A Macro View Model of a Bilirubin Monitoring System for Newborns

Fernando Crivellaro

1 a

, Ana Isabel Sousa

1 b

, Maria Narciso

1 c

, Rui Valente de Almeida

1 d

Anselmo Costa

and Pedro Vieira

1 e

Faculty of Science and Technology, NOVA University of Lisbon, Caparica Campus, 2829-516 Caparica, Portugal

Department of Paediatrics, Hospital Garcia de Orta, EPE, Almada, Portugal

Keywords:

Jaundice, Bilirubin, Newborns, Uniﬁed Modeling Language.

Abstract:

All newborns are routinely monitored for the development of jaundice due to they biological immaturity

to conjugate bilirubin. This situation is worrying because neonatal jaundice is a very common condition

and high-levels of unconjugated bilirubin concentration have neurotoxic effects. Therefore, a continuous

bilirubin monitoring system for newborns is being suggested to overcome visual inspection errors and to

reduce invasive procedures. This system is presented through a macro view modeling approach, in order to

validate the requirements and to build a base infrastructure to posteriorly progress in more detailed diagrams

for development support. The Uniﬁed Modeling Language was used for diagrams composition and, thus,

it was also developed a brief description about the different diagram types, in order to clarify the diagrams

selection. The system modeling at early stages was considered a powerful engineering methodology for new

designs due to its diffusion capacity of the system basic concepts for the respective interdisciplinary group

involved in this research.

1 INTRODUCTION

Jaundice is an abnormal yellowish of the skin, sclerae,

or mucous membranes due to accumulation of biliru-

bin in these body tissues (Jones et al., 2004). The

neonatal jaundice occurs at rates of 50 % for term

newborns and 80 % for preterm newborns. These sig-

niﬁcant rates and the possibility of evolution to an en-

cephalopathy lead to a strong recommendation about

routine monitoring of the newborns for the develop-

ment of jaundice (World Health Organization, 2010).

Although visual inspection is recommended as a

ﬁrst patient approach, it is classiﬁed as not reliable

to estimate the bilirubin levels in newborns, then, the

bilirubin levels should be measured non-invasively

by transcutaneous bilirubinometers or invasively by

serum bilirubin analysis (World Health Organization,

2017; Slusher et al., 2011; National Institute for

Health and Care Excellence, 2010). The measure-

ment of the Total Serum Bilirubin (TSB) is an inva-

sive and stressful procedure. Otherwise, the monitor-

https://orcid.org/0000-0002-7534-9149

https://orcid.org/0000-0003-2980-4742

https://orcid.org/0000-0001-5079-9381

https://orcid.org/0000-0002-2269-7094

https://orcid.org/0000-0002-3823-1184

ing of the Transcutaneous Bilirubin (TcB) is a reliable

and non-invasive method that can decrease the num-

ber of blood sampling required for jaundice evalua-

tion (Ercan and

Ozg

un, 2018; Jnah et al., 2018).

The TcB measurement is the analysis of the skin

diffuse reﬂectance, when it is exposed to different

wavelengths. The spectral content of the measured

light will depend on the concentration of the differ-

ent chromophores in the skin and subcutaneous tis-

sue. Therefore, through the absorption spectral differ-

ences, the TcB level is calculated. Thus, beyond the

blood sparing, the TcB easily allows more frequent

measurements, what is of great value for preterm

neonates, that have more risk factors predisposing to

neurotoxicity, or critically ill babies, that already pass

through painful procedure (Engle et al., 2014; Lyn-

gsnes Randeberg et al., 2005).

Also, as the jaundice management is made over

time, some studies reinforce the analysis of the rate

of rise of bilirubin as a predictor for risk designation,

or as an indicator for the phototherapy timing and

duration, or even for early discharge policy in term

and late preterm neonates (Hahn et al., 2019; Thakkar

et al., 2017; Bhutani, VK, Johnson, L, Sivieri, 1999).

Therefore, the idea behind this article is to model a

macro view of a system to continuously monitor TcB

136

Crivellaro, F., Sousa, A., Narciso, M., Valente de Almeida, R., Costa, A. and Vieira, P.

A Macro View Model of a Bilirubin Monitoring System for Newborns.

DOI: 10.5220/0008965301360141

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES, pages 136-141

ISBN: 978-989-758-398-8; ISSN: 2184-4305

Mobile

Network

Cell 1

Base Station

BLE

Light

Wearable

Devices

Cell N

Frontend

Auth

Web

Server

Clinician

Database

Figure 1: ProtoBili System.

of newborns towards the blood sampling minimiza-

tion through the analysis of the TcB variation patterns

with artiﬁcial intelligence, as depicted on Fig. 1.

The system operation scenario could be inside a

hospital or at the patients houses, through the creation

of measurement cells which have non-invasive contin-

uous measurement devices installed on newborn pa-

tients. Each cell has a base station that controls and

reads the acquired data of the devices via Bluetooth

Low Energy (BLE). Beyond this, each base station

communicates through the mobile network to a web

server, which interfaces with the database, the artiﬁ-

cial intelligence services and the clinician frontend.

After this introduction, a brief description of the

main aspects of the modeling theory will be ap-

proached, before the system modeling presentation

and the conclusion.

2 MODELING THEORY

A common approach for complex systems develop-

ment is the use of models. The modeling works to

improve the overall system comprehension at differ-

ent abstraction levels and allows the evaluation of dis-

tinct strategies for a system development (Zurawski,

2005). The standard language for visual modeling is

the Uniﬁed Modeling Language (UML), which is be-

ing used and improved since 1997, when the Object

Management Group (OMG) released the ﬁrst speciﬁ-

cation. Today, the last UML speciﬁcation is the 2.5.1,

published in December of 2017. The UML objec-

tive is to provide tools for analysis, design and imple-

mentation of software-based systems, business mod-

els or other similar processes (Gomes and Fernandes,

2010; OMG, 2015). The UML works through dia-

grams and each one can express a perspective of the

system. These diagrams are classiﬁed as Structure or

Behavior diagrams, which will be brieﬂy described in

the next sections.

2.1 Structure Diagrams

The Structure diagrams expose the static elements of

a system, as a time irrespective speciﬁcation (OMG,

2015). The focus on the system architecture design

require a signiﬁcant understanding of which elements

integrate the system and how the relationships to one

another are established. These informations can be

useful to manage resources, deﬁne parallel develop-

ments or reused elements and achieve a better sys-

tem implementation ﬂow (Alhir, 2003). The UML

diagrams used to express the system structure are de-

tailed below.

Class Diagram: the Class is the main diagram

of the object-oriented development and design. Be-

yond the different types of objects, the classes spec-

iﬁes they structural and behavioral features. These

features are Properties, Operations, Receptions, Ports

and Connectors (Gomes and Fernandes, 2010; OMG,

2015).

Composite Structure Diagram: when there is a

need to reveal the design of complex or aggregate el-

ements and they interfaces, a Composite Structure di-

agram can be used. Its idea is to break down complex

classes, components or collaborations to visually de-

scribe the roles that these elements play in they con-

text to satisfy the required interactions (Pender, 2003;

Fowler, 2003; Eriksson et al., 2003).

Object Diagram: The objective of this diagram is

to bring the designed classes to life in facts or exam-

ples. Through the instance of different object types

A Macro View Model of a Bilirubin Monitoring System for Newborns

137

and the representation of they relationships, it is pos-

sible to evaluate whether the Class diagram is cor-

rect and complete (Hamilton and Miles, 2006; Fowler,

2003).

Component Diagram: this kind of diagram rep-

resents the ﬂexible and reusable system components.

A component is a unit with a replaceable manifes-

tation inside the system environment, with speciﬁc

deﬁnitions about its provided and required Interfaces.

These Interfaces specify attributes, association, as

well as Operations and Receptions, which are needed

to perform the component expected functionalities

(OMG, 2015; Pender, 2003).

Deployment Diagram: the physical architecture of

the system elements, as well as, the connections and

protocols they have to each other, are visually repre-

sentations inside a Deployment diagram. The beneﬁts

of this kind of diagram is the capacity to identify the

hardware capabilities to optimize the software devel-

opment (Kimmel, 2005; Pender, 2003).

Package Diagram: as a key point to the the sys-

tem stability, the Package diagram is of great rele-

vance to evaluate packages relationships. It also com-

prehends a mechanism to manage the model groups,

where each element can only be owned by one pack-

age (Hamilton and Miles, 2006).

Proﬁle Diagram: due to many different applica-

tion domains where the UML can be applied, it is

possible to create and associate stereotypes, tags and

constraints in a speciﬁc collection called Proﬁle dia-

gram. For example, proﬁles can be deﬁned by speciﬁc

code generation tools to build platform adapted arti-

facts (Alhir, 2003; Pender, 2003; Hamilton and Miles,

2006).

2.2 Behavior Diagrams

The focus of this kind of diagrams is to understand

how the system elements interact and collaborate with

one another to achieve an objective. This information

helps to evaluate if the system, when implemented,

will satisfy its requirements (Alhir, 2003). The UML

diagrams used to express the system behavior are pre-

sented below.

Use Case Diagram: the system interaction with its

environment can be illustrated through the Use Cases

diagrams. They comprehend a collection of diagrams

and text that are useful for system requirements spec-

iﬁcation, helping to identify, in a simply and easily

way, the users expectations for the system. They

are also an excellent starting point for projects be-

cause they targets tend to be more clariﬁed, allowing

a development focused on the well understood goals

(Martin and M

uller, 2005; Pender, 2002).

Activity Diagram: this UML diagram is used to

describe details of processes, use cases, algorithms or

operations. It illustrates a sequence of elementary ac-

tions, that can be synchronous, parallel, concurrent,

or marked by speciﬁc conditions or decisions that ex-

press the required performance. The Activity dia-

grams have the capability to represent logic at the sys-

tem level, as well as in the individual methods level

(Weilkiens, 2008; Pender, 2003).

State Machine Diagram: in order to describe how

an external stimuli inﬂuence a class, a subsystem or

an entire application over time, a State Machine dia-

gram can be used. The value of this kind of diagram

is in the contribute to the object states deﬁnition and

the correspondent identiﬁcation of its attribute or state

changing conditions. The UML consider state ma-

chines that focus on the element implementation as

behavioral state machines and those that express re-

quired protocol behaviors, when the focus is on state

changes triggers, as protocol state machines (Pilone

and Pitman, 2005; Pender, 2003).

Interaction Diagram: these diagrams are useful

for individuals or groups to have different perspec-

tives about the objects or processes intercommunica-

tion. They basically express the ﬂow of control and

data among system objects. According to they pur-

poses, the Interaction diagrams can be particularized

as (OMG, 2015; Brambilla et al., 2017):

• Sequence Diagram: it deﬁnes the temporal se-

quence of messages considering a speciﬁc system

execution scenario;

• Communication Diagram: it models how the re-

lated objects structures are used during the inter-

actions, describing and numbering the messages

that combine information from Class, Sequence

and Use Case diagrams;

• Timing Diagram: it describes the states or con-

ditions changes of a structural element over time,

according to speciﬁc events and constraints;

• Interaction Overview Diagram: it details in a

high-level view the interactions logical progres-

sion required by the system control ﬂow.

3 SYSTEM MODELING

As pointed by (Kimmel, 2005), a cyclic modeling

from a high-level macro view to successively lower-

level micro views can helps in the problem space

comprehension. This approach was used for this arti-

cle, being presented the ﬁrsts modeling cycles of this

system, which is called ProtoBili.

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

138

The ProtoBili high-level model was developed to

describe the system most important characteristics for

a ﬁrst implementation approach. Based on the dia-

grams descriptions of 2, a collection of speciﬁc dia-

grams which will be discussed and presented below,

was used to address the following systems require-

ments:

• Req. 1: the system shall measure the TcB to iden-

tify signiﬁcant hyperbilirubinemia.;

• Req. 2: the system shall operate a maximum of 5

cells with until 10 wearable devices each;

• Req. 3: the system shall allow an authenticated

user to add the patient information to the server

database;

• Req. 4: the system shall allow an authenticated

user to associate devices to patients.

• Req. 5: the system shall allow an authenticated

user to get the stored samples of a patient.

The ﬁrst diagram, presented on Fig. 2, is an exam-

ple of use case, with the most important actions that

an user would perform during its interaction with the

system: to create a cell, to add a patient or to read

the results of a patient. All this actions can only be

performed by an authenticated user, as noted in the

respective diagram.

ProtoBiliUseCase

Create a Cell

Add Patient

Authentication

Device Association

Get Results

Clinician

Newborn

«include»

Figure 2: ProtoBili Use Case Diagram.

The process of a cell creation follows the steps

that are shown on Fig. 3. Assuming that the base

stations are already available in the system, the user

starts selecting which base station controls this cell.

After this, the devices installed on the newborn pa-

tients that are inside the respective base station cov-

ered area could be included.

Create a Cell Activity

Authenticated User

Create a Cell

Base Station Selection

Include New Patient

Select Patient

Include other Patient?

End

Call Add Patient

Is the patient in the system?

Include Patient

Yes

Figure 3: Create Cell Activity Diagram.

When a newborn was not previously added to the

system, the user can include this patient as a new one,

through the insertion of its information and the asso-

ciation of the respective installed device. This process

is done as presented in Fig. 4.

Add Patient Activity

Authenticated User

Add Patient

Insert Patient Information

Call Device Association

End

Figure 4: Add Patient Activity Diagram.

A Macro View Model of a Bilirubin Monitoring System for Newborns

139

The device association to a patient must always

be done. The Fig. 5 exposes this process, which is

performed by a patient selection, followed by the in-

sertion of the respective installed device identiﬁcation

(ID) code.

Device Association

Authenticated User

Associate Device

Patient Selection

Insert Device ID

End

Figure 5: Device Association Activity Diagram.

A more detailed view of the device association

process is presented on Fig. 6, showing the message

changing sequence from the web server to the patient

device. In this case, the idea is to use the BLE ad-

vertising to map which devices are close to the Base

Station. Therefore, when a cell management is being

performed by the user, these devices will be prompted

to the user interface, in order to associated them to a

base station and compose a cell. When the device re-

ceives an association message, it will answer a status

message corresponding to a successful association.

DeviceAssociationInteraction

Device 1

Base Station 1

Web Server

BLE_Advertising

DeviceStatus

StartAssociation

DeviceList

DeviceAssociated

GetDeviceList

AssociateDevices

Figure 6: Device Association Sequence Diagram.

The steps described before comprehend an

overview of the actions involved or required by the

process of creation of a measurement cell. When this

process is successfully done, the results would start to

be acquired and analyzed by the system, allowing the

clinician to evaluate the measured data and the out-

puts obtained from the artiﬁcial intelligence models

of a selected patient, as pointed in Fig. 7.

Get Results

Authenticated User

Get Results

Patient Selection

Show Results

End

Figure 7: Get Results Activity Diagram.

The user authentication process will deﬁne the dif-

ferent user permissions, which will reﬂect the differ-

ent clinician proﬁles. All the users will need an ac-

count to interact with the system.

4 CONCLUSIONS

The model designed and presented in this article have

signiﬁcant information to be evaluated in a macro

perspective, allowing a discussion about the system

functionalities and requirements among the develop-

ers and the clinicians involved in this project, for a

ﬁrst time validation. The Use Case diagram, followed

by the activities and sequence diagrams, demonstrate

how the clinician will interact with the system and

what they can expect about the system operation.

After this high-level and interdisciplinary align-

ment, the next modeling blocks will be designed from

this core validated infrastructure, in order to support

the overall system development. The idea is that these

next blocks will dive inside the lower layers of im-

plementation, even in the cloud side, even in the side

of the embedded system worn by the patient, which

will also require a custom wearable hardware devel-

opment.

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

140

ACKNOWLEDGEMENTS

This work was funded by the FCT PhD grant

PD/BDE/142935/2018.

REFERENCES

Alhir, S. S. (2003). Learning UML. O’Reilly.

Bhutani, VK, Johnson, L, Sivieri, E. (1999). Predic-

tive Ability of a Predischarge Hour-speciﬁc Serum

Bilirubin for and Near-term Newborns. Pediatrics,

103(1):6–14.

Brambilla, M., Cabot, J., and Wimmer, M. (2017). Model-

Driven Software Engineering in Practice. Morgan &

Claypool Publishers, 2 edition.

Engle, W. D., Jackson, G. L., and Engle, N. G. (2014).

Transcutaneous bilirubinometry. Seminars in Perina-

tology, 38(7):438–451.

Ercan, e. and

Ozg

un, G. (2018). The accuracy of transcu-

taneous bilirubinometer measurements to identify the

hyperbilirubinemia in outpatient newborn population.

Clinical Biochemistry, 55:69–74.

Eriksson, H.-E., Penker, M., Lyons, B., and Fado, D.

(2003). UML 2 Toolkit. Wiley, 1st edition.

Fowler, M. (2003). UML Distilled: A Brief Guide to

the Standard Object Modeling Language. Addison-

Wesley Professional, 3 edition.

Gomes, L. and Fernandes, J. M., editors (2010). Behav-

ioral Modeling for Embedded Systems and Technolo-

gies. IGI Global.

Hahn, S., B

uhrer, C., Schmalisch, G., Metze, B., and Berns,

M. (2019). Rate of rise of total serum bilirubin in very

low birth weight preterm infants. Pediatric Research,

(October 2018):1–6.

Hamilton, B. K. and Miles, R. (2006). Learning UML 2.0.

O’Reilly.

Jnah, A., Newberry, D. M., and Eisenbeisz, E. (2018). Com-

parison of Transcutaneous and Serum Bilirubin Mea-

surements in Neonates 30 to 34 Weeks Gestation Be-

fore, During, and After Phototherapy. Advances in

Neonatal Care, 18(2):144–153.

Jones, R., Britten, N., Culpepper, L., Gass, D. A., Grol, R.,

Mant, D., and Silagy, C., editors (2004). Oxford Text-

book of Primary Medical Care, volume 97. Oxford

University Press, 1 edition.

Kimmel, P. (2005). UML demystiﬁed. McGraw-Hill.

Lyngsnes Randeberg, L., Bruzell Roll, E., Norvang Nilsen,

L. T., Christensen, T., and Svaasand, L. O. (2005). In

vivo spectroscopy of jaundiced newborn skin reveals

more than a bilirubin index. Acta Paediatrica, Inter-

national Journal of Paediatrics, 94(1):65–71.

Martin, G. and M

uller, W. (2005). UML for SOC Design.

Springer.

National Institute for Health and Care Excellence (2010).

Jaundice in newborn babies under 28 days. https://

www.nice.org.uk/guidance/cg98?

OMG (2015). OMG

Uniﬁed Modeling Language

:

Version 2.5.1. https://www.omg.org/spec/UML/2.5/

PDF?

Pender, T. (2003). UML Bible. Wiley.

Pender, T. A. (2002). UML Weekend Crash Course. Wiley.

Pilone, D. and Pitman, N. (2005). UML in a Nutshell.

O’Reilly, 2 edition.

Slusher, T. M., Zipursky, A., and Bhutani, V. K. (2011). A

Global Need for Affordable Neonatal Jaundice Tech-

nologies. Seminars in Perinatology, 35(3):185–191.

Thakkar, P., Chavda, H., and Doshi, V. (2017). Transcu-

taneous bilirubin nomogram for healthy term and late

preterm neonates in ﬁrst 96 hours of life. Indian Pedi-

atrics, 54(5):369–372.

Weilkiens, T. (2008). Systems Engineering with

SysML/UML: Modeling, Analysis, Design. Morgan

Kaufmann.

World Health Organization (2010). Effective

Perinatal Care (EPC): Neonatology. http:

//www.euro.who.int/ data/assets/pdf ﬁle/0010/

146818/EPC participants neonatology.pdf?

World Health Organization (2017). WHO recommenda-

tions on newborn health. https://apps.who.int/iris/

bitstream/handle/10665/259269/WHO-MCA-17.07-

eng.pdf?

Zurawski, R. (2005). Embedded Systems Handbook. CRC

Press, 1 edition.

A Macro View Model of a Bilirubin Monitoring System for Newborns

141