by a medical monitoring device so as to calculate the output using embedded software. Incorrect calculations may endanger the patient's life if the software fails to meet the patient's requirements. Accordingly, the veracity of the software behavior is a matter of concern in the medicine; moreover, the data collected from the patient's body are fuzzy. Some methods have already dealt with monitoring the medical monitoring devices; however, model based monitoring fuzzy computations of such devices have been addressed less. The present paper aims to present synthesizing a fuzzy Petri-net (FPN) model to verify behavior of a sample medical monitoring device called continuous infusion insulin (INS) because Petri-net (PN) is one of the formal and visual methods to verify the software's behavior. The device is worn by the diabetic patients and then the software calculates the INS dose and makes a decision for injection. The input and output of the infusion INS software are not crisp in the real world; therefore, we present them in fuzzy variables. Afterwards, we use FPN instead of clear PN to model the fuzzy variables. The paper follows three steps to synthesize an FPN to deal with verification of the infusion INS device: (1) Definition of fuzzy variables, (2) definition of fuzzy rules and (3) design of the FPN model to verify the software behavior.

Keywords: Continuous infusion insulin device; fuzzy Petri-net; fuzzy rule; wireless body area network.

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Conflict of interest statement

Conflict of Interest: None declared

Figures

Figure 1
Figure 1
The proposed system diagram
Figure 2
Figure 2
Fuzzy system components
Figure 3
Figure 3
Blood sugar membership function
Figure 4
Figure 4
Insulin membership function
Figure 5
Figure 5
Body mass index membership function
Figure 6
Figure 6
Dose injection log membership function
Figure 7
Figure 7
Output dose membership function
Figure 8
Figure 8
Fuzzy rules to verify behavior of the medical monitoring device
Figure 9
Figure 9
Aggregation of three rules
Figure 10
Figure 10
A sample output
Figure 11
Figure 11
The overall variation of amount of two dose parameters
Figure 12
Figure 12
Fuzzy Petri-net for insulin using the membership function
Figure 13
Figure 13
Firing the fuzzy Petri-net (a) Before firing the tn transition (b) After firing the tn transition
Figure 14
Figure 14
A Petri-net for some rules
Figure 15
Figure 15
(a) Before firing (b) R4 fired (c) A sample of firing rule
Figure 16
Figure 16
State space of Figure 14
See this image and copyright information in PMC

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