Addressing the flaws of current critical alarms: a fuzzy

constraint satisfaction approach.

Abraham Otero1, Paulo Felix2, Senen Barro2 and Francisco Palacios3

1Department of Software and Knowledge Engineering. University San Pablo CEU.

28668 Madrid, SPAIN. Phone: +34 91 372 4040, fax: +34 91 372 4824.

e–mail: abraham.otero@usc.es

2Departamento de Electronica e Computacion. Universidade de Santiago de Compostela.

15782 Santiago de Compostela, SPAIN.

3Critical Care Unit. Universitary Hospital of Getafe. 28905 Madrid, SPAIN.

August 12, 2009

Summary

Objectives: Threshold alarms, the support supplied by commercial monitoring devices to su-

pervise the signs that pathologies produce over physiological variables, generate a large amount

of false positives, owing to the high number of artifacts in monitoring signals, and they are not

capable of satisfactorily representing and identifying all monitoring criteria used by health care

staff. The lack of an adequate support for monitoring the evolution of physical variables prevents

the suitable exploitation of the information obtained when monitoring critical patients. This work

proposes a solution for designing intelligent alarms capable of addressing the flaws and limitations

of threshold alarms.

Material and Methods: The solution proposed is based on the multivariable fuzzy temporal

1

profile (MFTP) model, a formal model for describing certain monitoring criteria as a set of

morphologies defined over the temporal evolution of the patient’s physiological variables, and a

set of relations between them. The MFTP model represents these morphologies through a network

of fuzzy constraints between a set of points in the evolution of the variables which the physician

considers especially relevant. We also provide a knowledge acquisition tool, TRACE, with which

clinical staff can design and edit alarms based on the MFTP model.

Results: Sixteen alarms were designed using the MFTP model; these were capable of super-

vising monitoring criteria that could not be satisfactorily supervised with commercial monitoring

devices. The alarms were validated over a total of 196 hours of recordings of physiological vari-

ables from 78 different patients admitted to an Intensive Care Unit. Of the 912 alarm triggerings,

only 7% were false positives. A study of the usability of the tool TRACE was also carried out.

After a brief training seminar, five physicians and four nurses designed a number of alarms with

this tool. They were then asked to fill in the standard System Usability Scale test. The average

score was 68.2.

Conclusion: The proposal presented herein for describing monitoring criteria, comprising the

MFTP model and TRACE, permits the supervision of monitoring criteria that cannot be repre-

sented by means of thresholds, and makes it possible to construct alarms that give a rate of false

positives far below that for threshold alarms.

Keywords: Patient monitoring, intelligent alarms, knowledge acquisition and representa-

tion, fuzzy constraint satisfaction problems.

1 Introduction

In order to monitor the state of patients admitted to an intensive care unit (ICU), healthcare staff

must consider large quantities of highly heterogeneous information, including medical history, X-rays,

ultrasound scans, laboratory analyses, data from examinations, etc. On this information, the greatest

overload of work results from the monitoring of physiological variables: electrocardiogram, heart rate,

blood oxygen saturation, breathing rate, blood pressure, etc. These variables evolve over time, and

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often reveal the appearance of physiopatological processes requiring rapid intervention to reduce or

avoid life-threatening situations for the patient. Thus they require continuous attention.

Critical care units are equipped with sophisticated commercial monitoring devices to assist in

the overwhelming task of monitoring patients’ physiological variables. Over the last three decades a

number of improvements have been incorporated into these devices, including larger screens, support

for a second monitor, the storage of various hours of recorded signals and information on alarms that

have been triggered, the capability to monitor more physiological variables, wireless transmission of

information from bedside devices to central workstations, ubiquitous access to data through a Web

interface, and a significant reduction in size (thanks, to a large degree, to the substitution of CRT

monitors for TFT ones).

Nevertheless, the support currently present in monitoring devices to identify abnormal behaviour

on physiological variables is similar to that provided 30 years ago: threshold alarms. These are

triggered each time the value of a variable leaves a pre-established range. The signals usually have

high levels of artifacts (often due to the movement of the patient) resulting in a high number of false

positives. Consequently, health-care staff may lose trust in threshold alarms, and fail to respond as

quickly as they could in situations where intervention is really required and, in extreme situations,

they may ignore and even disconnect the alarms [1, 2]. On the other hand, establishing the ranges

entails searching for a balance between sensitivity and specificity, to keep the number of false positives

within reasonable limits. With these ranges it is often not possible to monitor all those events that

may be indicative of possible life-threatening situations for the patient; hence, the limitations of these

alarms must be offset by the continuous supervision of the healthcare staff.

As it has not been possible to provide a more effective support for the monitoring of physiological

variables, commercial monitoring devices have not succeeded in simplifying the monitoring task; on

the contrary, the fact that they supply ever increasing quantities of data may indeed be counter-

productive if the volume of data available exceeds the cognitive capabilities of medical staff, as they

may be forced to ignore some of the data in the decision-making process [3, 4].

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Problems deriving from the shortcomings of threshold alarms are extensively documented in the

bibliography on critical care. Boldt [5] attributes the increase in functionality in monitoring devices

more to aggressive marketing strategies than to an attempt to address the real needs of healthcare staff,

affirming that it has yet to be determined whether this functionality truly enhances patient security,

or even improves patient outcome. Studies carried out by Chambrin et al. [6, 7] found that the level

of monitoring habitually used in ICUs gives rise to an excessive number of false positives, and they

point to the individualization of monitoring objectives for each patient, and to the incorporation of

multi-parametric monitoring techniques as possible solutions to this problem. Edworthy and Hellier [8]

defend the notion that the continuous triggering of alarms reduces the efficiency of auditory warnings,

suggesting the establishment of certain erroneous threshold limits and the poor design of alarms among

the causes of the problem. Tsien and Fackler [9] assert that the majority of alarms set off in an ICU

have no bearing on the patient’s treatment.

In the following section we analyse the characteristics that a new generation of alarms capable of

supplying improved assistance to healthcare staff should have. In Section 3, we present two patholo-

gies that cannot be monitored satisfactorily with commercial monitoring devices - hypovolemia and

pulmonary embolism. These pathologies will be used by way of example throughout the work to il-

lustrate our solution. In Section 4, we describe a proposal for defining alarms with the characteristics

presented in Section 2. This proposal incorporates the MFTP model, a structural pattern recogni-

tion model with which it is possible to capture a number of monitoring criteria in a computational

representation and identify them over the evolution of a patient’s physiological variables. Physicians

can describe these criteria by using TRACE, a graphical tool designed with this purpose. Section 5

presents the results obtained when various alarms designed with the aid of TRACE were applied over

a total of 196 recordings of physiological variables from 78 patients. This section also presents a study

of the usability of TRACE carried out with the aid of five physicians and four nurses. Finally, we

discuss the results of this work, and present a series of conclusions on it.

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2 A new generation of intelligent alarms

Limitations and deficiencies of threshold alarms have resulted in an increasing imbalance between

the volume of data available on patients and the improvements in health care that these data yield.

There is a need for a new generation of intelligent alarms, supplying more effective support for the

monitoring of pathological signs over the physiological variables of patients and, of course, allowing

the increase in the volume of data to translate into improved health care.

The principal objectives of the enhancements presented in this section are twofold: to reduce the

high number of false positives that appear with threshold alarms, and to supply greater expressive

power in the representation of monitoring criteria. The latter may make it possible to define alarms

capable of supervising signs of pathologies that cannot be adequately monitored using threshold

alarms, and to provide greater diagnostic evidence on the pathology.

2.1 Handling the uncertainty and imprecision of monitoring criteria

The threshold alarms currently in use exhibit an all-or-nothing behaviour, which contrasts with the

nature of illnesses in the clinical domain: often their presence or absence cannot be considered as a

binary problem, rather as a matter of degree [10]. The alarms, which aim to automatically identify

signs of pathologies, must reflect the degree of compatibility between the evolution of the patient’s

physiological variables and the description of the monitoring criteria made by the physician. Using

certain artificially precise criteria in the definition of alarms can lead to significant errors when eval-

uating a set of findings which are on the borderline between values that are clearly normal and those

which are not. New alarms must make it possible to handle the uncertainty and imprecision that are

characteristic of the medical domain, reflecting the gradual transition between those states considered

to be normal and those considered to be abnormal.

Some authors have attempted to capture a completely qualitative description of the patients’ phys-

iological variables [11]; but this often leads to excessive vagueness in the description of the monitoring

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criteria. Others use a semiquantitative representation where intervals are used to discern between nor-

mal and abnormal evolutions of variables [12, 13]. The main drawback of the interval-based proposals

is the risk of committing significant errors in borderline cases. Given that definitions of normality and

abnormality often rely on experience-based heuristic knowledge, fuzzy set theory -which has proved its

value for handling and representing this type of knowledge [10, 14]- would seem to be one of the most

suitable solutions for constructing these alarms, ant it probably is the most resorted to formalism in

the bibliography for developing intelligent monitoring systems [15–22].

2.2 Reasoning over the temporal evolution of physiological variables

One of the principal limitations of the alarms that are currently available, and which gives rise to a

large number of false positives, is that of restricting their activation to the instantaneous value of a

determined physiological variable, and to the membership or not to a range of normality; thus, any

artifacts producing a value outside the range will trigger the alarm. By employing knowledge on the

dynamics of variables and reasoning over their temporal evolution, it would be possible to identify any

inconceivable rate of change in the corresponding physiological variables and identify it as an artifact.

By way of example, for certain variables, such as oxygen saturation, a very sharp fall from a normal

value to a null or very low one is not possible; although it may appear as the result of a converter

saturation in the measurement sensor. The values of other variables may not exceed a given rate of

change; e.g. certain increases in the heart rate are impossible due to them being sharper than the

heart’s response capacity.

Furthermore, the ability to reason over the evolution of a variable means that it is possible to

define alarms with a higher semantic content than that of threshold-based alarms. The bibliography

on critical care reflects physicians’ interest in alarms of this type [23, 24], and a number of solutions

to this problem are proposed in the literature on biomedical engineering. Some simply identify trends

(i.e., values or rates of change sustained over an interval in a physiological variable) [15, 22, 25–28],

while with other proposals more complex morphologies over the temporal evolution of a variable can

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be identified [11–13, 17, 19, 29]. Among these proposals, those indicating the compatibility of the

evolution of a physiological variable with a fuzzy trajectory [15, 19, 22] are considered to be of special

interest, owing to their capacity for capturing and representing vague and imprecise information on

the temporal evolution of the variable, as well as to their robustness against noise and artifacts.

2.3 Integrating information from different variables

The integration of information arising from different variables is another key feature missing in thresh-

old alarms. This capability makes it possible to identify artifacts when the behaviour of one or more

of them is not consistent with the rest [6, 30]. For example, a null value for heart rate can be dis-

carded as an artifact if the systolic, mean and diastolic blood pressure have normal values, as this

indicates that the heart continues to beat. In patient monitoring, the number of false alarms that

are currently generated is increasing proportionally with the number of variables being monitored,

since each variable is monitored by a threshold alarm which, in order to be triggered, only considers

information relative to that variable. Thus, paradoxically, the more information there is available on

a patient, the less reliable monitoring devices will be, since they generate a greater number of false

positives.

On the other hand, the capacity to reason over the temporal evolution of a number of variables

means that alarms can be generated on the basis of findings which, taken in isolation, are irrelevant,

but which may endorse the hypothesis of the occurrence of pathological processes of clinical interest

if they can be related with other findings over other variables, which on their own may also be

irrelevant. Thus, it is possible to reduce the margins of the abnormality values monitored over each

physiological variable, keeping the number of false positives within reasonable levels, thanks to the

merging of information originating from more than one variable. Moreover, these alarms supply strong

diagnostic evience on the pathology being monitored, given the large quantity of information that they

contain.

There are a number of proposals in the bibliography on biomedical engineering which permit

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the integration of information from a number of physiological variables into one single alarm. Some

approaches consider the instantaneous value of two or more variables [20, 31-33], while others make

it possible to relate trends and/or more complicated morphologies that appear over the temporal

evolution of a number of variables [3, 17, 19].

2.4 Editing of the monitoring criteria

The new alarms should allow the healthcare staff to easily edit the monitoring criteria represented by

them without the assistance of a knowledge engineer. Although some medical texts include standard

definitions for certain pathological findings that are reflected in the physiological variables (e.g., a

tachycardia is usually considered as a heart rate in excess of 100 bpm [34]), applying these criteria

without their prior adaptation to the patient’s state and the monitoring objectives is not viable. In

the words of the eminent physician, Gregorio Maranon: “There are no illnesses, just the ill.” To date,

no Gold Standard has been found to define precisely how to individualize monitoring criteria; hence,

patient monitoring is still somewhat closer to a craft than to an exact science. Consequently, two

physicians may use different monitoring criteria for the same patient without it being possible to

assert a priori that one set of criteria is more suitable than the other. It is simply the case that each

physician wishes to be alerted of different deviations from normality. Thus, any proposal attempting

to solve the problem of the cognitive overload must provide some mechanism for individualizing each

patient’s monitoring criteria.

The few proposals in the literature which tackle this problem habitually incorporate contextual

information - which must be supplied by the healthcare staff - on the basis of which they modify the

monitoring criteria [13, 32, 35]. Given that the patient’s life may depend on the alarms, healthcare

staff cannot be expected to put blind faith in a number of criteria generated automatically by a

computer system, unless they can be revised and, if deemed advisable, edited. Furthermore, given

that in the clinical domain there are no monitoring criteria that are unique to a given pathology,

regardless of how much effort is made in carrying out the automatic individualization of the criteria

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obtained, they will never completely satisfy all physicians. Consequently, for a new proposal to be

applied to the clinical routine, it must supply alarms that are easily understandable for the healthcare

staff, and it must make it possible to interpret and edit their content.

Moreover, the generation of the recognition procedures should be automatic and transparent to the

user, and should not require any machine learning process. If, in any of the stages, the physician needs

assistance to define or modify an alarm, this sort of alarm will not go beyond the proof of concept,

and will not reach the clinical routine. In our opinion, this is the main reason why, in spite of the

efforts being made by the biomedical engineering community and of all the proposals appearing in the

bibliography, commercial monitoring devices are only fitted with threshold alarms - a solution which

enables the healthcare staff to modify alarms without the need for assistance. The objective of the

current work is to develop a solution which, besides supplying alarms with a greater semantic content

and increasing their specificity, allows the physician to edit them without the need for assistance.

3 Clinical examples: pulmonary embolism and hypovolemia

To illustrate our proposal we shall use, by way of example, two pathologies which are clearly evident

in the physiological variables commonly monitored in a critical care unit, but which are not identified

satisfactorily by the alarms currently available in commercial devices - pulmonary embolism and

hypovolemia. Both are frequent clinical complications and are often under-diagnosed in situations

where they appear lightly, and where they may be prodromes of life-threatening clinical situations.

The early detection of a monitoring pattern compatible with them would supply the physician with

invaluable evidence for their diagnosis.

A pulmonary embolism occurs when a blood clot that has developed in a blood vessel, normally

situated in the lower extremities, is dislodged and carried by the bloodstream towards an artery in the

lung, where it forms an occlusion. With regard to the set of physiological signals that are habitually

monitored in an ICU, the concurrence of a fall in arterial oxygen saturation with a slight increase in

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heart rate and a related drop in systolic blood pressure in a patient is compatible with this diagnosis

(see Fig.1).

Hypovolemia is a decrease in the amount of blood circulating in the body subsequent to a haem-

orrhage, dehydration or the displacement of liquid to another space (for example, to a haemodialysis

machine). Its presence in the heart rate and blood pressure is similar to that of embolism, but in this

case the oxygen level remains at its basal value (see Fig.1).

Heart rate (HR), blood oxygen saturation (SpO2) and blood pressure (BP) are physiological vari-

ables that are subject to continual fluctuation, so that setting alarm thresholds for them entails

searching for a compromise between sensitivity and specificity for the abnormality, in order to pre-

vent the number of false positives exceeding the healthcare staff’s response capacity. When these

thresholds are not sufficiently sensitive to detect any deviation from normality of physiopathological

interest -such as the case of the aforementioned pathologies, where deviations from normality that are

subtler than the threshold alarms can supervise may be relevant- the only manner of supplementing

this deficiency is through constant supervision by the healthcare staff. Nevertheless, the overwhelming

nature of this task means that occurrences of these types of patterns are only monitored in scenarios

where it is more likely that they will occur, and not in all those that would be desirable.

One example of this type of scenario is haemodialysis - a therapy requiring the introduction of a

catheter into the patient’s body. When the patient’s blood starts flowing through the tubes and filters

of the haemodialysis machine, the reduction of blood in the body may result in hypovolemia. On the

other hand, the introduction of the catheter, habitually by the femoral path, may release blood clots

and trigger an embolism. The probability of this taking place increases considerably if the patient

has been bed-ridden for a considerable amount of time, as often occurs in the case of critical patients,

since the lack of movement may lead to the formation of thrombi. Owing to the risks of haemodialysis,

during this process a physician continuously monitors the patient’s physiological variables, especially

during the initial moments. Maintaining this level of supervision throughout a patient’s stay in the

critical unit is, unfortunately, not feasible, in spite of the fact that pathologies such as those described

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herein may appear, with a lower probability, without there being a clear triggering factor. In such

cases, the problem is not usually identified until one of the patient’s physiological variables leaves the

pre-established ranges of the corresponding threshold alarm. Thus, the opportunity of acting at an

early point in the illness and gaining valuable time that could increase the patient’s possibilities of

survival has been lost.

A monitoring system capable of automatically identifying a monitoring pattern defined over the

concurrence of the aforementioned trends in HR, BP and SpO2 would free healthcare staff of a great

deal of work. By associating changes in a number of variables in one single alarm, it would be possible

to streamline the criteria for deviations from normality for each variable in isolation and, keeping the

amount of false positives within reasonable levels, it would be possible to detect situations without seri-

ous clinical impact which, nevertheless, with a high degree of probability, represent physiopathological

states of interest to the physician.

4 Material and methods

With the multivariable fuzzy temporal profile model (MFTP) it is possible to represent and identify

certain monitoring criteria defined as a pattern of findings that consists of the appearance of a set of

morphologies over several physiological variables, and a set of relations between them. This model

is an extension of the Fuzzy Temporal Profile model [29], which allows a finding to be represented

as a morphology described over a single variable. The fact of being able to relate the occurrence of

different findings among physiological variables is of great importance, as often the appearance of a

finding over a single variable, which on its own may not be a major determinant, may well be of

interest if it appears to be related with other findings over different variables which do not seem to

be definitive when considered in isolation either. For example, the appearance of a slight increase in

HR does not imply a pulmonary embolism; rather it is concurrent with a slight desaturation and fall

in BP.

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Moreover, with the MFTP model it is possible to capture the set of abstraction levels physicians

use to reason over the pattern. Thus, for example, in the pulmonary embolism pattern a first level

of abstraction comprises a set of trends over the physiological variables HR, BP and SpO2. If these

trends fulfil a set of temporal relations (starting approximately simultaneously), the information in

this first abstraction level is aggregated to give rise to information in a higher level of abstraction: the

pulmonary embolism pattern itself.

The pattern that an MFTP represents is obtained directly from a physician. Medical knowledge

is characterized by its large content of experience-based heuristic knowledge and its high level of

vagueness; this means that fuzzy set theory is highly suitable for representing and handling the

knowledge available in this domain [10, 14]. This formalism will be one of the pillars of our solution

for defining intelligent alarms; the other is constraint satisfaction problem (CSP) formalism [36], which

supplies a computable support for the representation of medical knowledge. An MFTP comprises a set

of points (defined over the temporal evolution of the monitored variables) that are especially relevant

for the physician, and a set of flexible constraints that limit the evolution of the physiological variables

between them. These constraints are represented by means of fuzzy sets, allowing a pattern to be

modelled as a flexible set of possible evolutions of the variables.

We now go on to introduce the basic concepts of fuzzy set theory upon which our proposal is based.

4.1 Fuzzy fundamentals

Given a discourse universe U we define the concept of fuzzy value C by means of a possibility distri-

bution πC defined over U [37]. Given a precise value u ∈ U, πC(u) ∈ [0, 1] represents the possibility

of C being precisely u. Given as discourse universe the set of real numbers R, a fuzzy number [38] is

a normal and convex fuzzy value. A fuzzy value C is normal if and only if ∃u ∈ R, πC(u) = 1. C is

said to be convex if and only if ∀ u, u′, u′′ ∈ R, u′ ∈ [u, u′′], πC(u′) ≥ min{πC(u), πC(u′′)}.

By means of πC we define a fuzzy subset C of R, which contains the possible values of C, where

C is a disjoint subset, in the sense that its elements represent mutually excluding alternatives for C.

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The fuzzy set C is characterized by a membership function μC : R → [0, 1] that associates with each

element u ∈ R a real number in the interval [0, 1]; this is termed the degree of membership of u in C.

We obtain a fuzzy number C from a flexible constraint given by a possibility distribution πC ,

which defines a mapping from R to the real interval [0, 1]. A fuzzy constraint can be induced by an

item of information such as “x has a high value”, where “high value” will be represented by πC=high.

Given a precise number u ∈ R, πC=high(u) ∈ [0, 1] represents the possibility of C being precisely u;

i.e., the degree with which u fulfills the constraint induced by “high value”.

Normality and convexity properties are satisfied by representing πC , for example, by means of a

trapezoidal representation. In this way, C = (α, β, γ, δ), α ≤ β ≤ γ ≤ δ, where [β, γ] represents the

core, core(C) = {u ∈ R| πC(u) = 1}, and ]α, δ[ represents the support, supp(C) = {u ∈ R|πC(u) > 0}

(see Fig. 2). The MFTP model can use any normal and convex representation for the possibility dis-

tributions, given that its recognition and consistency analysis procedures only rely on these properties.

However, in possibility theory, rather than the precise assignment of possibility degrees what matters

is the order of the possibility degrees attached to the different values of the universe of discourse.

Thus, we have opted for the trapezoidal representation owing to its computational efficiency and the

intuitiveness of its semantics for the healthcare staff.

4.2 Representation of Monitoring Criteria

We shall consider time as being projected onto a one-dimensional discrete axis τ = {t0, t1, ..., ts, ...},

where ts represents a precise instant and for every s ∈ N, ts+1 − ts = Δt, being the constant Δt the

minimum step of the temporal axis.

Definition 1 We define P = {P 1, ..., Pm} as the set of physiological variables obtained from the

monitoring of a patient. We consider that each of the physiological variables P p ∈ P is obtained by a

signal sampling process, in the form of a temporal series P p = {(vp[s], t

p[s]); s ∈ N}, where vp

[s] is the

value of P p at the temporal instant tp[s].

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The physician will describe the behaviour of the physiological variables of P using a set of points

defined over their temporal evolution which are of special interest for him/her.

Definition 2 We define a significant point Xpi associated with a physiological variable P p, as the pair

formed by a variable of the domain V pi and a temporal variable T p

i , Xpi =< V p

i , T pi >. Significant

points represent points of the temporal evolution of the variable of special interest for the physician.

In the absence of any constraints, the variables V pi and T p

i of the significant point Xpi may take

any precise value vpi and tpi , respectively, where (vp

i , tpi ) ∈ P p.

Definition 3 Api denotes the assignation of a sample (vp

[s], tp[s]) of the evolution of P p to the significant

point Xpi ; i.e., Ap

i = (vpi , tpi ) = (vp

[s], tp[s]) signifies that V p

i = vpi = vp

[s] and T pi = tpi = tp[s].

We shall use the notation (vp[s], t

p[s]) to refer to any sample of P p, and the notation (vp

i , tpi ) to refer

to a sample of P p which has been assigned to the significant point Xpi .

The physician describes the evolution of a variable by limiting the possible values of each of the

significant points using fuzzy constraints. Given that the knowledge projected in the model is obtained

directly from physicians, it is usually of a descriptive nature. In this sense, experience has shown that

in order to describe the temporal evolution of a set of variables, a set of constraints limiting the fuzzy

temporal extension, fuzzy increment and fuzzy slope between a set of significant points captures a

good number of nuances.

Definition 4 We define a constraint Lpqij over two temporal variables T p

i and T qj by means of the

normal, convex possibility distribution πLpqij (h) over Z, such that ∀ h ∈ Z : πLpq

ij (h) ∈ [0, 1]. Given

a precise temporal duration hpqij , πLpq

ij (hpqij ) represents the possibility of the temporal duration between

T pi and T q

j being precisely hpqij ; thus Lpq

ij represents the fuzzy temporal duration between T pi and T q

j .

In the absence of any other constraints, the precise value assignations T pi = tpi and T q

j = tqj will be

possible if πLpqij (tqj − tpi ) > 0. With the constraints Lpq

ij it is possible to model linguistic descriptions

that limit the fuzzy temporal duration between a pair of events. If both events are defined over

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the temporal evolution of the same variable, Lppij ≡ Lp

ij normally represents a temporal extension

during which the value or rate of change of the variable is constant; e.g. in Fig. 3, L212 models the

linguistic description “increase in HR ... sustained at least for about half a minute”. When events are

defined over different variables, Lpqij describes the temporal layout of the two findings that form part

of the global pattern, thus allowing a fuzzy temporal constraint network to be defined between all the

components of the pattern; e.g. in Fig. 3 L1211 models the linguistic description “the drop in SpO2

must start at approximately the same time as the slight increase in HR”.

These constraints supply the MFTP model with the power to represent temporal information

between instants similar to that of point algebra [39], the only difference being that the MFTP model

cannot represent the constraint “different from”, since its possibility distribution is not convex. The

MFTP model can also represent imprecise quantitative relations between instants, such as those

appearing in the linguistic expressions “just before half an hour” or “no more than 10 minutes after”.

Between intervals, the model makes it possible to represent all 13 base relations from Allen’s algebra

[40], as well as the base relations of the fuzzified Allen’s algebra proposed in [41].

Definition 5 We define a constraint Dpqij over two variables of the domain V p

i and V qj by means of a

normal, convex possibility distribution πDpqij (d) over R, such that ∀ d ∈ R : πDpq

ij (d) ∈ [0, 1]. Given a

precise increment dpqij , πDpq

ij (dpqij ) represents the possibility of the increment between V p

i and V qj being

precisely dpqij ; thus Dpq

ij represents the fuzzy increment between V pi and V q

j .

In the absence of any other constraints, the assignations of precise values V pi = vp

i and V qj = vq

j will

be possible if πDpqij (vq

j −vpi ) > 0. With the constraints Dpq

ij where p = q, i.e. Dpij , it is possible to model

linguistic descriptions that limit variations in the magnitude of a variable; e.g., in Fig. 3 D212 models

the description “slight increase in HR ... of light or moderate intensity”. When these are defined

between significant points that belong to different but commensurate variables, relations between the

magnitudes of both variables can be described; e.g., “the systolic blood pressure is approximately 40

mmHg higher than the diastolic blood pressure”.

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Following the bibliography on temporal constraint networks [42], and with the aim of obtaining a

more compact notation, we define the origin significant point Xp0 = < V p

0 , T p0 > which will allow us to

represent temporal constraints (e.g. “a little after 22:30”) and value constraints (e.g. “approximately

35 mmHg”) as constraints of duration and increment, respectively, relating to the origin significant

point. Any arbitrary value can be assigned to Xp0 . It is habitually assigned the value V p

0 = 0,

T p0 = 0, although it may occasionally be more practical to assign it the value V p

0 = basal(P p), T p0 = 0

where basal(P p) represents the value of the physiological variable P p under normal conditions. In the

medical domain, the different physiological variables for a given patient, under normal conditions and

at rest, normally have approximately constant values. Physiopathological alterations are habitually

reflected in physiological variables as deviations from this basal value - hence the interest in this second

assignation; e.g. in Fig. 3, D101 models the description “fall ... in SpO2 from its basal level”.

Definition 6 We define a constraint Mppij ≡ Mp

ij over two significant points Xpi and Xp

j by means of

a normal, convex possibility distribution πMpij (m) over R, such that ∀ m ∈ R : πMp

ij (m) ∈ [0, 1]. Given

a precise value mpij, πMp

ij (mpij) represents the possibility of the slope of the straight section joining Xp

i

and Xpj being mp

ij ; thus Mpij represents the fuzzy slope between Xp

i and Xpj .

In the absence of any other constraints, the assignations of precise values V pi = vp

i , V pj = vp

j ,

T pi = tpi and T p

j = tpj are possible if πMpij ((vp

j − vpi )/(tpj − tpi )) > 0. With the constraints Mp

ij it is

possible to model linguistic descriptions of the variable’s rate of change; e.g. in Fig. 3, M212 models

the description “moderately sharp increase in HR” where “moderately sharp” is modelled by means of

a high slope value.

In order to obtain a more compact notation for the model, we define a constraint Rpqij between

a pair of significant points Xpi and Xq

j as the set of constraints defined between this set of points;

i.e., if both points are defined over the same physiological variable Rpqij =< Lpq

ij , Dpqij , Mpq

ij >≡<

Lpij , D

pij , M

pij >, otherwise Rpq

ij =< Lpqij , Dpq

ij >.

16

Physicians often define complex patterns recursively: a complex pattern is often made up of a set

of findings and a set of relations between them. Each of the findings of the pattern may also be a

pattern, and may comprise a set of findings and relations between them, and so on. For example, the

embolism pattern comprises three findings (increase in HR, decrease in BP and fall in saturation) and

a set of temporal relations between them, which forces their onsets to be approximately simultaneous.

In turn, each of the three findings are patterns defined as a trend over the temporal evolution of a

physiological variable. This has led us to define the model in a recursive way, as this recursiveness

facilitates the elicitation and maintenance of knowledge, given that the computational representation

of the pattern is closer to the mental model experts have of it. Each finding on each abstraction level

is represented by an MFTP. An abstraction operation is defined as the aggregation of a set of findings

by means of a set of constraints they must satisfy, in order to give rise to a new finding of a higher

abstraction level. The set of MFTPs of an abstraction level are subMFTPs of the MFTP of the next

abstraction level.

Definition 7 We define a Multivariable Fuzzy Temporal Profile (MFTP) M =< WM,XM,RM >

over the set of physiological variables P as a finite set of MFTPs WM = {M1, ...,Ms}, a finite set of

significant points XM = {Xp1i1

, Xp2i2

.., Xpg

ig} and a finite set of constraints RM = {Rpq

ij , 1 ≤ p, q ≤ m,

0 ≤ i ≤ np, 0 ≤ j ≤ nq} amongst the points of WM and XM.

where m is the number of parameters involved in M; np and nq are the number of significant points

defined over P p and P q, respectively. Both the set WM and XM may be empty. The constraints

Rpqij ∈ RM can be defined between significant points belonging to XM, between significant points

belonging to the set of subMFTPs WM, or between both types of significant points. We say that a

significant point Xpi belongs to an MFTP M if Xp

i ∈ XM or Xpi ∈ Mk, Mk ∈ WM. At the lower

abstraction level, MFTPs comprise a finite set of significant points and a finite set of constraints; i.e.,

M =< {∅},XM,RM >.

17

An MFTP can be represented by means of a graph (see Fig. 3) in which the nodes correspond to

significant points, and the arcs correspond to constraints. This graph resembles the morphology of

the pattern it represents, which makes it a valuable visual metaphor for the acquisition of monitoring

criteria.

In the pattern of pulmonary embolism the first level of abstraction is made up of three different

findings, each one of which is a morphology defined over the temporal evolution of a physiological

variable: MSpO2 =< {∅}, {X10 , X1

1 , X12}, {R1

01, R112} >, MHR =< {∅}, {X2

1 , X22}, {R2

12} > and

MBP = < {∅}, {X31 , X3

2}, {R312} > (see Fig. 3). These findings must satisfy a series of tempo-

ral relations (be approximately simultaneous) in order to give rise to a pattern that is compatible

with pulmonary embolism. Thus, the pattern for embolism will take the form: MEmbolism =<

{MSpO2,MHR,MBP }, {∅}, {L1211, L

2311} >, where L12

11 and L2311 force the temporal concurrence of the

three pattern findings.

Optionally, the MFTP model enables us to restrict the evolution of a variable P p between each

pair of significant points Xpi and Xp

j by means of a section constraint Spij represented by a membership

function μSpij (Ap

i , Apj ) which defines a fuzzy course (see Fig. 3) within which the temporal evolution

of the variable must remain in order to satisfy the constraint. The constraint Spij limits the temporal

evolution of a variable between the pair of significants points Xpi and Xp

j , while Mpij limits the values

that those significants points can take. A detailed explanation of the modelling of linguistic utterances

expressing the temporal evolution of a variable can be found in [43].

In the case of the embolism pattern, the samples of each of the sections from the three trends

that make it up must show a certain rate of change with regard to the assignation made to the first

significant point in that section. For example, those appearing between the assignations A21 and A2

2

should show compatibility with M212 taking the assignation A2

1 as a reference (see Fig. 3). Therefore,

18

S212 would be given by:

S212(A

21, A

22) = min

(v2[s],t

2[s]);t

21≤t2[s]≤t22

maxu

{μ(v2[s]−v2

1)∩M212⊗(t2[s]−t21)

(u)}, (1)

where ⊗ represents the fuzzy product of the fuzzy slope M212 by the crisp number t2[s] − t21. The

expression between curly braces evaluates the degree of membership of a signal sample (v2[s], t

2[s]) to

the fuzzy straight line that is given by the constraint M212 and the assignation A2

1 = (v21 , t21). S1

12 and

S312 are given by similar expressions.

4.3 Pattern recognition procedures

The MFTP definition allows the matching task to be structured hierarchically, where a pattern M

constitutes a processing level that incorporates a set of findings detected in the previous processing

level. Identifying a pattern M over the set of the patient’s physiological variables P = {P 1, ..., Pm}

is equivalent to finding a solution to the fuzzy constraint network defined by M. A network solution

is built by means of the assignation Api of a sample of the evolution of P p to each significant point

Xpi . A solution of M is defined as a set of assignations A = {Ap1

i1, Ap2

i2.., A

pg

ig} that satisfy the set of

constraints that make up M, with a degree higher than zero. The degree of satisfaction of A is given

by the minimum degree of satisfaction of all the constraints of M, i.e.:

πM(A) = min{min

MMh ∈WM

{πMMh (AMM

h )}, min

Rpqij ∈RM

{πRpqij (ARpq

ij )}} (2)

where AMMh is the projection of A over the set of significant points involved in MM

h (∀Api ∈ A,

Api ∈ AMM

h ⇔ Xpi ∈ MM

h ), and ARpqij = {Ap

i , Aqj}. πRpq

ij is the degree of satisfaction of Rpqij ∈ RM,

and πMMh is the degree of satisfaction of the sub-MFTP MM

h ∈ WM; it represents the degree

of compatibility between the sub-MFTP and a fragment of the evolution of P . The solutions to

each MMh , and hence πMM

h , are calculated in a previous recognition stage and then assembled to

19

find a solution for M. πM(A) represents the compatibility between a fragment of the evolution of

the physiological variables with the description made in M of a given association between findings of

physiological interest. Thus, for example, in order to calculate the degree of compatibility of the slight

increase in HR finding, MHR, with the set of assignations AMHR

= {A21 = (v2

1 , t21), A22 = (v2

2 , t22)},

the following expression is used:

πMHR

(AMHR

) = min{πL212(t22 − t21), π

D212 (v2

2 − v21)), π

M212 ((v2

2 − v21)/(t22 − t21)), S

212(A

21, A

22)}

where the assignations A21 and A2

2 are taken from the values registered for the HR. A similar

expression is used to identify occurrences of MBP and MSpO2.

After this stage, solutions that satisfy the set of constraints RM are searched for among the

solutions found in the previous stage for the findings of WM, thus obtaining global solutions A for

M. The degree of satisfaction for global solutions is calculated from the degree of satisfaction of the

assembled solutions. Thus, for example, in order to calculate the degree of satisfaction of AEmbolism

the following expression is used:

πMEmbolism

(AEmbolism) = min{πMSpO2(ASpO2), πMHR

(AHR), πMBP

(ABP ), πL1212(t21−t11), π

L2312(t31−t21)}

where ASpO2, AHR and ABP ⊂ AEmbolism.

The modular decomposition in the matching - obtained thanks to a recursive representation of the

model - along with a set of heuristics developed expressly for resolving CSPs in which the domain of

variables is made up of temporal series [44], has permitted the development of matching algorithms

that are fully capable of satisfying real-time requirements. On the other hand, reusing findings in

pattern definition means that it is possible to share information when matching a set of patterns

with common findings. Let us suppose, for example, that an intelligent patient supervision system is

monitoring the occurrence of hypovolemia and embolism patterns. The latter can be represented by

20

means of MHypovolemia =< {MHR,MBP ,MSpO2′}, {∅}, {L1231, L

2311, L

1242, L

1342} > (see Fig. 4) where

the findings MHR and MBP and the temporal relation between them L2311 are the same as those that

form part of the pattern for pulmonary embolism; MSpO2′=< {∅}, {X3

0 , X33 , X3

4}, {R303, R

304, R

334} >

represents the finding “SpO2 remains approximately constant and at a value close to the basal one”;

the constraint L1231 forces the temporal coincidence of the onset of the finding on SpO2 with the other

two findings; and the constraints L1242 and L13

42 force the SpO2 to maintain a basal value subsequent

to the respective increase in HR and fall in BP. In order to monitor the occurrence of both patterns,

the supervision system can reuse the information common to both of them, so that it only needs to

identify the occurrence of the findings MHR and MBP and verify the constraint L2311 only once (see

Fig. 4).

4.4 Knowledge acquisition with TRACE

TRACE (see Fig 5) is a graphical tool that provides healthcare staff with a set of utilities and

wizards for the modelling of monitoring criteria defined as patterns over the temporal evolution of a

patient’s physiological variables [45]. These patterns comprise a set of findings, each one of which is

a morphology defined over the temporal evolution of a physiological variable in a patient, and a set

of relations between these findings.

TRACE handles each finding - and, generally, any pattern - as a graph whose nodes represent

significant points, and each arc represents a set of constraints between the significant points it connects

(see Fig 6). The graph can be built up from scratch by adding significant points and constraints to an

initial empty graph. Constraints are modelled visually: each one is defined as a possibility distribution

which can be edited by clicking over the arc representing it. A simple graphic interface enables the

user to edit the trapezoidal representation of each possibility distribution (see Fig 7), and changes are

automatically reflected in the graph, in such a way that its shape reflects the morphology of the finding

it represents. This instantaneous visual feedback on the monitoring criteria being described is based

on the ecological interface design principle [46], according to which a user interface must represent

21

the mental model that a user has of the system, and is a powerful tool for the simple identification of

pattern definition errors.

A morphological finding can also be defined using a set of simple morphology templates (peaks,

pulses, etc.) from among which users can choose the one which best approximates the finding they

wish to model. Selecting a template automatically generates a graph with its corresponding shape,

and whose constraints take generic values that the physician must edit in order to describe the finding

properly. TRACE also incorporates a wizard for visual modelling with which users can select a

signal fragment in which a morphology of interest appears (see Fig 8), and they set the significant

points over the selected fragment with the mouse. Then, for each significant point, they select the

temporal interval in which any trajectory is completely compatible with the desired morphology,

and the interval outside of which compatibility is null. Similarly, for each significant point, the user

indicates the tolerance in the magnitude of a trajectory that is compatible with the finding. On the

basis of this information, the wizard constructs a prototype pattern with total compatibility with the

finding, and which can subsequently be edited and revised by the physician.

The relations between findings identified over different physiological variables are fundamental for

an accurate interpretation of the patient’s state. A particular temporal arrangement between these

findings may be the determining factor in identifying an abnormal situation, or in differentiating

between different abnormal situations. With TRACE, these types of constraints can be defined in a

simple visual manner as possibility distributions, using the same mechanism proposed for the modelling

of findings.

Usually, to define certain monitoring criteria the user starts with a signal recording where these

criteria have occurred. Using the wizard for defining morphological findings (see Fig 8), he/she creates

an initial prototype of each of the morphological findings encompassed in the monitoring criteria.

Then, using the knowledge editor of TRACE (see Fig 6), he/she edits the possibility distributions

that describe the findings (see Fig 7), so they capture the criteria to be described. If a signal recording

where the monitoring criteria have occurred is not available, the definition of the findings can be carried

22

out with the aid of the set of morphology templates provided by TRACE. The disadvantage of using

the templates is that they only capture a qualitative description of the findings, while the wizard not

only captures a qualitative description, but also a quantitative approximation.

Finally, using the knowledge editor (see Fig 6), the user defines the relationship between the

different findings; often these relationships force a certain temporal layout of the beginning of each

finding (e.g., concurrence). A series of flash videos showing the support that TRACE supplies for the

definition of certain monitoring criteria can be found at [47].

Monitoring recordings can be loaded in TRACE from files in ASCII or MIT-BIH format [48]

and they can be searched for a number of monitoring criteria. As a result of this search over each

physiological variable, those signal fragments showing some compatibility with the finding defined

over the variable are colour-coded, indicating the level of similarity in the identification, and a signal

(which we call Detection) is added to the environment, indicating (with a percentage) the possibility

of the monitoring criteria being complied with at each instant (see Fig 5).

5 Validation of the proposal

We have performed a clinical validation of a set of 16 alarms represented by means of the MFTP

model and identified by means of its algorithms, over 196 hours of recordings obtained from intensive

care patients. We also have performed a validation of the usability of TRACE for the design of this

sort of alarms, by asking five five physicians and four nurses to use TRACE to define several alarms,

after they attended a training seminar on the tool. The results from both validations are presented

in this section.

5.1 Alarm performance

Through the use of TRACE, and with the support of a medical team, we have defined a set of alarms

checking monitoring criteria that cannot be suitably represented by means of thresholds. Alarms did

23

not necessarily correspond to a finding of a pathology, even though they all identified the occurrence

of events which were of interest for the physicians, and which could not be satisfactorily identified by

the alarms currently available in commercial monitoring devices.

A number of these alarms supervise the occurrence in a physiological variable of episodes, lasting

at least 4 minutes, of abnormal values with light to moderate severity: an abnormally low value

in (1) blood oxygen saturation (LV SpO2), (2) heart rate (LV HR), (3) blood pressure (LV BP)

and respiration rate (LV RR); and an abnormally high value for the heart rate (HV HR), (2) blood

pressure (HV BP), (3) and respiration rate (HV RR). Others supervise trends in a variable; i.e.

sustained moderate increase or decrease lasting at least 45 seconds: rise in (1) blood pressure (I BP),

(2) respiratory rate (I RR); and a fall in the blood oxygen saturation (D SpO2).

Some alarms incorporate information originating from two variables: a rise in the heart rate ap-

proximately simultaneous with (1) a decrease in blood pressure (I HR-D BP), (2) a drop in the blood

oxygen saturation (I HR-D SpO2), (3) an increase in the blood oxygen saturation (I HR-I SpO2) and

(4) and increase in blood pressure (I HR-I BP); and an increase in the respiratory rate approximately

simultaneous with a decrease in the blood oxygen level (I RR-D SpO2); and an increase in the respi-

ratory rate simultaneous with a decrease in the blood oxygen saturation (I RR-D SpO2). In this case

the abnormality values that are supervised over each variable are lower than for alarms which only

consider information from one variable.

Approximately 196 hours of recordings of physiological variables from 78 different patients admitted

to the ICU in the University Hospital of Elche were used to validate the alarms. Recordings varied in

length (from barely 20 minutes to over 12 hours) and not all of them contained the same variables.

The software used to store the recordings electronically was SUTIL [49]. With this system it is only

possible to register the data from one patient at a time. Owing to these limitations, the physicians

often opted to record only the patients’ crisis periods, in which there is usually a high number of

relevant events. Consequently, the 196 hours of monitoring cannot be considered representative of the

average state of a patient admitted to an ICU; rather they correspond to their worst moments.

24

TRACE was used to run the matching procedures, and the results of the validation are given in

Table 1. The generation of an alarm in a context which physicians “would not have wished to be

alerted” due to their considering a more detailed examination of the patient unnecessary, is taken as

a false positive. A correct detection is taken to be the generation of an alarm in a context in which

physicians would have wished to be alerted, considering a more detailed examination of the patient

to be necessary. In order to judge whether the results from the tool correspond to false positives or

to correct detections, physicians were also asked to take into account the degree of compatibility that

the alarm showed. For example, if TRACE generates an alarm and, based on the temporal evolution

of the physiological variables involved, the physician is not strongly convinced of either carrying out a

more detailed examination, or of ignoring the alarm, the alarm should be considered a false positive if

it has given a high degree of compatibility; nevertheless, if it has supplied a low degree of compatibility

it should be considered as a correct detection.

The tests were carried out using the same monitoring criteria for all 78 patients. In clinical

routine, the criteria must be customized for the monitoring objectives of each patient using TRACE’s

pattern editor. Tests were carried out in this manner as, with the exception of the signal recording,

on the immense majority of the 78 patients we had no information with which to carry out the

individualization. On average, processing an hour of signal recording to identify one of the alarms

that describe a trend requires about half a second. Identifying alarms that describe the concurrence

of two findings over a one-hour recording takes around two seconds. All variables were sampled at 1

Hz, and the tests were carried out on a Pentium IV running at 2.4 GHz.

5.2 Alarm definition

In order to validate the usability of TRACE we requested the cooperation of five physicians and four

nurses. Most of them work in an ICU, and all of them had experience in patient monitoring. None of

them was familiar with our work, nor had any previous experience with TRACE.

Before conducting the tests, the healthcare staff attended a brief seminar where the core concepts

25

underlying our proposal to create alarms and the basic use of TRACE were presented. The first part

of the seminar was intended to provide intuitive definitions of several fuzzy set theory concepts; mainly

the concept of degree of membership and the semantics of a trapezoidal possibility distribution. Then,

how signal patterns can be described by using fuzzy increments, fuzzy durations and fuzzy slopes that

limit the evolution of the signals between a set significant points was shown.

The rest of the seminar was intended to present the operation of TRACE, and it covered how to

perform measurements over the signals, several signal visualization features such as zooms, the use

of the wizard for the visual modelling of morphologies, and how to edit the trapezoidal possibility

distributions which represent fuzzy increments, fuzzy durations and fuzzy slopes. During this part

of the seminar, an instructor built an alarm from scratch with the tool. All the actions carried out

by the instructor were also explained in a manual of TRACE containing abundant screenshots which

had been given to the attendees at the beginning of the session. Once the instructor had completed

the definition of the sample alarm, the attendees were asked to define the same alarm again on their

own, starting from scratch. During this exercise they could request the assistance of the instructor or

consult the manual.

Then, each of them was asked to design several alarms that could not be adequately represented by

threshold criteria -the pulmonary embolism and hypovolemia alarms used as examples in this article,

an alarm defined as a sustained rise in the HR approximately simultaneous with a drop in SpO2,

and an abnormally low value in HR lasting at least 4 minutes. The attendees were provided with a

signal recording for each of the alarms that contained at least one alarm occurrence. Each description

of an alarm had to be performed from scratch and with the assistance of the wizard for defining

morphological findings. After its definition, the alarm had to be validated over its corresponding

recording. During the definition of these alarms healthcare staff did not have the support of the

instructor, but they could consult the TRACE manual. Although some of the attendees needed more

than one attempt to define some of the alarms, they were all capable of correctly defining them all.

After finishing the exercises, the attendees were asked to fill in the standard system usability scale

26

(SUS) test [50]. This test provides a scale to measure the usability of a system. The scale ranges from

0 to 100; usually a score of 50 or more is considered as user-friendly. TRACE’s average score was 68.2.

The average score between the five physicians was 76.8, whereas among the four nurses was 57.5.

6 Discussion

The results of the validation presented herein show the following: (1) alarms defined with the MFTP

model make it possible to automatically supervise monitoring criteria that are of interest for healthcare

staff and which cannot be identified with threshold alarms; (2) they show a number of false positives

that is more than acceptable when compared with the alternatives currently available; (3) the efficiency

of the matching procedures means that they can be applied to real-time monitoring tasks, (4) and the

tool TRACE is a viable solution to elicitate the knowledge required to create the alarms.

Nevertheless, there remain a good many questions to which we still do not have the answer. Though

we believe that the alarms proposed in this work make it possible to monitor situations of clinical

interests that are not suitably covered by threshold alarms, we do not know exactly which and how

many alarms, among those habitually used in critical care units, could be substituted by alarms defined

with the MFTP model. In certain cases, the new alarms will probably make it possible to identify

all situations of life-threatening risks that can be identified by the corresponding threshold alarms,

but with a much lower number of false positives, which would make replacing the old alarm with the

new one advisable. In other cases, threshold alarms monitor situations that cannot be satisfactorily

monitored by more complex alarms based in a morphological description of the temporal evolution of

one or more physiological variables.

We believe that a large proportion of alarms that supervise the upper limits of most physio-

logical variables could be substituted by alarms allowing increasing trends to be identified over the

corresponding variable. The physiological mechanisms which the physiological variables reflect often

prevent the variables from showing increases that are sharper than a given rate of change, or which

27

reach values that are higher than a given limit. For example, a HR value of 500 bpm or an increase of

50 bpm to 150 bpm in just one second are clearly false positives; in the first instance, as the human

heart is not capable of beating so fast; in the second because the heart’s response rate does not permit

an increase of 100 bpm in just one second. Thus, threshold alarms monitoring the maximum values

permitted for most variables could be substituted by alarms that identify increasing trends that reach

a given critical value in the variable, but which reject those increasing trends with a rate of change

clearly exceeding that permitted by the dynamics of the variable by interpreting them as artifacts.

We also believe that it will probably not be possible to replace a good number of threshold alarms

that supervise the lower limits of most physiological variables with trend-based alarms. Even though

for example, the heart cannot go from 50 bpm to 150 bpm in just one second, it can go from 150 bpm

to 0 bpm in instant if the patient suffers heart failure. Even though it could be possible to resort to

the integration of information from various related variables to reduce the number of false positives

caused by artifacts, the mechanism that could warn of the total failure of an organ with the greatest

speed is, in all probability, the threshold alarm.

Our preliminary validation has shown that, after a short period of training, it is possible for the

healthcare staff to define alarms without assistance using the tool TRACE, which allows us to be

optimistic regarding the viability of its use in the clinical routine. Nevertheless, the impact of the

greater time required to define the alarms must be studied carefully. Whether the MFTP model is

used to create the alarms, or another equivalent solution for creating alarms with greater semantic

content is used, the greater expressive power of the alarms will require more time to define each alarm.

It may well be the case that the benefits obtained from the new alarms do not offset the increased

complexity and the time required for the definition and, thus, healthcare staff may opt not to use them.

In the case of our proposal, users with experience using TRACE can define an alarm that supervises

monitoring criteria that relate two different findings over two physiological variables in less than 2

minutes, if templates are used. While it may not appear to be too long, it is considerably higher than

the 10-20 seconds required to set a threshold alarm. Moreover, this situation may be compounded

28

if, when the new alarms are incorporated into the clinical routine, the total number of alarms in the

ICU (the new ones plus the threshold alarms that still are required) increases significantly.

The impact of the higher costs to train the healthcare staff to learn how to use the new alarms

should also be studied in detail. In this sense, our validation suggests that physicians are able to

learn new techniques more rapidly and with greater ease than nurses: the SUS average score for the

nurses was 57.5, whereas for the physicians was 76.8. These results are consistent with the general

impression that the instructor perceived during the training seminar: it was usually harder for the

nurses to master new concepts -especially those related to the fuzzy set theory- as well as remembering

how to access a particular functionality within TRACE.

Another open question is how to alert healthcare staff to alarms that have different degrees of

certainty regarding the absence or presence of the signs they are monitoring. Even though we do

believe that the use of visual metaphors, such as the colour-coded lines used by TRACE, is a suitable

solution on screen, some type of auditory warning to call the attention of the healthcare staff in

a critical care unit is essential. The use of different tones or melodies to reflect the alarm’s level of

compatibility does not seem suitable: currently healthcare staff do not recognize a good number of the

alarm tones that are already present in a critical care unit [51]; hence, introducing more tones would

not seem to be a good idea. One possible solution could be to use voice synthesis, so that the patient

supervision system could warn of a “very slight tachycardia” or a “moderate hypotension”, although

a more in-depth study of the implications of the solution is needed. Sonification (the science that

studies the conversion of data into sound) may be another way of representing this information [52].

In sonification, each of the dimensions of the data being monitored is assigned an acoustic parameter,

such as pitch, loudness, speed, harmonic content, etc. Applied to patient monitoring, the sound of

a heart beating faster and faster during a short time interval, which then starts from the beginning

again, could be used to indicate a rise in HR with low degree of possibility; the longer the time during

which the heart is beating faster and faster the higher the possibility associated with the alarm would

be.

29

7 Conclusions and future work

We have presented a proposal for constructing alarms that aims to mitigate the cognitive overload

borne by healthcare staff due to the enormous volume of physiological variables monitored in critical

units. The solution proposed is based on the MFTP model for projecting the monitoring criteria that

an alarm must supervise onto a computational representation. The MFTP model represents these

criteria as a set of fuzzy constraints between a set of points - which are of special relevance for the

physician - defined over the temporal evolution of physiological variables. Fuzzy constraints make

it possible to limit the evolution of the variables between these points in a flexible manner. In our

proposal the monitoring criteria are acquired and edited using TRACE, a graphical tool that uses

the graph corresponding to an MFTP as a visual metaphor to simplify the acquisition and editing

processes.

The alarms defined with this proposal (1) show different degrees of compatibility between the

monitoring criteria and the evolution of physiological variables; (2) they make it possible to reason

over the temporal evolution of variables; (3) they can integrate information coming from multiple

variables into one single alarm; and, in spite of their high semantic content,(4) they can be edited by

physicians in a simple manner without the need for assistance, thanks to the use of visual metaphors

in their definition.

We have carried out a validation of the proposal in which a total of 16 different alarms were

searched for over 196 hours of signal recordings from 78 different patients. These alarms supervised

monitoring criteria that could not be supervised with thresholds. On average, the time required to

process one hour of signal recordings sampled at 1 Hz is one second. Only 7% of the alarms generated

were false positives, a much lower percentage than for threshold alarms.

We also have carried out a validation of our solution for creating and editing alarms -the tool

TRACE. Five physicians and four nurses with no previous experience of our proposal designed several

alarms with TRACE after receiving a training seminar on the tool. Then they were asked to fill in

30

the standard System Usability Scale test to evaluate the usability of the tool. The score was 68.2; i.e.,

the tool can be considered user-friendly.

Our future work is aimed towards applying the proposal presented herein in a pilot experiment,

using an intelligent patient supervision system in an Critical Care Unit in order to validate our

solution in a real setting. This will also make it possible to further study which threshold alarms may

be substituted by alarms defined with our proposal, how threshold alarms and the new alarms could

be used side-by-side, what type of auditory warnings should be used to indicate the different degrees

of compatibility, and the impact of requiring more training for the healthcare staff and more time to

define each alarm.

Prior to this, we intend to construct a monitoring pattern catalogue based on patterns from the

temporal evolution of physiological variables and/or on the combination of multi-variable information.

The MFTP model and TRACE both support the objectivization, transmission and validation of

monitoring criteria, which makes them ideal tools for constructing a catalogue of this nature. This

catalogue will serve as a basis for designing a set of monitoring templates, which will provide support

for defining alarms in the pilot experiment.

ACKNOWLEDGMENTS

We thanks the Drs Remedios Lopez Serrano, Romina Carreno Ponfil, Elena Gonzalez Gonzalez,

Esteban Javier Fernandez and Sergio Valenzuela, and the nurses Ana Mancha Quesada, Melissa Parcia

Villarubia, Mar Sanchez Sanchez and Sonia Hernandez Fabian from the Universitary Hospital of Getafe

for their collaboration in the validation of the tool TRACE. We also wish to acknowledge the support

by the Spanish Ministry of Education and Science (MEC), the European Regional Development Fund

of the European Commission (FEDER) under the grant TIN2006-15460-C04-02; by the Xunta de

Galicia under the grant PGIDIT08SIN002206PR; and by the University San Pablo CEU under the

grant USP-PPC 04/07.

31

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8 Tables

Table 1: Results of the validation.Alarm C FP %C %FPLV SpO2 37 2 95 5LV HR 39 3 93 7LV BP 36 2 95 5LV RR 21 2 91 9HV HR 62 1 98 2HV BP 78 3 96 4HV RR 56 5 92 8I HR 119 12 91 9I BP 91 9 91 9D SpO2 136 12 92 8I HR-D BP 35 2 95 5I HR-D SpO2 21 2 91 9I HR-I SpO2 36 4 90 10I HR-I BP 72 5 94 6I HR-I RR 58 3 95 5I RR-D SpO2 15 0 100 0Total 912 67 93 7

C: Correct; FP: False Positive. Percentages are calculated with regard to the total number ofdetections.

38

9 Figure captions

Figure 1: Manifestation of a pulmonary embolism (left) and of a hypovolemia (right).

Figure 2: Trapezoidal possibility distribution.

Figure 3: Graph of the MFTP representing an embolism traced over an occurrence of the pathol-

ogy.

Figure 4: Graphs of MFTPs representing pulmonary embolism and hypovolemia. The part

common to both MFTPs is highlighted.

Figure 5: TRACE showing the detection of a pulmonary embolism.

Figure 6: TRACE’s knowledge editor, showing the graph corresponding to the MFTP for pul-

monary embolism.

Figure 7: Window enabling the edition of the constraints between a pair of significant points.

Possibility distributions can be edited graphically, with the mouse, or textually using text fields.

Figure 8: Wizard for defining morphological findings. The horizontal and vertical lines in black

represent possibility distributions showing the deviation admissible for an evolution of the variable

that is compatible with the finding.

39

Heart rate

SpO2

Bloodpressure

Bloodpressure

Heart rate

Pulmonary embolism Hypovolemia

SpO2

Figure 1: Manifestation of a pulmonary embolism (left) and of a hypovolemia (right)

.

40

1

0

b g da

pC

Figure 2: Trapezoidal possibility distribution.

41

Heart rate

SpO2

Bloodpressure

McreaseHR in

Membolism

MBP decrease

MecreaseSpO2 d

X2

1

X2

2

X1

2

X1

1

X3

2

X3

1

X1

0

D1

01

L12

11

{1 1 1

}D12, 12 , 12L M

{2 2 2

}D12, 12 , 12L M

{3 3 3

}D12, 12 , 12L M

L23

11

Heart Rate

Blood Pressure

X13

X23

X12

X22

SpO2

X11

X21

S121

S122

S123

Figure 3: Graph of the MFTP representing an embolism traced over an occurrence of the pathology.

42

X21

X11

X12

X22

Heartrate

Bloodpressure

McreaseHR in

Membolism

MBP decrease

X1

0

X13

X23

SpO2

MecreaseSpO2 d

X41

X12

X22

McreaseHR in

MBP decrease

X13

X23

MSpO2 stable

X31

D1

03 D1

04 L12

42

L13

42L12

31

Mhypovolemia

L12

11

{1 1 1

}D12, 12 , 12L MD011

{2 2 2

}D12, 12 , 12L M

{3 3 3

}D12, 12 , 12L M

L23

11

{1 1 1

}D34, 34 , 34L M

{3 3 3

}D12, 12 , 12L M

{2 2 2

}D12, 12 , 12L M

X01

L23

11

Figure 4: Graphs of MFTPs representing pulmonary embolism and hypovolemia. The part commonto both MFTPs is highlighted.

43

Figure 5: TRACE’s showing the detection of a pulmonary embolism.

44

D1

01

L12

11

{1 1 1

}D12, 12 , 12L M

{2 2 2

}D12, 12 , 12L M

{3 3 3

}D12, 12 , 12L M

L23

11

Figure 6: TRACE’s knowledge editor, showing the graph corresponding to the MFTP for pulmonaryembolism.

45

Figure 7: Window enabling the edition of the constraints between a pair of significant points. Possi-bility distributions can be edited graphically, with the mouse, or textually using text fields.

46

Figure 8: Wizard for defining morphological findings. The horizontal and vertical lines in blackrepresent possibility distributions showing the deviation admissible for an evolution of the variablethat is compatible with the finding.

47