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Applied Soft Computing 11 (2011) 4448–4457

Contents lists available at ScienceDirect

Applied Soft Computing

j ourna l ho me p age: www.elsev ier .com/ l ocate /asoc

eview Article

odelling of urological dysfunctions with neurological etiology by means of theirentres involved

avid Gila,∗, Magnus Johnssonb, Juan Manuel García Chamizoa, Antonio Soriano Payaa,aniel Ruíz Fernándeza

Computing Technology and Data Processing, University of Alicante, SpainLund University Cognitive Science & Department of Computer Science, Lund University, Lund, Sweden

r t i c l e i n f o

rticle history:eceived 23 January 2009eceived in revised form 9 July 2010ccepted 2 May 2011vailable online 20 May 2011

eywords:uzzy logic systems

a b s t r a c t

Urinary incontinence is a considerable problem which is clearly reflected in the number of patientsaffected by it. Moreover, it is extremely difficult to obtain an accurate diagnosis as the urinary incon-tinence very often is related to the neurological system. In this article a model with capabilities forurological diagnosing is proposed. This model is specialized towards the diagnosis of urological dysfunc-tions with neurological etiology. For this reason the model explores all the neural centres involved inboth the urological phases, voiding and micturition. Once these centres have been studied it becomespossible to establish a direct relation between neural centres which present an anomalous functioning

eurofuzzyrtificial neural networksodelling biological systemsrologyxpert systems in medicinertificial intelligence

and neurological dysfunctions.© 2011 Elsevier B.V. All rights reserved.

ecision support systems

ontents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44482. Model of the lower urinary tract and the proposed extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4449

2.1. Detection of dysfunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44512.2. Detection of neural centres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4452

3. Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44533.1. Approaching the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44533.2. Detection of dysfunctions with urodynamical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44543.3. Detection of dysfunctions by means of neural centres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4455

4. Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4456Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4456References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4457

. Introduction of view, the individuals with incontinence are predisposed to the

Urinary incontinence is one of the problems of the urinary sys-em that can affect persons of any age, but it occurs particularlyrequently in the geriatric population, among children, paraplegicsnd postpartum according to sources [1]. From a medical point

∗ Corresponding author.E-mail address: dgil@dtic.ua.es (D. Gil).

568-4946/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.asoc.2011.05.029

development of infections of the urinary tract. From a psychoso-cial point of view they often feel ashamed and suffer isolation anddepression. This is especially true for the infantile and the adultpopulations.

In the lower urinary tract (LUT) elements intervene which are

not linear and of difficult characterization and it is one of thesystems solely controlled by the sympathetic, parasympatheticand somatic nervous systems [2,3]. The appreciable complexity ofthe regulator can be easily understood if it is considered that its

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D. Gil et al. / Applied Soft C

unctionality consists partly of reflexive and partly of voluntaryctivity; as well as the combination of both.

Furthermore, etiological studies of the lower urinary tractave demonstrated a great pathological diversity [4,5]: the sameysfunction can have neurogenic, anatomical, infectious and

nflammatory causes, etc.The quite limited knowledge available regarding the lower uri-

ary tract is partially due to its complexity, to limitations that existn the current research tools and to the diversity relating to bothhe methods used by the scientists and to the interpretation thathey make of the results obtained. Consequently, articles displaying

anifest contradictions at various levels have been published: withegard to the morphology of the musculature [6–8], with regardo the connections of the nervous peripheral system [3,9,10], withegard to reflexes [11,7] and with regard to pontine and supra-ontine connections and their role in control [3,12–14].

The remaining part of this paper is organized as follows: first, aescription is given of the original model, emphasizing its exten-ion, in which the experiments will be carried out and which haseen implemented by means of a fuzzy approach. With this exten-ion, the model enhances its diagnosis capabilities as it is now ableo diagnose urological dysfunctions with neurological etiology fromhe functioning of its neural centres. Then, a description of the twoays this model operates is given. Later, the subsequent testing

arried out in order to analyze the results is described. Finally, theelevant conclusions are drawn.

. Model of the lower urinary tract and the proposedxtension

Bearing in mind that in a biological system ignorance and ambi-uity normally are prevalent, it is necessary in order to be able toevelop a specification of this system to continue along the lines rigorously as possible. In this respect the starting point of theesearch work is the elaborated model of the LUT [15] as well as theharacterization and alteration of urological dysfunctions [16,17].

The presented model of the LUT based on the agent paradigm ishown in Fig. 1. The original nomenclature, which is much wider

escribes the whole paradigm of agents in which the entire workf the model of the LUT has been based [17], has been rewrit-en to facilitate the reading of a complex and extensive model.he LUT is divided into two parts: the mechanical system (MLUT)

ig. 1. Approximation of the artificial model to the biological system. The nine neural cenfferent and internal) as the biological model in order to display the highest possible deg

ing 11 (2011) 4448–4457 4449

and the neuronal regulator (RLUT). The agents which constitutethe multiagent system correspond to the neuronal centres of theRLUT. These agents collect information generated by the MLUT andprocess/transmit it back towards the mechanical part. Each agentmakes a contribution to the system, called influence, in such a waythat the total number of the different influences will determine theoverall state of the system and the activation or non activation ofthe different signals involved. In the process, the model of the LUTis defined as:

LUT = 〈MLUT, RLUT,MLUT IRLUT 〉 (1)

where the MLUT models the mechanical part of the lower urinarytract, the RLUT models the neuronal regulator of the lower uri-nary tract and the MLUT IRLUT approaches the relation between bothparts. Since the interface regards the LUT as a system of actions andreactions [18], it is defined as:

MLUT IRLUT = 〈Y, I, P〉 (2)

In the above equation Y is the set of possible states in whichthe system can stay, whereas I is the set of the possible intentionsof actions (an action proposed by an agent is represented as anintention of modification, defined above as set of influences willdetermine the overall state) in the system and finally P is the set ofthe actions (plans) that the different agents can execute with theobjective of modifying their states.

The neuronal regulator of the lower urinary tract is composedof a set of neuronal centres (NC) that are constantly perceiving,deliberating and executing. The neural centres are modelled as PDE(Perception-Deliberation-Execution) agents with capacity of mem-orizing and decision [19,20]. In this way, a centre ̨ ∈ NC ̨ ∈ NC isdefined by [18]:

˛ = 〈E˛, s˛, Percept˛, Mem˛, Decision˛, Exec˛〉 (3)

where E˛ corresponds to the set of perceptions; s˛ corresponds tothe set of internal status; Percept˛ provides the centre with infor-mation about the state of the system; Mem˛ allows the centre toshow awareness of the state; Decision˛ selects the next influence;

and Exec˛ represents the agent’s intention of acting on the system.These functions present a general structure that depends on speci-fied sets and functions of each agent. We can appreciate the internalstructure of an agent in Fig. 2.

tres are represented in the artificial model with the same types of signals (afferent,ree of similarity.

4450 D. Gil et al. / Applied Soft Compu

Fig. 2. Internal structure of the neuronal centres used in the model with the per-cf(

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eption (Percept), memorization (Mem), decision (Decision) and execution (Exec)unctions, the state of the system (y), perceptions (e), internal state (s), plan decidedp) and influences (i).

More specifically, the perception function of an agent ̨ will con-ist of extracting the sublist of pairs from the state of the worldhose afferent neuronal signals are the destination of the ̨ agent

nd whose efferent signals are the origin. The memorization func-ion will associate the internal state of the agent with a perceptionnd the decision function will be in charge of associating a task with

perception in a specific internal state. The actions on the systemre carried out by means of the function of execution.

Percept˛ : Y → E˛; Mem˛ : E˛ × s˛ → s˛

Decision˛ : E˛ × s˛ → P; Exec˛ : P × E˛ → I(4)

On the other hand, the mechanical system (MLUT) is definedike a function that from a set of efferent signals generates afferentignals. This function is named MLUT and it provides the next statef the system depending of the influences of the different agentsnd the current state:

LUT : Y × I → Y (5)

The new state of the system is the result of evaluating thenfluences contributed by different agents when concurrently per-orming their tasks. The state of the system with regard to time wille expressed by:

y(t + 1) = MLUT

(y(t),

n∏i

Execi(Decisioni(ei(t), si(t)), ei(t))

)s1(t + 1) = Mem(e1(t), s1(t));. . . ;sn(t + 1) = Mem(en(t), sn(t))

(6)

In the previous section has been formally defined the originalodel of the LUT implemented by means of multiagent systemshich displayed a high degree of similarity with the biologicalodel [21] with flexibility regarding the capacity to add or change

he functions and the components. This flexibility was also a goodeason to carry out the implementation of the model.

The nomenclature used in Fig. 1 is DACD and indicates an afferentignal from the mechanical part (detrusor) towards the CD neuralentre for the afferent signals. For the efferent signals SMED indi-ates an efferent signal from the SM neural centre towards theechanical part (detrusor).The biomedical Knowledge-Based Systems (KBS) in general, and

n particular the urodynamic ones, as well as the Decision Supportystems (DSS) constitute two fundamental elements for a better

nderstanding of the LUT. Thus it is necessary to incorporate thems part of the model, which will significantly extend and enrich it.his extension will be possible thanks to the considerations maden previous work on a flexible system that was facilitating the

ting 11 (2011) 4448–4457

incorporation of new knowledge in the model, leaving this pos-sibility entirely open for future research [22].

When using the model mentioned above, which takes into con-sideration all the principal neural centres and both the voluntaryand involuntary reflexes [12,23], it is possible to simulate dysfunc-tions, especially the neurogenic ones, modify the parameters, studythe influence of every variable and, especially, help understandingeach variable’s function in order to resolve as far as possible thepresently existing contradictions [3].

Given the diversity of dysfunctions due to neurological reasonsa classification will be established and it will be analyzed howthey influence the model mentioned above. With this perspectivesome bidirectional cause and effect associations will be establishedbetween the dysfunctions with neurogenic etiology and the neu-ronal centres involved in these dysfunctions and which form partof the LUT model.

In this regard, an extension of the model, by means of multiagentsystems is proposed. This extension is showed in Fig. 3 displayingthe interconnection between all the subsystems where:

• MLUT: structure that represents the mechanical system, formedby the mechanical components and its relations.

• RLUT: structure that represents the neural regulator. It is definedby a series of agents, one for every neural biological centre. InFig. 3 it can be seen that the new elements (intelligent agents)present a series of internal connections. These internal signals(which are the union of internal states and influences are repre-sented by s and i, respectively, and they determine the overallstate of the system) are the connections between the neuralcentres and they work in a similar way as the biological modelshowed in Fig. 1. This figure also illustrated the efferent signalstowards the mechanical system and the afferent signals from themechanical system.

• UDMAS (Urological Diagnosis Multiagent System): structure thatrepresents the system of control and diagnosis of the neural reg-ulator. It is defined by a set of control and diagnostic agents. Thisrepresents the extension of the original model. They control thefunctioning of the neural centres of the RLUT.

As can be seen in Fig. 3 every diagnosis agent (DA) that belongsto that system (UDMAS) has a different sub-indication (DACD,DAPAG, DAPA). This indicates that each of these agents communi-cates directly with one and only one of the agents of the neuralregulator (a neural centre). This circumstance and the specializa-tion of every agent provides the capacity to know in every case thevalues both of the input and of the output of the neural centre. TheDA are implemented by ANNs storing all the internal informationof the neural centres and learning with new data. In this sense, atall times, a trustworthy diagnosis which makes corrections, whennecessary, by means of the corresponding influences will be carriedout. In addition, the mechanical system presents an incorporationof an agent, AM, with respect to the original model. Not inter-nally, but at the level of the communication interface. This newelement not only provides an increased communication betweenthe mechanical system and the neural regulator but also a connec-tion between the dysfunction due to neurogenic reasons with theinformation proceeding from the urodynamic studies. Until nowthis link, which is fundamental to the work of urologists, was notincluded. Hereby, the current model offers a more real and generalvision, being able this way to derive, simulate and experiment onthe assembled neural signals and urodynamical data.

Furthermore, the UDMAS contains another agent named ACOOR.This agent coordinates the communication with the diagnosisagents managing the requests of these to the knowledge base andconnecting with the mechanical system through AM.

D. Gil et al. / Applied Soft Computing 11 (2011) 4448–4457 4451

CD

PAG

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PA

SS

PMPS

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MLUT

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Urethra

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RLUT

UDMAS

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Fig. 3. Interconnection of the extended model. MLUT and RLUT repres

Finally, there is another element included in the UDMAS, whichas been also incorporated to this extension of the model, and that

s the knowledge base. It contains all the information regarding theifferent urodynamic curves in order to compare them with the

nformation received through the monitoring agent.In the next sections it will be explained how this system operates

n two ways.

.1. Detection of dysfunctions

The model makes it possible to disable all the various individualentres in order to simulate the influence of it in the whole sys-em. Fig. 4 summarizes graphically the procedure of making thisiagnosis. As can be seen in Fig. 3 the knowledge base integrated

n the UDMAS will have all the information about the urodynamicurves relating to each known dysfunction as well as the statesf normal micturition. Secondly, every one of the diagnosis agentsas the values of the efferent, afferent and internal signals for eachf the centres both with regards to the normal states and to theysfunctions.

It is fundamental to stress that it is a question of a knowl-dge base with all the inherent dynamic properties of learning thatakes it possible to increase this much the knowledge of both the

rodynamic curves and the nerves signals of the neural centres.Consequently, as a result of this knowledge, one proceeds in the

ollowing manner. This procedure will be explained by means of an

xample. Fig. 4 shows a fail in the centre SS. DASS knows the centre’sunctioning both at the level of internal states, which are the signalshowed in Fig. 1, (sC

SS) and of the influences (iCSS). Thereby the agentASS detects a failure in the SS centre by means of the followingquation:

e original model and UDMAS its extension (the diagnosis capability).

∀ ̨ ∈ (1 . . . n) / i˛ /∈ iCSS ∨ s˛ /∈ sCSS (7)

For that reason a centre presents a dysfunctional state when acontinued set of values (n) do not belong to the correct values forthis centre, both at the level of internal states (sC

SS) and of influencesof the state (iCSS).

DASS informs the coordinating agent ACOOR that the SS centreworks incorrectly and that this failure has produced the curve pro-jected from the SS centre as shown by Fig. 4. ACOOR establishesdegrees of similarity, by means of membership functions (MFs),with the different dysfunctional curves that are stored in the KB. Inother words, it indicates the degree of dysfunction according to thedegree of membership. In addition the KB admits new incorpora-tions for its growing knowledge.

At the instant the dysfunction appears (when DASS detects it) inthe centre it is compared with the urodynamical curves of the KBto determine the degree of similarity with each one of them. Thiscan be expressed as follows:

Similarity = 1 −∫ tf

ti

UG(SS) − UG(D) dt (8)

That indicates the degree of similarity of the Urodynamic Graph(UG) of the SS centre (UG(SS)) with each of the curves of dysfunction(UG(D)) during the entire time t of duration of the curve. If the resultof the integral is 0 it means that the similarity is the highest (thefinal value of the equation is equal to 1). From there, the higher isthe value of the integral, the lower is the similarity, which will be

subtracted from the unit. ti and tf represent the initial and final timerespectively. The definite integral, function of time, of the functionsUG(SS) and UG(D) represent the area in which there is no similaritybetween these functions.

4452 D. Gil et al. / Applied Soft Computing 11 (2011) 4448–4457

on of

2

ci

neural centre disabling its neural signals in order to approximate

Fig. 4. Detecti

.2. Detection of neural centres

In the opposite way to the previous section it is possible to dis-over the centre or centres which causes the dysfunction. The basicdea is to disable every centre in an iterative procedure.

Fig. 5. Specific dysfunction and failure simulation in the neural centres to establi

a dysfunction.

Fig. 5 shows the simulation of an anomalous functioning of a

the output function to the diagnosis curve. To do so, the procedureapplied in the previous section is repeated in an iterative way inevery centre and with all neural signals.

sh resemblances between this anomalous functioning and the dysfunction.

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D. Gil et al. / Applied Soft C

imilarity˛ = 1 −∫ tf

ti

UG(˛) − UG(D) dt, ∀ ̨ ∈ NC (9)

This equation indicates the degree of similarity of the Urody-amic Graph (UG) of every centre (UG(˛)) with each of the curvesf dysfunction (UG(D)) during the entire time t of duration of theurve. On this occasion the procedure works as explained below.ig. 5 shows the projected curves of the diagnosis agents SS, PMnd SM due to the malfunctioning of the above mentioned centres.t this moment the coordinating agent ACOOR establishes degreesf similarity between the curves with dysfunction with each one ofhe centres with the different dysfunctional curves that are storedn KB. This approximation, as it was done in the previous paragraph,s also carried out with respect to the MFs. This way, each one ofhe curves represented by failures in each of the neural centres will

how a degree of similarity with each of the dysfunctional curvestored in the KB. The degree of similarity with the neural centresllows to identify the centre or centres affected by this dysfunction.inally, it is worthy to mention that although we are aware that the

0 200 400 600 800

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1

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2

2.5

3

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Dividing line to carry oucorrections

0 200 400 600 80

0.5

1

1.5

2

2.5

3

3.5 x 10-4 Ves

A general fail in SM ED signal

Normal

(a)

(b)

Vol

ume

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Time (sec

Vol

ume

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Time (sec

Fig. 6. (a) Curves with perfect fails in the centre SM for the signal Smed. (b) H

ing 11 (2011) 4448–4457 4453

malfunctions detection method has an exponential cost, the suit-able of this approach will be shown in the experiments as it onlyhas a few entities.

3. Experiments

3.1. Approaching the problem

The accomplishment of this experimentation would follow thesame guidelines as in real life if all the patients were to show thesame values when performing the urodynamic tests. Obviously thiswill never occur.

We are going to review this situation by means of an example.The light graph of Fig. 6(a) corresponds to urodynamic measures

carried out with the highest degree of injury in the SM centre (sig-nal SMED which was already referenced in Fig. 1). Dark curve froma healthy patient is overwritten to remark the wide differencesbetween both of them. We are obviously dealing with theoretical

0 1000 1200 1400 1600

al volume

t

MFD

00 1000 1200 1400 1600

ical volume

onds)

onds)

igh variety of curves with different degrees of fail in the Smed signal.

4454 D. Gil et al. / Applied Soft Compu

0

0.5

1

1.5

2

2.5

3

3.5x 10

-4 Vesical volume

0 100 200 300 400 500 600 700 800

Higher Max Volume

Lower Max Volume

Max Volume OKAreflexia

Hyper

efle

xia ?

Hyper

efle

xia

Vol

ume

(ml)

Time (seconds)

Fig. 7. Indications of curves with and without dysfunctions. The light graph showsthe curve of urine volume of a healthy patient, whereas the dark and broken graphsi

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over 90% of the fourth membership function of areflexia, 6 between

ndicate the points which determine neurological dysfunctions.

odels in which the differences between the normal models andhose which present some failure in a nervous signal can easily beuantified. In real life this situation is not always as obvious. Someignals, although they work in an incorrect manner, do not pro-uce a visible output that can be explicitly reflected in the valuesf the urodynamical curves. The circumstance that a great numberf cases exist with different outputs leads to the need for methodsf learning regarding these curves for the prediction and diagnosisf new curves to be evaluated. These methods of learning as wells the explanatory rules by means of fuzzy logic with their mem-ership functions allow a more real explanation which is closer tohe biological models unlike the classic logic that this would onlye so if all the cases were equal.

This real situation with the curves processing for every curveill indicate a certain degree of error in the SMED signal. Fig. 6(b)

ries to represent this situation with a good quantity of curves, all ofhem with degrees of lesion in SMED signal. It indicates the degreef dysfunction according to the degree of membership.

This problem has been developed by means of a fuzzy approachegarding the explanation of Fig. 6. Then, Fig. 7 represents a gen-ral vision which is essential to deal with many similar graphs. Theight graph shows the curve of a healthy patient in the storage andoiding phases. The dark and broken graphs overwritten indicatehe points which determine neurological dysfunctions.

Fuzzy tool of matlab is the tool chosen to implement the systemince it has a high variety of elements and it has been used in aroad range of areas for solving fuzzy logic problems [24–27].

The implementation consists of three input variables and oneutput variable. This implementation has been performed in a closeooperation with the urologists. It is constructed taking Fig. 7 as atarting point. They expose us, through Fig. 7, to the importancef understanding the vesical volume curve. In particular, the studyerformed by urologists, stress upon the points of inflection suchs maximum volume (highest and lowest). Furthermore, once thiseak is reached, then the turn down is of particular importance.

f it is the case of a healthy patient, it is shown in a continuousine that goes down almost completely towards 0 while otherwise,epending on each of the discontinuous lines, it can lead to certainiseases that are marked on the figure. According to Fig. 7, thisariable measures the maximum value of the curve reached in itsrst peak and it is compared with normal and dysfunctions curves,easuring their degree of approximation. Also according to Fig. 7,

his variable now measures the minimum value which also reachests first peak curve and it is compared with normal and dysfunctionsurves, measuring their degree of approximation.

ting 11 (2011) 4448–4457

The problem areas, dysfunctions, are represented as fuzzymembership values using an intuition technique which involvescontextual and semantic knowledge [28]. The two membershipfunctions used are trapezoidal and triangular. The trapezoidal andtriangular membership functions of a vector x depends on four (a,b, c and d) and three (a, b and c) scalar parameters in Eqs. (10) and(11), respectively, which corresponds to all the problem areas asmentioned.

F(x, a, b, c, d) = max(

min(

x − a

b − a, 1,

d − x

d − c

), 0)

(10)

F(x, a, b, c) = max(

min(

x − a

b − a,

c − x

c − b

), 0)

(11)

The components of the input vector consist of membershipvalues to the linguistic properties such as very low volume, low vol-ume, normal volume and high volume. The first input First VolumeMaximum (FVM) can be represented as FVM = VL (very low), L (low),N (normal), H (high). The membership value of VL, L, N and H can bewritten as in Eqs. (9)–(12) and can be depicted in Fig. 8(a). The otherinputs and the output are represented in Figs. 8(b) 9(a) and 9(b).According to Fig. 7, the last variable input measures the maximumand minimum values reaching the curve but it measures it in thesecond rise instead of the first and it is compared with normaland dysfunctions curves, measuring their degree of approxima-tion. Fig. 7 shows the dashed curves that simulate dysfunctionscompared to healthy patients. This is what is represented by themembership functions of this variable. The output variable relatesto the vesical volume presented on the input variables and the diag-nosis curves. There are many curves, some for healthy patients,some for dysfunctions, such as areflexia and hyperreflexia, but alsosome of them are in between. This indicates a possible dysfunctionsuch as the “PosibHyper” membership function which indicatesa possible hyperreflexia or the “HighVolCap” or the “LowVolCap”which indicate possible problems with the vesical volume that mayappear. These should be clarified via more urological tests.

�(VL) ={

0−0.25

+ 0.5−0.15

+ 1−0.05

+ 10.05

+ 0.50.15

+ 00.25

}(12)

�(L) ={

00.05

+ 0.50.15

+ 10.25

+ 10.35

+ 0.50.45

+ 00.55

}(13)

�(N) ={

00.35

+ 0.50.45

+ 10.55

+ 10.65

+ 0.50.75

+ 00.85

}(14)

�(H) ={

00.65

+ 0.50.75

+ 10.85

+ 10.95

+ 0.51.05

+ 01.15

}(15)

In the next two subsections, two types of experimentationwhich have been carried out will be explained.

3.2. Detection of dysfunctions with urodynamical data

The experimentation of the system has been made with mat-lab, in particular with the Fuzzy logic toolbox. It makes possible todeal with situation of degree of the membership functions whichindicate complex diagnoses, i.e. not a particular dysfunction buta situation with different health states. These tests offer severalkinds of approximations to dysfunctions or health situations but aremade with simulations data. For this reason the model has also beentested with real data obtained after several years of working withurologists at the San Juan hospital in Alicante. The total number ofpatients is 300. More information about this data set is describedin [29]. Fig. 10 represents the neurogenic dysfunctions detected viathe tests carried out on the 300 patients; 20 with areflexia, 10 are

70% and 90%, 4 below 70%, 15 with hyperreflexia, 10 are over 90%of the seventh membership function of hyperreflexia and many ofthem in the fifth one (possible hyperreflexia). 2 between 70% and

D. Gil et al. / Applied Soft Computing 11 (2011) 4448–4457 4455

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.5

1

Membership function FVM

Range of First Volume Maximum

VL L HN

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.5

1

Range of First Volume Minimum

Membership function FVm

L HM

Fig. 8. (a) Fuzzy membership values for First Volume Maximum (FVM). (b) Fuzzy membership values for First Volume minimum (FVm).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.5

1

Membership function NVMm

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.5

1

Membership function Output

Diagnosis - Dysfunctions

Low Vol Cap OK Vol High Vol Cap Areflexia Health HyperPosib Hyper

L HM

Range of Next Volume Maximum Minimum (NVMm)

(NVM

9sot

3

tft

Fh

Fig. 9. (a) Fuzzy membership values for Next Volume Maximum Minimum

0%, 5 below the 70%, 80 healthy patients, 63 above 90% of theixth membership function and also most of them in the secondne. Rest of the not neurogenic dysfunctions, are not covered byhe fuzzy system that considers only neurogenic dysfunction.

.3. Detection of dysfunctions by means of neural centres

The other type of experimentation is to disable every centre of

he neural regulator and then to show how this centre affects theunctioning of the whole system causing a neurological dysfunc-ion.

ig. 10. The total number of patients are 300 divided in four groups (areflexia,yperreflexia, healthy patients and not neurogenic dysfunctions).

m). (b) Fuzzy membership values for kind and level of Dysfunction (GD).

Fig. 1 may help to understand the anatomical distribution ofeach of the centres and their representation in the artificial model.In particular Suprapontine, Suprasacral, Supraspinal and healthypatients are the four targets of comparison. Fig. 11 validates themodel by the normal and malfunctioning operating conditions dueto neurogenic causes.

The experiments are divided into four sections under the fourobjectives of comparison defined above and showed in Fig. 11.When the model does not disable any centre, the result is shownin Fig. 11(a). It then corresponds to a healthy patient, endorsed byseveral curves of healthy patients of the urological databases.

Fig. 11(b) corresponds to the disabling of one of the centresthat are suprapontine CD, PA and PAG. This curve generally cor-responds to a curve of a suprapontine injured patient and mayalso corresponds to patients with Parkinson’s disease, brain tumor,dementia, encephalitis, cerebral paralysis, etc. A wrong functioningin one or more of the suprapontine centres, or in the communica-tion between them and the centres or these ones with the pontineor spinal centres can cause involuntary contractions of the detrusorand involuntary voiding [30]. There is no voluntary and involuntarycontrol of the upper centres.

When an injury occurs between the sacral centres (SM, SS andDGC are now disabled for these test) and the other neural centres(thoracolumbar centre, pontine and suprapontine centre), then, acommunication between them will no longer be possible. The vol-untary and involuntary areas do not control the LUT, but they keepthe vesicosomatic defense reflex and vesicosomatic reflexes of mic-turition and urethra parasympathetic. An injury of this type usuallygenerates a detrusor-sphincter dyssynergia [31]. At the same timethere is a contraction of the detrusor, there is also a contractionof the external sphincter. With this injury the individual does not

retain voluntarily and he expresses a feeling of emptiness in thepresence of small amounts of urine in the bladder (Fig. 11(c)).

In the case of an supraspinal injury, the connections betweenthe spinal centres (SM, DGC, SS and TS) with the pontine ones (PM

4456 D. Gil et al. / Applied Soft Computing 11 (2011) 4448–4457

0 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 02 04 06 08 0

100

120

140

160

180

200

220

(d)

Vesical volume (Vves) - Supraspinal lesion

Time (seconds)

Vol

ume

(ml)

0 20 0 40 0 60 0 80 0 1000 12000

50

100

150

200

250

300

350

(b) Vesical volume (Vves) - Suprapontine lesion

Time (seconds)

Vol

ume

(ml)

(c) Vesical volume (Vves) - Suprasacral

0 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 00

50

100

150

200

250

300

350

(a)

Vesical volume (Vves) - Healthy patient

Time (seconds)

Vol

ume

(ml)

0 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 00

50

100

150

200

250

300

350

Time (seconds)

Vol

ume

(ml)

F suprase sabled

aAurtopcto

4

Taoscdi

sapaim

We want to express our acknowledgement to Mikael Skylv for

ig. 11. The graphs correspond to a specific dysfunction (b, suprapontine lesion; c,

xcept for graph (a) that depicts a healthy patient and where no centre has been di

nd PS) and suprapontine (CC, PA, CPA) will no longer be possible.s in the previous case, the voluntary and involuntary control of thepper areas disappear, but they keep away micturition and defenseeflexes at the sacral level and the vesicosomatic defense reflex athoracolumbar level becomes operational. This lesion causes a lackf excitation of the detrusor and the sphincter, causing a very lowressure [32]. The small difference in pressure generates a outputonstant flow equal to the urine input flow from the kidneys, sohat there is a constant dripping of urine. This behaviour can bebserved in Fig. 11(d).

. Conclusions and future work

In this paper the functioning of an urological model is evaluated.he original model consist of two parts, the mechanical one (MLUT)nd the neural regulator (RLUT). The research include the extensionf the model with a diagnosis system called UDMAS consisting ofeveral autonomous agents. With this extension some bidirectionalause and effect associations have been established between theysfunctions with neurogenic etiology and the neuronal centres

nvolved in these dysfunctions which form part of the LUT model.The ability to carry out simulations enabling and disabling the

ignals of any one of the neural centres or even more of them hasllowed the recreation of any situation which in the real world

resents manifest inconveniences. Thus we have achieved thedvantages provided by the control and diagnosis system regard-ng the neural regulator of the LUT which function in the directions

entioned in Sections 2.1 and 2.2.

pinal lesion; d, suprasacral lesion) all depending of what centre has been disabled,.

The results of the experimentation identify the centres moreclosely related with the neurological dysfunctions, in particularwith suprasacral, supraspinal, sacral lesions or healthy patients.

However, it is not always possible to establish with 100%accuracy which are the centres involved in the neurological dys-functions. Other problems can occur in the LUT such as muscular,physiological, anatomical ones, that can have an influence on theneurological dysfunctions. This is the reason why the diagnosiscan be inaccurate if it is based solely on the neural centres. Thefuzzy logic approach will make it possible to deal with this imper-fection. Apart from physiological and anatomical changes in thepatient, there are always psychological reasons which have markedinfluence on the centres with the neurological dysfunction. Fur-thermore, the quantity of combinations with all the centres andthe number of signals of each of them, makes up a very complexsystem with many degrees of approximation towards the diseases.Membership functions will help to approach this model.

In order to improve diagnosis accuracy, extending to new chal-lenges that arise in urology and always working together withurologists we are currently developing and extending this modelfor the LUT.

Acknowledgments

very helpful comments on the manuscript. The collaboration withurologists of the Hospital of San Juan (Alicante-Spain) has made itpossible to reach a better understanding of the complex neurolog-ical parts of the lower urinary tract.

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D. Gil et al. / Applied Soft C

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