DATA MINING AND THE FUNCTIONAL RELATIONSHIP
BETWEEN HEART RATE VARIABILITY AND EMOTIONAL
PROCESSING
Comparative Analyses, Validation and Application
F. Riganello and A. Candelieri
S. Anna Institute and RAN – Research on Advanced Neuro-rehabilitation, Crotone, Italy
Laboratory for Decision Engineering and Health Care Delivery, DEIS, University of Calabria, Cosenza, Italy
Keywords: Heart Rate Variability Analysis, Data Mining, Music, Traumatic Brain Injury, Vegetative State.
Abstract: Aims of the study are to 1-classify emotional responses in healthy and conscious brain injured subjects by
Data Mining analysis of subjective reports and Heart Rate Variability (HRV), 2-compare different
procedures for reliability, and 3-test applicability in patients with disordered consciousness (vegetative
state). We measured HRV of 26 healthy and 16 posttraumatic subjects listening music samples selected by
emotions they evoke. Each subject was interviewed and the reported emotions were used for identifing a
model assessing the most probable emotion by the HRV parameters. Two macro-categories were defined:
positive and negative emotions. The study matched a three-phases strategy. First, we applied several
classification approaches to healthy subjects evaluating them through suitable validation techniques.
Secondly, the best performing classifiers were used to forecast emotions of posttraumatic patients, without
retraining. In the 3rd phase we used the most reliable decision model both for validation (1st phase) and
independent test (2nd phase) in order to classify the “emotional” response of 9 subjects in vegetative state.
One HRV parameter (normalized Low-Frequency Band Power) proved sufficient to forecast a reliable
classification. Accuracy was greater than 70% on training, validation and test. Model represents an objective
criterion to investigate possible emotional responses also in unconscious patients.
1 INTRODUCTION
Data mining techniques are used in medicine to sort
significant information out of large databases in
mutagenicity studies, predictive toxicology, disease
classification, selective integration of multiple
biological databases, etc. Application in neurology
has focused on early prediction of outcome
(Herskovits and Joan, 2003; Chen et al, 2008) or on
the characterization of brain processing in healthy
subjects and patients with disorders of consciousness
(Dolce et al, 2008b; Riganello et al, 2008). Heart
Rate Variability (HRV) is an emerging objective
measure of the continuous interplay between the
sympathetic and parasympathetic autonomic nervous
sub-systems (Task Force of European Society of
Cardiology and the North American Society of
Pacing and Electrophysiology of Circulation, 1996);
it is thought to provide information also on complex
patterns of brain activation (Dolce et al, 2008a;
Riganello et al, 2008; Appelhans and Luecken,
2006;). HRV abnormalities are reportedly common
in psychiatric or brain damaged patients (Keren et al
2005, Cohen 2000, Draper, 2007); HRV proved a
useful predictor of outcome in brain injured patients
(Briswas et al, 2000; Wijnien et al, 2006); HRV
spectral parameters proved able to classify the
emotional responses to complex stimuli in a
preliminary study on healthy subjects (Riganello,
2008).
HRV studies require quantitative approaches
and large numbers of parameters are generated when
the parametric and non-parametric HRV spectra are
computed, in contrast with the unconsolidated
knowledge of the problem and the lack of models
based on physiology. In this connection,
conventional statistics based on a priori
requirements about the data distribution could
generate biased results (Abt, 1981, 1983).
159
Riganello F. and Candelieri A. (2010).
DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND EMOTIONAL PROCESSING - Comparative
Analyses, Validation and Application.
In Proceedings of the Third International Conference on Health Informatics, pages 159-165
DOI: 10.5220/0002691101590165
Copyright
c
 SciTePress

In this study we compare different classification
learning strategies through suitable validation
techniques, such as 10folds-cross and leave-one-out
validation and test on independent data set, in order
to select the most reliable model to characterize and
classify by HRV the emotional responses to complex
stimuli. To this end, we analyzed data generated in
different studies on the emotional response to music
of healthy controls, conscious posttraumatic subjects
and patients in vegetative state.
2 MATERIALS AND METHODS
2.1 Subjects
Three selected groups of subjects were studied:
1-twenty six healthy volunteers (14 women;
mean age: 31.7±7.1 yrs., age range: 21-45 yrs.) with
high-school to university education);
2-sixteen patients with no residual severe
disabilities completing rehabilitation after severe
traumatic brain injury (5 women; mean age:
21.6±3.0 yrs., age range: 17 to 33 yrs., grammar to
high school education);
3-nine subjects (3 women; mean age: 32.5±7.4
yrs., age range: 16 to 48 yrs., grammar to High
School education) in persistent vegetative state
unambiguously diagnosed compliant to the
international criteria as being awake, but unaware of
self and environment, with no purposeful movement
or behavior and no indication of processing of
external sensory inputs (Dolce and Sazbon, 2002;
Jennett, 2002; Giacino et al, 2002; Laureys and
Boly, 2007).
None of controls or posttraumatic patients had
received formal musical training. Controls, brain
injured patients and the caregivers of subjects in
vegetative state were informed in full detail about
the study purposes and experimental procedures and
the ethical principles of the Declaration of Helsinki
(1964) by the World Medical Association
concerning human experimentation were followed.
The procedures for data collection and the
experimental setting caused no physical or
emotional discomfort and the staff carrying on the
experiments were instructed to discontinue
stimulation and data recording whenever a subject
complained or appeared to be tired or in distress.
2.2 Stimulus Conditions
Four music samples were selected following
characterization by intrinsic structure and expected
emotional response as indicated by the available
formal complexity and dynamics descriptors. These
descriptors reportedly relate music structure to self-
assessed emotions and allow characterize the
emotional status along a continuum from euphoria
and well-being to melancholy, severe anxiety and
perceived aggressive tendencies (Imberty, 1997;
Tarasti, E., 1994; Nikki, 2004; Urakawa and
Yokoyama, 2005) (Table 1).
Table 1: Selected music samples.
L. Boccherini: Quintet op. 11 n. 5, Minuetto
P.I. Tchaïkovski: 6th symphony., op. 74, first movement
M.P. Mussorgsky: St. John's Night on the Bald Mountain
E. Grieg: Peer Gynt, op 23, The morning
Experiments on patients and control took place at
the same time of the day in a familiar environment
and did not interfere with the posttraumatic patients
or vegetative state subjects’ rehabilitation schedule
or medical/paramedical care.
Subjects were comfortably lying on an armchair,
with constant 24º C ambient temperature and in
absence of transient noises. They were exposed
binaurally (earplugs) to the four selected music
samples balanced for loudness and played in random
sequence to minimize carryon effects. There was a
20-min rest between consecutive samples to avoid
over-stimulation and excessive fatigue; for this
purpose, the subjects in vegetative state were
exposed to two music samples per day only.
At the end of each music sample, controls and
posttraumatic patients were requested to report and
subjectively classify their emotions, without
reference to any pre-selected categories and
irrespective of the emotional feeling they thought the
music was intended to induce.
The distribution of the emotions expressed for
each music sample was determined (Imberty, 1997;
Tarasti, E., 1994). Methods are described in detail
elsewhere (Riganello, 2008) where the authors have
preliminary studied relationship between HRV
Analysis and emotions during music listening in
healthy subjects and traumatic brain injured patients.
In present study authors have studied the possibility
to apply the approach to the evaluation of emotional
conditions of patients in vegetative state.
2.3 Heart Rate Variability
The heart beat was recorded from the beginning of
the music sample and for a total of 300 beats
(3',36"±24", with 83.7± 9.5 beats/min and a resulting
total recording time between 3',12" and 3':55") by
HEALTHINF 2010 - International Conference on Health Informatics
160

means of the Virtual Energy Tester (Elamaya
Instruments, Milano, Italy).
The photopletismographic sensors were
positioned on the third phalange of the left hand
middle finger in order to minimize the subjects’
discomfort consistent with the guidelines of the Task
Force of European Society of Cardiology and the
North American Society of Pacing and
Electrophysiology (1996).
The photopletismographic signal was sampled at
100 samples/sec; the series of consecutive intervals
between heart beats was analyzed in the time and
frequency domains by the HRV advanced analysis
software developed at the Department of Applied
Physics, University of Kuopio, Finland (Niskanen et
al, 2004). The non-parametric (Fast Fourier
Transform, Welch spectrum) and parametric
(autoregressive) spectra were computed (Table 2).
The power spectral density from 0.01 Hz to 0.5 Hz
was computed with 0.001 Hz resolution and three
frequency ranges (very low frequency [VLF]: 0.01-
0.04 Hz; low frequency [LF]: 0.04-0.15 Hz; and
high frequency [HF]: 0.15 Hz – 0.5 Hz) were
considered. A total of 35 spectral parameters were
obtained from each one of each subject/patient’s
four recordings (Table 2).
Table 2: Clinical parameters from HRV Analysis.
Statistical
Parameters
Spectral Parameters
- Mean RR interval
(Mean RR) and SD
(STD RR);
- Mean Heart Rate
(Mean HR) and SD
(STD HR);
- Root mean square of
SD (RMSSD);
- number (NN50) and
percentage (pNN50)
of NN intervals
longer than 50 ms
-
Very Low, Low and High
Frequency (VLF, LF and HF),
and normalized unit (nu) in FFT
and autoregressive spectra
-
VLF, LF and HF Peak frequency
in FFT and autoregressive
spectra
-
Power Spectrum of VLF, LF, HF
and Total in FFT and
autoregressive spectra
-
% of VLF, LF, HF in FFT and
autoregressive spectra
-
LF/HF, nuLF, nuHF, nuLF/HF
in FFT and autoregressive
spectra
2.4 Study Design and Data Mining
Processing
The relationship between HRV and emotional
response was studied in a classification task, in
which every instance was represented by the
subject’s HRV parameters related to each music
sample listening. Formulating any hypothesis about
a specific relationship between heart activity
(measured through HRV analysis) and reported
emotions was as difficult as selecting a-priori a
unique learning strategy. For such reasons we
applied several classification learning procedures,
such as the Decision Trees, Support Vector
Machines, Rule Learners and Artificial Neural
Networks all available in the open source software
WEKA (Waikato Environment for Knowledge
Analysis) (Witten and Eibe, 2005; Eibe, 2004).
The class label for each instance was defined by
the emotions self-reported by healthy controls and
traumatic brain injured patients after passive
listening to each music sample. As a further step, the
reported emotions were clustered into two major
categories: positive (happiness, joy, serenity, calm,
etc.) or negative (fear, anxiety, tension, etc.). Data
Mining task was designed in three consecutive
phases. In Phase 1, the most reliable classification
approaches outlining a relationship between the
HRV spectral parameters and the subjectively
reported emotions was selected by using the healthy
control group as the training set. Such a set included
104 instances (26 healthy subjects x 4 music
samples, with 45 instances labeled as “positive
emotion”) and 35 attributes (the HRV parameters
listed in Table 2).
The reliability of each classification method was
estimated by the most suitable validation techniques
(10 folds-cross and leave-one-out validation). The
Classification Rule Learner (ONE-R) (Holte, 1993)
and an Artificial Neural Network (Multi Layer
Perceptron (Jain et al, 1996)) proved the most
performing approaches at the end of Phase 1 and
were entered in the study Phase 2, in which an
independent test set (the traumatic brain injured
patients’ group) was processed without performing
any algorithm retraining. Purpose of this strategy
was to evaluate the model capability of correctly
classifying the emotional status for new unseen data.
The independent test set included 64 instances
(16 posttraumatic subjects x 4 music samples, with
25 instances labeled as “positive emotion”) and the
same 35 HRV parameters. In Phase 3, we applied
the decision model proving reliable on both phase 1
and 2 (notably, only the ONE-R model) to classify
the emotional status of patients in vegetative state
listening the same music samples, only using HRV
parameters selected as relevant descriptors in phase
1 and 2. The study design is summarized in Figure 1.
DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND
EMOTIONAL PROCESSING - Comparative Analyses, Validation and Application
161

Figure 1: Outline of the study design.
Table 3: Range of nuLF for classifying emotional status.
1 2 3 4 5
[0; 29.5] [29.5;
46.35]
[46.35;64.25] [64.25;
72.45]
[72.15; 100]
2.5 Data Mining Procedures
ONE-R is a Classification Rule Learner that accepts
a training set as input and searches for “1-rule”
classifying instances on the basis of a single variable
(attribute). Initially, the algorithm ranks attributes
according to the training set error rate. If the
attribute is numerical, the algorithm divides the
range of possible values into several disjoint
intervals, each one associated to a class value. In
order to avoid over-fitting (i.e. intervals including
only one instance belonging to one class), ONE-R
allows set a parameter (the “bucket-size”) that is the
minimum number of instances in each interval.
We have obtained the best performing
configuration for ONE-R by setting up 7 for such a
learning parameter. The rule extracted from the
healthy controls’ group (Table 3) is characterized by
five ranges of nuLF (note that nu_LF computed for
each listening can belong to only one interval, so
only one prediction is possible).
Multi Layer Perceptron (MLP) is a specific
Artificial Neural Network (ANN) architecture also
known as “feed forward architecture”. It comprises
at the least three different layers: input, hidden and
output layers. Generally, the hidden layer can be
greater than one and the number of neurons into
hidden layers can also vary, where a neuron is the
net “simple” processing unit. As any ANN, MLP is a
mathematical/computational model based on
biological neuronal networks and is commonly
applied to model complex input/output relationships
or to identify data patterns of distribution/
correlation. Structurally, an ANN is composed by a
set of neurons linked together by a large number of
(usually nonlinear) weighted connections. Each
neuron is able to calculate a specific function, given
the inputs and the weights on the connections are
adjusted in order to minimize some criteria as the
errors number. By using MLP we have obtained the
best results by setting up the following net
parameters: only one hidden layer with 5 neurons,
Learning Rate=0.2, Momentum=0.1 and 1000
training epochs. Moreover we have previously
ranked the variables by preprocessing the dataset
through ONE-R WEKA Filter, which evaluates the
worth of an attribute by using the ONE-R strategy.
Finally, the best results for MLP were obtained
selecting the first 8 ranked attributes (nu_LF,
powerHF, STDRR, gender, powerVLF, peakVLF,
peakLF and peakHF). The MLP architecture is
showed in Figure 2.
HEALTHINF 2010 - International Conference on Health Informatics
162

nu_LF
Power HF
STD RR
gender
Power VLF
Peak VLF
Peak LF
Negative
Positi ve
Peak HF
nu_LF
Power HF
STD RR
gender
Power VLF
Peak VLF
Peak LF
Negative
Positi ve
Peak HF
Figure 2: Multi Layer Perceptron architecture.
3 RESULTS
Controls and posttraumatic subjects reported a
variety of emotions in response to the music samples
they listened to, ranging from happiness to rage and
fear. The reported emotions clustered in seven
categories (indifference, bother, rage, fear, sad,
serenity and joy) and could be successively
categorized as being “positive”, “negative”. In
particular in the healty subjects Boccherini and
Grieg induced emotions for which the positive label
was applicable(100% and 81.5% respectively), as
well as negative emotion were associated to
Tchaicovsky and Mussorsky music’s (96.3% and
92.6 respectively). Similarly, in posttraumatic
subjects Boccerini induced positive emotion in the
75% of subjects while Tchaicovsky and Mussorsky’
music’s induced negative emotions in the 87.5% and
75% of subjects respectively. By contrast Grieg’s
music induced ambiguous results with 46.8% and
56.2% of positive and negative emotions
respectively (figure 3). The distributions of reported
emotions differed significantly across music samples
(Log-likehood radio: 258.8, p<0.001), but not
between patients and controls (Log-likehood radio:
2.318, p>0.05). Heart Rate increases in response to
Mussorgsky and Tchaicovsky music’s in 63.5% of
subjects, while decreases in response to Boccherini
and Grieg music’s in 61.5% of subjects (ANOVA
computed on HRV: F=5.074, p=0.026; homogeneity
of variance p=0.4). For the posttraumatic patients,
significant differences were not observed for the
heart rate variation. For patients in vegetative state
the heart rate trend of variation were towards
decreases with Boccherini’s (-1.85 ±3.9 beat/m) and
toward increases with Mussorgscky Tchaicovsky
and Grieg’s music samples (2.9 ±7.3beat/m, 1.7±4.5,
and 0.85±4.4 bea/m respetively).
Figure 3: Distribution of self-reported emotion (positive-
green, negative-red) in healthy and brain injured subject
(oblique strips). In patients in vegetative state (horizontal
stripes) the emotion has been extracted by Data Mining
procedures (ONE-R).
With Data Mining procedures the best results
were obtained by the algorithm ONE-R, that sorted
out the nu_LF HRV spectral descriptor as the
significant HRV parameter in the selected
experimental conditions and for the study purpose.
The algorithm allowed the best trade-off between the
portion of correct sample/label association on
training set (recognition) and the portion of correct
sample/label association both on internal validation
and an independent test set (generalization). ONE-R
accuracy was 76.0% for the healthy controls’ group
(tenfold cross-validation: 70.2%; leave-one-out
validation: 71.1%), with 69% and 81% correct
classification by HRV of the emotions subjectively
reported as positive or negative, respectively.
Comparable accuracy estimates were obtained when
the ONE-R learned model was applied to the
traumatic brain injured patients’ group, with 70.3%
correct assessments (65% and 74% assessments of
positive and negative emotions, respectively).
Despite the greater accuracy of the MLP approach
on the training set (82.7% vs. 76.0%, with MLP and
ONE-R respectively), MLP accuracy has decreased
to 50% on internal validation, with 51,6% correct
attributions of the emotion relative to injured
patients (Table 3). The ONE-R algorithm performed
better than MLP when clustering emotions into the
negative and positive classes. In this, the algorithm
provides an understandable and user-friendly rule
applicable in the classification of emotional
conditions (Figure 2). Results comparable to (or
worse then) those obtained by MLP were also
observed when other classification learning
approaches were used, such as Decision Trees and
Support Vector Machines (personal, unpublished
data).
DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND
EMOTIONAL PROCESSING - Comparative Analyses, Validation and Application
163

Table 4: Accuracy, Sensitivity and Specificity on training,
validation and independent test.
Training
10Folds-
cross
Leave-
one-out
Independent
Test
All data: ONE-R vs MLP
OneR 75.96% 70.19% 71.15% 70.31%
MLP 82.69% 47.11% 46.15% 51.56%
Attribution of positive emotions
OneR 68.89% 57.78% 64.44% 68.00%
MLP 68.89% 33.33% 26.67% 32.00%
Attribution of negative emotions
OneR 81.36% 79.66% 76.27% 71.79%
MLP 93.22% 57.63% 61.02% 64.10%
The ONE-R model classified the “emotional”
responses of the subjects in vegetative state by
nu_LF, in the absence of any subjective
classification. The results were comparable (figure
3). Boccherini’s music was attributed to the class of
positive emotions in 94.4% of cases (self-classified
as positive in 92.3% and 75.0% by healthy and
posttraumatic subjects, respectively). The ONE-R
model attributed Thcaicovsky and Mussorgsky’s
music to the negative emotions class in 55.6% and
100% of cases (controls and posttraumatic patients
allocated these music samples to the negative class
in 96.3% and 87.5% of cases for Tchaicovsky
listening, as well as 92.6% and 75.0% of cases for
Mussorgsky listening respectively). Grieg’s music
was classified as positive by the ONE-R model in
44.4% of cases (81.5% and 43.8% for healthy
controls and posttraumatic patients, respectively).
4 COMMENTS
Data Mining techniques offer operational advantages
when large datasets are analyzed, as may be the case
in medicine (van Bemmel and Munsen, 1997;
Robert et al, 2004; Lee et al, 2005). No underlying
data distribution is requested as is conventional
statistics; the variables are automatically
transformed in the computational process;
applicability to multivariate non-linear problems and
identification of interrelationship between predictor
variables are high. On the other hand, generalization
is crucial when using Data Mining algorithm: full
control of over-fitting requires an extensive
computation, a large sample size and suitable
validation techniques.
Every Data Mining approaches uses a peculiar
structure to code the knowledge intrinsic to the
dataset; for such reason, comparing different
approaches – as in this study – may be a pragmatic
strategy to extract the most reliable model applicable
to new databases. In this study, the Data Mining
approaches succesfully identified condition-related
HRV spectral descriptors (notably the nu_LF
measure) by which responses to different music
samples may be classified in conscious subjects
(healthy controls and posttraumatic patients) as well
as in subjects with severe disorders of consciousness
and no indication of sensory processing (Riganello
et al, 2008; Appelhans and Luecken, 2006). When
the reported subjective emotions were classified into
two macro-categories, classification by ONE-R
proved able to identify a “simple” relationship with
good classification performance, both on training
and internal validation on healthy controls.
Comparable reliability has been obtained by
applying (without any algorithm retraining) the
learned ONE-R decision model on traumatic brain
injured patients. These findings indicate that a single
HRV descriptor is able to characterize the subjects’
emotional status or response. The information
extracted by means of the simple “if…then” rule
(ONE-R) from the healthy controls’ group was
validated in the posttraumatic patients independent
group. Another relevant advantage of ONE-R is that
it provides a model easy-to-understand, while MLP
is affected by the Block Box effect. The HRV
approach allows to investigate brain responsiveness
by non-invasive data recording techniques and
widespread application in medicine, e.g. in the
functional monitoring of subjects in vegetative state.
A possible extension to the approach is the
implementation of software tools for the HRV
functional monitoring in patients whose brain
processing may be relevant for diagnostic or
prognostic purposes (Herskovits, 2003; Chen, 2008;
Dolce, 2008b).
REFERENCES
Abt, K., 1981. Problems of repeated significance testing,
Control Clinical Trials, 1, 377-381.
Abt, K., 1983. Significance testing of many variabols.
Problems and solutions, Neuropsychobioloy, 26, 77-88.
Appelhans, B. M., Luecken, L. J., 2006. Heart rate
variability as an index of regulated emotional res-
ponding. Review of General Psychology, 10, 229–240.
Briswas, A. K., Scott, W. A., Sommerauer, J. F., Luckett,
P. M., 2000. Heart rate variability after acute traumatic
injury in children. Critical Care Medicine, 28, 3907–
3912.
Chen, S., Haskins, E. W., Ottens, K. A., Hayes, L. R.,
Denslow, N., Wang., K. W. K., 2008. Bioinformatics
HEALTHINF 2010 - International Conference on Health Informatics
164

for Traumatic Brain Injury: Proteomic Data Mining.,
Data Mining in Biomedicine , 7, 363-387
6. Cohen, H., Benjamin, J., Geva, A.B., Matar, M.A.,
Kaplan, Z., Kotler, M., 2000. Autonomic
dysregulation in panic disorder and in posttraumatic
stress disorder: Application of power spectrum
analysis of heart rate variability at rest and in response
to recollection of trauma or panic attacks. Psychiatry
Research, 96(1), 1–13.
7. Dolce,G., Riganello, F., Quintieri, M., Candelieri, A.,
Conforti, D., 2008a. Personal interaction in the
Vegetative State: a Data Mining Study. Journal of
Phycophisiology, 22(3), 150-156.
Dolce, G., Quintieri, M., Serra, S., Lagani, V., Pignolo, L.,
2008b. Clinical signs and early prognosis: a decision
tree, data minino study, Brain Ingury, 22:7, 617-623.
Dolce, G., Sazbon L., 2002. The posttraumatic vegetative
state. Stuttgard, Thieme.
Draper, K., Ponsford, J., Schönberger, M., 2007.
Psychosocial and emotional outcomes 10 years
following traumatic brain injury. Journal of Head
Trauma Rehabilitation, 22, 278–287.
Eibe, F., 2004. Machine learning with WEKA.
Department of Computer Science, University of
Waikato, New Zealand. RETRIEVED 2006 from
http://puzzle.dl.sourceforge.net/sourceforge/
weka/weka.ppt
Giacino, J.T., Ashwal, S., Childs, N., Cranford, R.,
Jennett, B., Katz, B.I., Kelly, J.P., Rosemberg, J.H.,
Whyte, J., Zafonte, R.D., Zasler, N.D., 2002. The
minimal Conscious State: Definition and Diagnostic
Criteria. Neurology, 58, 349-353.
Herskovits, H. E., Joan, P. G., 2003. Application of a data-
mining method based on Bayesian networks to lesion-
deficit analysis, NeuroImage, 19(4),1664-1673.
Holte, R.C., 1993. Very simple classification rules
perform well on most commonly used datasets.
Machine Learning, 11, 63–90.
Imberty, M. 1997. Epistemic subject, historical subject,
psychological subject: Regarding Lerdhal and
Jackendoff’s generative theory of music. In I. Deliege
& J.A. Sloboda, Perception and cognition of music.
Hove, UK. Psychology Press, 429-432
Jain, A. K., Jianchang, M., Mohiuddin, K. M., 1996.
Artificial neural networks: a tutorial. Computer, 29(3),
31-44.
Jennett, B., 2002. The vegetative state. Cambridge, UK,
University Press.
Keren, O., Yapatov, S., Radai, M.M., Elad-Yarum, R.,
Faraggi, D., Abboud, S., Ring, H., Grosswasser, Z.,
2005. Heart rate variability of patients with traumatic
brain injury during postinsult subacute period. Brain
Injury, 19, 605–611.
Laureys, S., Boly, M., 2007. What is it like to be
vegetative or minimal conscious? Current Opinion in
Neurology, 20, 609-613.
Lee, C., Yoo, S.K., Park,Y., Kim, N., Jeong, K., Lee, B.,
2005. Using Neural Network to Recognize Human
Emotionsfrom Heart Rate Variability and Skin
Resistance. Proceedings of the 2005 IEEE Engineering
in Medicine and Biology , 5, 5523-5525.
Nikki, S.R., 2004. Intense emotional response to music: A
test of the physiological arousal hypothesis.
Psychology of Music, 32, 371–388.
Niskanen, P.J., Tarvainen, M.P., Ranta-aho, P.O.,
Karjalainen, P.A., 2004. Software fo Advanced HRV
analysis. University of Kuopio Departement of
Applied Physics. Computers Methods and Programs in
Biomedicine, 76(1), 73-81
Riganello, F., Quintieri, M., Candelieri A., Conforti D.,
Dolce, G., 2008. Heart Rate response to music. An
artificial intelligence study on heathy and traumatic
brain injured subjects. Journal of Psychophysiology,
22:4, 166-174.
Robert, C., Arreto, C.D., Azerad, J., Gaudy, J.F., 2004.
Bibliometric overview of the utilization of artificial
neural networks in medicine and biology.
Scientometrics, 59(1), 117-130
Tarasti, E., 1994. A theory of musical semiotics.
Bloomington, IN. Indiana University Press.
Task Force of European Society of Cardiology and the
North American Society of Pacing and
Electrophysiology of Circulation. 1996. Heart Rate
Variability: standard of measurement, physiological
interpretation, and clinical use, Circulation, 93, 1043-
1065.
Urakawa, K., Yokoyama, K., 2005. Music can enhance
exercise-induced sympathetic dominancy assessed by
HRV. Tohoku Journal of Experimental Medicine, 205,
213–218.
van Bemmel, J.H., Munsen, M.A., 1997. Handbook of
medical informatics. Berlin: Springer-Verlag.
Wijnien, V.J., Heutinl, M., van Boxtel, G.J., Eilander,
H.J., de Gelder, B., 2006. Autonomic reactivity to
sensory stimulation is related to consciousness level
after severe traumatic brain injury. Clinical
Neurophysiology, 117, 1794-1780.
Witten, H.W., & Eibe, F., 2005. Data mining – Practical
machine learning tools and techniques with Java
implementations. San Francisco, CA. Morgan
Kaufman.
DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND
EMOTIONAL PROCESSING - Comparative Analyses, Validation and Application
165