Use of the Heart Rate Variability as a Diagnostic Tool

Raquel Gutiérrez Rivas

, Juan Jesús García Domínguez

and William P. Marnane

Electronics Department, University of Alcalá, Alcalá de Henares (Madrid), Spain

Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland

1 RESEARCH PROBLEM

The electrocardiographic signal represents the

electrical activity of the heart. It has several nodes

able to generate synchronized electrical impulses to

sequentially activate its valves. All this impulses

overlapped form the well-known QRS complex

(Figure 1). Usually, the position of the R peak is

taken as the instant in which the heartbeat has place.

Thus, to determine the heart rate it is necessary to

find all the R peaks present during the measurement

of the ECG signal.

Heart Rate (HR) is controlled by the

Autonomous Nervous System (ANS), which is

composed by the Sympathetic Nervous System

(SNS) and the Parasympathetic Nervous System

(PNS). Both of them, SNS and PNS, respond to the

necessities of the rest of physiologic systems

(thermoregulatory, vasomotor, respiratory, central

nervous, etc. systems) which make possible to

correlate variations in the HR with the performance

of all those systems. In short, due to the easiness

with which is possible to obtain the ECG signal, and

taking into consideration that is taken through a non-

invasive measurement, several parameters of it have

been studied for helping to the diagnosis of several

diseases and as a tool to study the patients’ fitness.

However, to study carefully the performance of

those physiological systems, in most of the cases it

is not only enough to know just the HR, but also the

Heart Rate Variability (HRV), which is the focus of

the study carried out through this thesis.

HRV signal represents the variation of the heart

rate beat-to-beat, i.e. represents all the durations of

the intervals between adjacent R peaks, or RR

intervals. Thus, it is extremely important to correctly

detect all the R peaks. Most of the times, this is not a

trivial task as some noises and artefacts that affect

the ECG signal difficult that detections. The most

important of those noises and artefacts are the

electromyographic noise, produced by the activation

of the thorax muscles, and the artefacts produced by

the movement of the electrodes. There are more

noises (respiration noise, 50/60 Hz noise, etc.) as is

stated by Friesen et al in (G. M. Friesen et al., 1990),

but the peaks produced but electromyogram and

those produced by the movement (motion artefacts)

are very similar to the R peaks, so their presence can

provoke false detections. In general, to avoid those

false detections, time constrains are used, setting an

interval in which after an R peak detection, a new

heartbeat cannot be produced (in this work, a

minimum RR interval has been set to 300 ms. which

corresponds to 300 bpm).

Figure 1: QRS complex peaks and segments and their

correspondences with heart valves and muscles.

A great number of R peak detection algorithms

have been proposed during the lasts 30 years.

However, with the increasing computational

capabilities of the computers, those algorithms tent

to be more and more complex by using filters and

tools with a large number of computations in order

to avoid all the sources of noise and artefacts.

Nevertheless, in the real-time monitoring area, in

which portable devices are used, complex algorithms

can not be used due to the fact that those portable

devices must have a long life battery, and they have

a limited number of resources. Also, R peak

detections algorithms only show the position of the

R peaks, but HRV signal must be analysed on one

manner or another to study different kinds of factors,

so the R peaks detection algorithm are the basis of

other algorithms, and they must be implemented

together with the main algorithm. That is why, in

real-time monitoring applications, R peaks

detections must be as simple as possible. For those

reasons, a new R peaks detection algorithm is

proposed in this thesis. This algorithm is able to

work in real-time on reduced resources platforms

obtaining a good performance in terms of sensitivity

Gutiérrez Rivas R., García Domínguez J. and P. Marnane W..

Use of the Heart Rate Variability as a Diagnostic Tool.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

and positive predictivity.

Once we have the R detections problem solved,

we can access to the HRV signal features which can

be related to several physiological systems

performance. For instance, as will be commented

later on, the study of the respiration rate through the

HRV signal has led to the study of the sleeping

phases or to the development of several apnoea

detectors; relationship between HRV signal and the

Central Nervous System has been used to study the

mental state in order to detect stressing situations or,

even, to classify the mood of the subjects (anger,

confusion, happiness, anxiety, etc.); in the neonatal

context, this signal has been used to detect seizures,

or pain episodes of new-born infants, or even, to

perform an assessment of the foetal health state; etc.

In this thesis we study the relationship between

changes on the HRV signal and the presence of

allergic reactions to drugs or food. Today, there is a

process called provocation test to detect this kind of

allergies. The steps followed during this test are:

1. When the patient arrives to the hospital, his basal

state is established by measuring blood pressure,

heart rate, SpO2 and the volume of the expired air

(peak flow).

2. The suspected allergen is divided into several

doses of different sizes (the number of doses and

their sizes depend on the allergen). The smallest

dose is then administered to the subject.

3. After an observation period of 30 or 60 minutes

(again, depending on the allergen) if the patient does

not present any symptom, the next smallest dose is

administered to him.

The process continue until all the doses are given

to the subject. When the allergen is finished, there is

an observation period of 120 minutes. If nothing

happens to the subject, neither in the next 24 hours,

it is classified as non-allergic. In the case in which

any symptom appears during the tests, the symptoms

are treated, and the patient is classified as allergic.

Figure 2 summarizes a generic provocation test.

As can be deduced, this process is not risk free:

even with the constant observation of the medical

staff, the allergic reaction can be of different levels

of dangerousness: from the appearance of hives, or

conjunctivitis, to breathing difficulties or, even,

anaphylaxis. It is neither possible to know which

patient may suffer a more or less dangerous reaction,

nor the kind of the reaction. So it is really a complex

and dangerous process. Through a previous study

carried out by the research group, we have

concluded that there is a relationship between

changes on several patients’ HRV signal and a

posterior occurrence of allergic reactions. Thus, the

objective is to employ the measurement and analysis

of the HRV signal to detect allergic reactions. In this

way, it is possible, first of all, the automation of the

process, so the medical staff could receive an alert as

soon as the allergic reaction is detected by analysing

the HRB; and, secondly, the number of doses needed

during the test could be reduced. Due to the fact that

the lasts doses are the biggest and more dangerous,

we could reduce the risk to which patients are

exposed during the provocation tests. The rest of this

paper is organized as follows: Section 2 lists the

main objectives of the presented thesis; Section 3

introduces the state of art regarding QRS complex

detection algorithms and application fields of HRV

as a diagnostic tool; Section 4 introduces the

proposed algorithms; Section 5 explains the

expected outcome and finally, Section 6 states the

stay of the research.

2 OUTLINE OF OBJECTIVES

The main objective of this thesis is the study of the

HRV signal to demonstrate its worth as an

Start

Treatment ofsymptoms

Allergic

Dose remaining?

Administer dose

Observefor 30or 60min.

Observefor 90‐120min.atthe hospital

Reaction?

YES

Initial Check‐up

Non‐allergic

Observefor 24h.athome

Reaction?

YES

Figure 2: Provocation tests flowchart.

BIOSTEC2015-DoctoralConsortium

alternative or a helping tool to diagnose and control

health problems of different natures. To reach this

objective, it is necessary:

1. To design an R peak detection algorithm

capable to work in real-time and to be implemented

on low-cost devices.

2. To study the HRV of patients (allergic and

non-allergic) during provocation tests.

3. To design an algorithm that, based on the

HRV signal, detects the presence of allergic

reactions before the appearance of physical

symptoms.

Besides, it is planned to propose another interesting

use of the HRV monitoring as a diagnostic tool for

its use with diabetics

3 STATE OF THE ART

3.1 QRS Complex Detection

R peak detection algorithms can be divided into two

stages (Figure 3). The first phase (pre-processing)

processes the ECG signal in order to reduce or

remove the most part of the artefacts and noises

commented above. At the second stage, the ECG

signal is analysed to find the R peaks.

Pre-processing stage:

noise/artefact reduction

R peaks/ QRS complex

detection

RRR

Figure 3: Typical structure of a QRS complex detection

algorithm.

3.1.1 Pre-processing Stage

Some of the most common techniques used to pre-

process an ECG signal are the following ones:

- Wavelet transform (M. W. Phyu et al., 2009; a.

Ghaffari et al., 2008): Through this technique,

ECG signal can be divided into a set of basic

functions and the ones that do not provide valid

information are removed.

- Hilbert transform (C. Xiaomeng, 2011; R.

Rodriguez et al., 2013): An interesting property

of this technique is that it produces a zero

crossing every time there is an inflexion on the

raw signal (as in the R peaks case). However the

features of some artefacts are very similar to the

R peaks detected in this way, so they can be

misinterpreted.

- EMD (S. Pal and M. Mitra, 2012; S. Pal and M.

Mitra, 2010): As wavelet, this is a decomposition

technique in which ECG signal is divided into

oscillatory functions (Intrinsic Mode Functions,

IMF). Some authors combine EMD with

Wavelet (M. K. Das et al., 2011; B. Khiari et al.,

2013) as well as with Hilbert (S. Kouchaki et al.,

2012; M. Zhang and C. Zhang, 2010) to improve

the performance of the pre-processing stage.

- Differentiation based computations (J. Moraes et

al., 2002; M. Adnane et al., 2009): These

techniques are based on the well-known Pan and

Tompkins (J. Pan and W. J. Tompkins, 1985)

algorithm. Although these are the most efficient

techniques from a power consumption and

computational complexity point of view, they

require a more complex detection stage.

Most of this techniques are sometimes combined

with different kinds of high and low pass filters, not

without a level of complexity.

3.1.2 Detection Stage

Regarding the detection stage, most of the

algorithms use one or more fixed thresholds to

detect the R peaks. This is a good technique only if

the processed ECG signal is very clear, without

noise or. However, this is not the most common

situation so, usually, this threshold or thresholds

have to be adaptive i.e. their amplitude should

change depending on the features of the ECG signal.

Furthermore, time intervals are usually employed for

which it has to be considered the maximum and

minimum possible heart rates (usually 220 – 40

bpm), and the minimum and maximum RR intervals.

Another technique used to detect the R peaks, is the

analysis of the QRS complex morphology (slopes

analysis, position of the peaks and intervals, etc.);

nevertheless the morphology of a QRS complex

depends on the position of the electrodes and,

mainly, on each person. Actually, the morphology of

a person’s QRS complex can change during a

measurement, what invalidates this technique.

As is commented below, in most of the proposed

R peak detection algorithms, the main concern of the

authors is the completely removal of all the noises,

without taking into account the computational

complexity. That fact, make most of them unable to

be implemented on battery-driven devices. In the

area in which this research is carried out it is

extremely important to reach a trade-off between

performance and computational efficiency.

3.2 HRV Study Applications

The use of the HRV signal to study some diseases or

UseoftheHeartRateVariabilityasaDiagnosticTool

the analysis of the health state of a person is being

used more and more lately. The main reason is the

previously mentioned relationship between this

signal and almost all the physiologic systems,

through the ANS. The next selection of works

represents the use of the HRV signal in different

areas:

- The most direct use of the HRV signal is to

optimize high performance trainings or to

analyse the state of subjects during stress tests

(R. Bailón et al., 2013: R. Bailón et al., 2011).

Through the HRV it is possible to obtain

information related to the effort made, energy

expenditure, the oxygen, fat and carbohydrates

consumption, or the recover ability of each

individual. So, a physical trainer is able to adjust

each training session in a personalized way,

obtaining the best results from each participant.

- Another important application can be found on

the study of people’s mood trough HRV signal

(M. Kumar et al., 2007; P.-Y. Hsieh and C.-L.

Chin, 2011). Due to the relationship between the

HRV and the CNS, it is possible to infer if a

person is stressed, relaxed, happy, depressed, etc.

This can be used to improve the quality of life of

all the workers in an office and, thus, their

throughput.

- A novel and very interesting application of the

HRV signal is the detection of pain episodes (J.

De Jonckheere et al., 2011; A. Fanelli et al.,

2013) and seizures (M. B. Malarvili and M.

Mesbah, 2009) of neonatal and, even, foetal

patients. In this way, just measuring and

analysing their ECG signal, it is possible to

reduce their mortality.

- Due to the relation between the HRV signal and

the respiration rate, it is possible to automatically

detect apnea episodes (J. Hayano, et al., 2011; E.

Gil et al., 2009) or analyse the quality of the

sleeping phase (S. Eyal et al., 2012).

4 METHODOLOGY

As is commented above, the main objective is to

analyse the HRV signal in order to relate its changes

with an abnormality, as can be the presence of an

allergic reaction. Another constrain is the realisation

of this analysis in real-time. For that reasons, one of

the basis of this thesis is the design of a real-time R

peaks detection algorithm capable to work on

portable devices. Once we are able to obtain the

positions of all the heartbeats, the next steps will

consists in the study of the relationship between the

changes on the HRV signal and the situations at

which the subjects are exposed.

4.1 QRS Complex Detection Algorithm

Each of the stages of the designed algorithm have

been optimized to reach the “low-cost”

requirements. Figure 4 shows the block diagram of

the proposed R peak detection algorithm.

Preprocessing stage

Threshold stage

Figure 4: R peaks detection algorithm’s blocks diagram.

This algorithm is based on the Pan & Tompkins

algorithm, mainly the pre-processing stage. The first

stage processes the ECG signal as follow: First, raw

ECG signal is derived by using (1). This operation

reduces the low frequency noises (as the one

produced by the respiration). Then an integration

window (2) is used to reduce the high frequency

artefacts; and, finally, all the samples are squared (3)

in order to empathize the R peaks, while reducing

the noise peaks. Results of the pre-processing stage

are shown in Figure 5.

[] [] [ ]

y n xn xn N

(1)

with





128

N round

[] [ ]

yn yn k









(2)

with



128

N round





[] []yn y n

(3)

Once the noises and artefacts have been reduced,

next steps consist in detecting the positions of the R

peaks. The output of the last stage is a positive

signal that has two high-amplitude peaks

corresponding to each QRS complex. At the

detection stage, the objective is to detect one of

these peaks. However, some noise peaks could

appear due to electromyographic noises that produce

the same high amplitude peaks. To avoid the

generation of false positives (i.e. noise peaks

classified as R peaks), we use a dynamic threshold

controlled by the state machine of Figure 6. The

value of the threshold changes with each new

sample as follows:

State 1: During interval RR

min

+ QRS

int

(minimum

BIOSTEC2015-DoctoralConsortium

possible RR interval + standard QRS duration), a

maximum is searched. When this interval finishes,

the maximum found is taken as an R peak, and the

threshold value is set to the median value of all the

detected R peaks.

State 2: Taking d as the difference between the

position of the last peak and the ending of State 1,

this state waits during RR

min

– d. This avoids the

detection of large T waves and artefact as a new R

peak.

State 3: Threshold level decreases following (4)

until its value reaches again the processed ECG

signal.

[] * [ 1]th n Pth th n

(4)

with

0.7 *

4.7

128

Pth

 

Figure 7 represents the correspondence between

the State and the value of the threshold.

Parameters that depend on the frequency have

been chosen by evaluating the performance of the

algorithm over 90 signals of different frequencies

and different features (Table 1). First database is the

MIT Arrhythmia database (MITDB), composed by

48 signals of patients with different kinds of

arrhythmias; second one is Normal Sinus Rhythm

Database (NSRDB), its 18 ECG signals corresponds

to healthy adults; and, finally, Allergy database

(ADB) is composed by 24 ECG signals of children

(aged 7 months to 10 years) exposed to provocation

test at the Paediatrics section of the University Cork

Hospital. 2100 combinations of the 3 parameters

were tested: 10 values of N (1 to 10), 10 values of

Nd (1 to 10), and 21 values of Pth (4.5 to 6.5 in steps

of 0.1). Table 1 summarizes the overall performance

of the algorithm on each database. Metrics used are:

- Sensitivity:

TP FN





(5)

- Positive predictivity:

TP FP





(6)

-1

Subject 100m ECG signal (F

= 360 Hz)

-1

Derivation result

1 200 400 600 800 1000 1200 1400

0.5

x 10

-3

Preprocessing output

n (samples)

y[n]

[n]

x[n]

Figure 5: Pre-processing stage results.

UseoftheHeartRateVariabilityasaDiagnosticTool

State 1

Find maximum value

counter 

State 2

counter



State 3

counter R



• save

peak

Pos

ykt

counter 0

counter RR

min

Figure 6: Threshold value setting finite state machine.

min

+ QRS

int

min

-d

min

+ QRS

int

min

-d

Figure 7: Correspondence between the states of the FSM and the value of the threshold.

Table 2: Features and Overall Performance of the R Peaks Detection Algorithm over the Used Databases.

Database



subjects



(Hz)



Length



(%)



(%)



MITDB



48 360 24 h

109107 308 295

99.719 99.730

NSRDB



18 128 15 h

195846 60 11

99.969 99.994

ADB



24 256 10 h

195872 1137 1396

99.423 99.292

4.2 Allergy Detection Algorithm

Thanks to the studies about the HRV signal and its

relation with most of the physiological systems of

the body, some works have been published on how

to obtain a great number of its features and the

meaning of their variations. Depending on the final

application, it would be necessary to select one HRV

feature of a group of them.

In a previous work, 18 features of the HRV were

selected to study the heart performance during an

allergic reaction. These features were obtained using

60 seconds windows with 1 second shift: mean of all

the RR intervals within the window; standard

deviation (STD); coefficient of variation:

STD/mean; RMS of differences between adjacent

RR intervals; number of pairs of adjacent RR

intervals differing by more than 50 ms, NN50.;

pNN50, pNN25: NN50/NN25 divided by the total of

RR intervals; positive and negative sequential trend:

percentage of consecutive RR increasing/decreasing

pairs; cardiac sympathetic/vagal indexes and their

relations (CSI, CVI, CSI*CVI and CSI/CVI);

histogram index; very low, low and high frequency

power: total power of the HRV spectrum in the VLF

band (0 to 0.04 Hz), LF band (0.04 to 0.15 Hz) and

HF band (0.15 to 0.4 Hz); ratio LFHF.

The process needed to obtain all these features

and detect an allergic reaction has a very high

computational cost. Another objective of this thesis

consists in reducing this group in order to be able to

perform the allergy detection in real-time. Through

the analysis of a previous obtained database (ADB),

we can study all these features on 24 subjects

BIOSTEC2015-DoctoralConsortium

exposed to provocation tests: 15 of them allergic,

and 9 non-allergic. Table 3 summarizes the features

of each test.

Table 3: ADB features.

ID Age Gender Allergen OFC length Total doses Result

1 18 months M Wheat 0h. 14min. 1

Fail

2 6 years M Peanut 1h. 40min. 5

3 9 years M Egg 1h. 34min. 5

4 12 months M Milk 1h. 44min. 4

5 8 years M Peanut 2h. 13min. 7

6 9 years F Peanut 0h. 36min. 1

7 6 years M Soy 0h. 57min. 3

8 5 years M Peanut 1h. 45min. 5

9 8 years F Egg 0h. 50min. 2

10 3 years M Milk 1h. 23min. 3

11 6 years F Peanut 1h. 25min. 5

12 5 years F Milk 0h. 41min. 2

13 3 years F Milk 1h. 46min. 5

18 8 years M Soy 0h. 33min. 1

21 9 years F Wheat 1h. 37min. 7

14 12 months M Milk 2h. 10min. 4

Pass

15 6 years M Egg 1h. 42min. 5

16 10 years M Egg 2h. 09min. 9

17 4 years F Soy 2h. 11min. 8

19 6 years M Peanut 1h. 51min. 8

20 7 months F Milk 0h. 56min. 2

22 4 years F Wheat 1h. 29min. 6

23 2 years M Peanut 1h. 03min. 4

24 18 months F Milk 1h. 33min. 6

Mean

4 years 9

months

-- -- 1h. 15 min. 4.5 ---

The diagnostic ability of each feature can be

obtained by analysing its Area under the ROC curve

(AUC). Sensitivity (7) and Specifity (8) for each

value of each feature’s standard deviation is

represented.

ofAllergicSubjectsCorrectlyClassified

ofAllergicSubjects



(7)

ofNonAllergicSubjectsCorrectlyClassified

ofNonAllergicSubjects



(8)

If the AUC is 50% (the lowest possible value),

the result of the classification is random; above this

value, the greater the AUC value is, the better the

diagnostic ability is. Also, computational time

employed by Matlab to obtain each feature has been

obtained. These two values are represented in Figure

8. Best AUC is obtained by using mean feature

(94.07 %), while worst one (50.7 %) was obtained

with CSI feature.

Due to the fact that with the mean, the best result

is obtained and the computational load needed to

compute it is low, this was the selected feature to

detect the allergic reactions. The next step involves

the analysis of this feature during all the provocation

tests, in order to establish the differences between

allergic and non-allergic subjects.

Black line of Figure 9 shows the mean of the

HRV, MRR, (computed in windows of 60 seconds

with 1 second shift) of an allergic subject and Figure

10, of a non-allergic subject. First grey area

represents the interval until the patients gets the first

dose, information during this interval will be used as

patients’ background. Next grey areas represent the

intervals in which they get the next doses. During

these intervals, MRR signal will not be analysed

because patients are having the allergen, medical

staff could be taken measurements, or even,

reallocating the electrodes, etc., so, ECG signal

could being corrupted, and a false positive can be

produced.

There is a clear difference between these two

signals: the elevations produced on the subject 7

MRR are not present on the other one. It has been

observed that this kind of elevations appears (more

or less clearly) in all the allergic subjects, while they

are not on the non-allergic ones. Another interesting

thing is that they appear, in most of the cases,

several minutes before the tests ended (i.e. the

instant in which the allergic subjects manifested

symptoms). The allergy detection process consists,

thus, in modelling them and detect these peaks.

Normal HRV of a subject depends on his/her

age, sex, health state, weight, etc. so, it is necessary

to normalize the MRR signal. Due to the fact that the

algorithm does not know these features, it is

necessary to normalize each signal depending on its

own features. First grey area (background interval)

has been used to compute the mean MRR of each

subject, as during this time, they are in a “normal”

situation. Once they take the first dose, the value of

the mean is subtracted from the MRR signal (red

line of the figures 9 and 10), the value of the mean is

recomputed each time the subjects have a new dose.

The new mean’s value is computed with the

MRR values from the last time the subjects ate a

dose, to the instant in which they had a new one. As

we are looking for positive peaks (HR increases), if

the MRR decreases, it will not represent an allergic

reaction so, values lower than zero are removed,

obtaining what we call normalized MRR signal

(NMRR). NMRR represent how the MRR signal

moves away from its mean normal value.

Finally, the mean value of all the peaks within

the NMRR signal are computed as follows: a

threshold called ParamZero has been established.

The mean of all the consecutive values higher than

this threshold is obtained. If this value is higher than

allergy_threshold, the peak represents an allergic

reaction. To set the allergy_threshold, the maximum

value of all the peaks present on the NMRR signal

has been computed for each subject. As Figure 11

shows, there is a clear difference between allergic

subject’s maximum peak value (green bars); and

non-allergic’s ones (orange bars). Finally, several

UseoftheHeartRateVariabilityasaDiagnosticTool

values of ParamZero have been tested, obtained

performances to those values are plotted on Figure

12.

The selected value of these two parameters allow

us to correctly detect 14 of 15 allergic subjects (all

except for the number 18), and none of the non-

allergic subjects are misclassified as allergic. Also,

the detection of the allergic subjects occur in an

average time of 42 minutes before they are detected

by the medical staff (mean provocation tests length:

79 min.). This time reduction implies also a

reduction of the number of doses needed from 3.93

to 1.5 (mean value).

Mean CSI/CVI STD STNN RMSSD STPP CV LF ratioLFHF VLF Histo pNN25 HF pNN50 CSI*CVI CVI NN50 CSI

100

Feature

ROC area

Features' Roc areas

AUC

% computational time

Figure 8: AUC vs. computational time of each feature.

Figure 9: Mean of the HRV of an allergic subject.

Figure 10: Mean of the HRV of a non-allergic subject.

BIOSTEC2015-DoctoralConsortium

1 2 3 4 5 6 7 8 9 10 11 12 13 18 21 14 15 16 17 19 20 22 23 24

Maximum NRR integration value comparison between groups

Sub

ect ID

Maximum NRR integration value

Allergic

Non-allergic

mean allergic

mean non-allergic

Figure 11: Maximum peak value for all the subjects.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

100

105

X: 1.244

Y: 100

Allergy detection performance vs. ParamZero values

ParamZero

X: 0.065

Y: 93.33

X: 0.064

Y: 86.67

X: 0.016

Y: 73.33

X: 0.007

Y: 66.67

X: 1.246

Y: 88.89

X: 1.429

Y: 77.78

X: 1.494

Y: 66.67

X: 0.028

Y: 80

Sensitivity

Specificity

Figure 12: ParamZero values vs. Se and Sp.

5 EXPECTED OUTCOME

Among all objectives, this thesis pretends to

demonstrate the utility of the analysis of the HRV to

the early detection of allergic reactions. For that, the

main objective is the design of a system composed

by a local positioning system, a host and multiple

devices to analyse continuously and remotely all the

subjects’ HRV present in the same room.

In the case of an allergic reaction is detected, the

system will alert the personal staff, reporting the

name or ID of the patient, as well as his/her location.

6 STAGE OF THE RESEARCH

During the realization of this thesis, two algorithms

have been designed. The first algorithm, the QRS

detector, have been tested thanks to the existence of

a great number of annotated ECG databases.

However, as there is no preceding studies

showing the relationship between the ECG signal

and the allergies, there are no databases that the

authors could use to test the allergic reactions

detection algorithm. For that reason, and due to the

fact that the available database (allergy database) is

UseoftheHeartRateVariabilityasaDiagnosticTool

composed only by 24 subjects (15 allergic), the

author is nowadays carrying out a collection process

at allergy section of the Guadalajara University

Hospital (Spain). Subjects of this database are adults

and children exposed to allergy provocation test

involving drugs and food.

Nevertheless, only 2 of 11 subjects that come up

are allergic and not all of them could be detected due

to several circumstances.

Moreover, another application for the use of the

HRV signal is just starting: the detection of

hypoglycaemia in diabetic patients. At present, if a

diabetic patient believes he is in a state of

hypoglycaemia, he needs to do himself a blood

analysis or carry continuously a device that

measures the glucose level on the interstitial tissue.

This devices are carried inserted, and apart from

they affect the comfort of the patients, they are very

expensive. Besides, this devices have a big delay on

the level of glucose measurement on situations of

abrupt changes: immediately after a big meal or

during the realisation of physical activity.

It has been studied that, during a hypoglycaemia,

the body generates adrenaline (like during a stress

situation), which provokes a HR elevation (O.

Hamdy et al., 2014). In this case, what makes

difficult the detection of this situations are the daily

activities that also elevate the HR, like the

realization of physical activity. In this way, it is

planned to study the HR and the movement (by

using inertial sensors) of a series of diabetic subjects

to establish the differences between normal HR

elevations and those produced during normal

situations.

ACKNOWLEDGEMENTS

This work is supported in part by the Spanish

Ministry of Economy and Competitiveness (LORIS

Project, TIN2012-38080-C04-01) and by the

University of Alcalá trough the FPI program.

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