Enhancing Emotion Recognition from ECG Signals using Supervised

Dimensionality Reduction

Hany Ferdinando

1,2

, Tapio Seppänen

and Esko Alasaarela

Optoelectronics and Measurement Technique Research Unit, University of Oulu, Oulu, Finland

Department of Electrical Engineering, Petra Christian University, Surabaya, Indonesia

Physiological Signal Analysis Team, University of Oulu, Oulu, Finland

{hany.ferdinando, tapio, esko.alasaarela}@ee.oulu.fi

Keywords: Emotion Recognition, kNN, Dimensionality Reduction, LDA, NCA, MCML.

Abstract: Dimensionality reduction (DR) is an important issue in classification and pattern recognition process. Using

features with lower dimensionality helps the machine learning algorithms work more efficient. Besides, it

also can improve the performance of the system. This paper explores supervised dimensionality reduction,

LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally

Collapsing Metric Learning), in emotion recognition based on ECG signals from the Mahnob-HCI database.

It is a 3-class problem of valence and arousal. Features for kNN (k-nearest neighbour) are based on statistical

distribution of dominant frequencies after applying a bivariate empirical mode decomposition. The results

were validated using 10-fold cross and LOSO (leave-one-subject-out) validations. Among LDA, NCA, and

MCML, the NCA outperformed the other methods. The experiments showed that the accuracy for valence

was improved from 55.8% to 64.1%, and for arousal from 59.7% to 66.1% using 10-fold cross validation after

transforming the features with projection matrices from NCA. For LOSO validation, there is no significant

improvement for valence while the improvement for arousal is significant, i.e. from 58.7% to 69.6%.

1 INTRODUCTION

Decreasing the dimensionality of features without

losing their important characteristic is a vital pre-

processing phase in high-dimensional data analysis

(Sugiyama, 2007). Dimensionality reduction (DR) is

an important tool to handle the curse of

dimensionality. Projecting high dimensional feature

space to lower dimensional feature space helps

classifiers perform better. As human vision system is

limited to 3D, visualization of feature space gets

benefits from DR. Moreover, DR is also useful in data

compression (Lee and Verleysen, 2010), for example

when it is important to store all training data as in k-

nearest neighbour classifier (kNN).

Dimensionality reduction (DR) methods include

linear and nonlinear techniques. Well known method

for linear DR is principal component analysis (PCA)

(Jolliffe, 2002). The nonlinear DR emerged later, e.g.

Sammon’s mapping (Sammon, 1969). Furthermore,

there are supervised and unsupervised DR techniques.

The supervised DRs use labels of the data to guide the

mapping process while the unsupervised ones rely on

finding a projection space which provides the highest

variance.

This paper explores a number of supervised DR

techniques, i.e. Neighbourhood Components

Analysis (NCA), Linear Discriminant Analysis

(LDA), Maximally Collapsing Metric Learning

(MCML), and applied them to enhance the accuracy

of emotion recognition-based ECG signal from the

Mahnob-HCI database for affect recognition.

The Mahnob-HCI database was published in 2012

with some baseline accuracies (Soleymani, et al.,

2012) for 3-class classification problem of valence

and arousal. However, a baseline for emotion

recognition based on ECG signals only were not

given therein. Ferdinando et al. (Ferdinando, et al.,

2014) computed Heart Rate Variability (HRV)

indexes achieving baseline accuracies, 42.6% and

47.7% for valence and arousal respectively. Later,

Ferdinando et al. improved the accuracy to 55.8% and

59.7% for valence and arousal respectively by

applying bivariate empirical mode decomposition

(BEMD) to ECG signals and use the statistical

distributions of dominant frequency as the features

(Ferdinando, et al., 2016).

112

Ferdinando, H., Seppänen, T. and Alasaarela, E.

Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction.

DOI: 10.5220/0006147801120118

In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), pages 112-118

ISBN: 978-989-758-222-6

Although significant improvements have been

achieved in (Ferdinando, et al., 2016), the best

accuracies, so far, from this database were 76% and

68% for valence and arousal respectively (Soleymani,

et al., 2012) using features from eye gaze and EEG.

We aim at improving the classification accuracy by

using only ECG signals.

In this paper, we enhance the accuracy of emotion

recognition by applying supervised DR to the features

based on applying BEMD analysis to ECG signals

(Ferdinando, et al., 2016) prior feeding them to the

kNN classifier. Projection matrix calculations were

done with the Matlab code by van der Maaten (van

der Maaten, 2016).

2 SUPERVISED

DIMENSIONALITY

REDUCTION

Supervised DRs in drtoolbox are Linear Discriminant

Analysis (LDA), Generalized Discriminant Analysis

(GDA), Neighbourhood Components Analysis

(NCA), Maximally Collapsing Metric Learning

(MCML), and Large Margin Nearest Neighbor

(LMNN) (van der Maaten, 2016). They work based

on the label/class of the inputs. The labels serve as a

guideline to reduce the dimensionality. The

supervised DR methods in this exploration are based

on a Mahalonobis distance measure

  

2121

xxAxxxx 

(1)

within kNN framework, except LDA and GDA,

where

WWA



is a positive semidefinite (PSD)

matrix, and W is the projection matrix to a certain

space. The ultimate goal is to find projection matrix

A, such that the classifiers perform well in the

transformed space. Unfortunately, the GDA does not

provide a projection matrix A such that new features

can be transformed into other space but user can

choose the target dimensionality (van der Maaten,

2016). For this reason, GDA was not included in our

study. Looking to the implementation of LMNN,

there is no such dimensionality reduction but it

provides a projection matrix A (van der Maaten,

2016). Due to this fact, the LMNN was also discarded

from the experiments.

2.1 Linear Discriminant Analysis

(LDA)

Linear Discriminant Analysis (LDA) (Weinberger

and Saul, 2009) computes linear projection

xx A

that maximizes the amount of between-class variance

) relative to the amount of within-class variance

). The objective function is defined as















ACA

TraceAf )(

subject to

IAA 

(2)

The LDA DR works well when the reduced

dimensionality is less than the number of classes. In

addition, the conditional densities of the classes must

be multivariate Gaussian. Failing to fulfil this

requirement makes the transformed features not

suitable for kNN. This method has been applied to

spoken emotion recognition problem (Zhang and

Zhao, 2013), EEG-based emotion recognition

(Valenzi, et al., 2014), and ECG-based individual

identification (Fratini, et al., 2015).

2.2 Neighbourhood Components

Analysis (NCA)

Neighbourhood Component Analysis (NCA)

(Goldberger, et al., 2005) is non-parametric which

makes no assumption about the shape of the class

distribution or the boundaries between them. The

algorithm directly maximizes a stochastic variant of

the leave-one-out kNN score on the training set. The

final goal is to find a transformation matrix such that

in the transformed space, the kNN performs well. The

size of the transformation matrix determines the

dimension of the transformed features. Using this

method, one can visualize high dimensional features

in 2D or 3D space.

To deal with the discontinuity of the leave-one-

out classification error of kNN, a differentiable cost

function based on stochastic (“soft”) neighbour

assignment in the transformed space was introduced

(Goldberger, et al., 2005). The idea is to use softmax

function, to transform distance from point i to j into

probability p

and inherit its class label from the

selected point.















exp

AxAx

0

(3)

with objective function defined as

Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction

113





 i

iCj

ppAf

)(

(4)

The algorithm searches for the transformation matrix

A, such that the objective function is maximized. The

algorithm uses a gradient rule, by differentiating f(A)

with respect to the transformation matrix A, for

learning. The NCA was able to separate data

containing useful information and noise, which ended

up with dimensionality reduction (Goldberger, et al.,

2005).

The NCA has been applied to research in

Affective Computing, e.g. Zhang and Zhao applied it

to the spontaneous Chinese and the acted Berlin

database for spoken emotion recognition and then

compared it with other dimensionality reduction

methods (Zhang and Zhao, 2013). McDuff et al. used

the NCA in AffectAura project (McDuff, et al.,

2012). Romero et al. put on the NCA to reduce

dimensionality of features from EEG (Romero, et al.,

2015).

2.3 Maximally Collapsing Metric

Learning (MCML)

Maximally Collapsing Metric Learning (MCML)

(Globerson and Roweis, 2006) uses simple geometric

intuition that all points belonging to the same class

are mapped (collapsed) to a single location in feature

space and all points from the other classes are mapped

to other locations. The main goal is to find a

transformation matrix A such that it fulfills the simple

geometric intuition idea. To learn the distance

measure, each training point is assigned to a

conditional probability,



pijp |

, over other

points using softmax function. From conditional

probability point of view, the probability of a point

belonging to class X given that point is in class X is

1, otherwise it is zero. Given pairs of input and label



yx ,

, the conditional probability is defined as













(5)

The algorithm searches for a matrix A such that

is as close as possible to

by minimizing

objective function f(A), i.e. Kullback-Leibler

divergence between them, such that

PSDA

. The

objective function (Globerson and Roweis, 2006) is

defined as













ijpijpKLAf |||)(

(6)

The MCML has been applied to spoken emotion

recognition (Zhang and Zhao, 2013) and EEG-based

Iyashi expression analysis (Romero, et al., 2015).

3 MATERIAL AND METHODS

3.1 ECG Signal Processing

The Mahnob-HCI database contains 32-channel

EEG, peripheral physiological signals (ECG,

temperature, respiration, skin conductance), face and

body video, speech, and eye gaze recording from 27

subjects (11 males and 16 females). All signals were

precisely synchronized which is suitable for

multimodal emotional response studies. The ECG

signals were sampled at 256 Hz (Soleymani, et al.,

2012).

We used the same data as in (Ferdinando, et al.,

2014), i.e. “Selection of Emotion Elicitation” in the

database. The original data contains 513 samples.

However, the sample from session 2508 was

discarded because visual inspection showed it is

corrupted. Thus, we worked with 512 samples,

subject to several filters to suppress noise from power

line interference, baseline drift, motion artifact,

electrode contact, and muscle contraction

(Soleymani, et al., 2012).

The ECG signals contain data from both

unstimulated and stimulated phase. Since we were

only interested in ECG during stimulated phase, this

part must be separated from the other utilizing

synchronization signal provided by the database.

The BEMD method (Rilling, et al., 2007) was

used to get features from ECG. Based on our

experiments, the BEMD method was sensitive to the

length of the signal. For this reason, the ECG signal

was divided into 5 second segments. A synthetic ECG

signal, synchronized with the R-wave event to the

original signal, was generated by using the model

from McSharry et al. (McSharry, et al., 2003). This

signal served as the imaginary part of the ECG signal

while the original served as the real part. This

complex-valued ECG signal was analyzed by the

BEMD method, resulting in 5-6 intrinsic mode

functions (IMFs). The first three IMFs, as suggested

by Agrafioti et al. (Agrafioti, et al., 2012), were

analyzed for dominant frequencies using spectrogram

analysis (Ferdinando, et al., 2016). The spectrogram

analysis relies on two parameters, i.e. window size

ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods

114

and overlap. The dominant frequencies of all 5 second

segments are collected and various features are

calculated as follows. The features are based on the

statistical distribution of the dominant frequencies

and their first difference: mean, standard deviation,

median, Q1, Q3, IQR, skewness, kurtosis, percentile

2.5, percentile 10, percentile 90, percentile 97.5,

maximum, and minimum. The results are groups in

three sets: feature1 (statistical distribution of the

dominant frequencies; 84 features), feature2

(statistical distribution of the dominant frequencies’

first difference; 84 features), and feature12 (combine

both feature1 and feature2; 168 features). The best

features are then selected from each group with

sequential forward-floating search. The number of

most selected features varies from two to twenty-

three, depending on whether valence or arousal is

recognized and the parameters used in the

spectrogram analysis (Ferdinando, et al., 2016).

3.2 Dimensionality Reduction

The chosen DR methods, LDA, NCA, and MCML,

are applied to the selected features from certain

window size and overlap parameters combination in

the spectrogram analysis found in (Ferdinando, et al.,

2016) to get features with lower dimensionality. The

initial matrix

A for NCA and MCML are generated

with a random number generator. It means that there

is no guarantee that they provide the optimum matrix

A in one pass. The algorithm is modified to be

iterative such that it stops – a flag is set – when there

is no improvement, validated using leave-one-out

cross-validation, within 200 iterations. The DR is

applied only in cases when the number of selected

features is greater than the target dimensionality. The

optimum projection matrix

A is saved for further

process.

3.3 Classifier and Validation Methods

We used kNN classifier as in (Ferdinando, et al.,

2016) to solve the original 3-class classification

problem for valence and arousal. 20% of the data are

held out for validation while the rest are subject to 10-

fold cross validation. The classifier model is built

based on the projection of the selected features using

the optimum projection matrix

A during the DR

phase. The whole validation process is repeated 100

times with new resampling in each iteration. The

average over the repetition represent the final

accuracy. When the accuracies from different

combinations of window size and overlap parameter

are close to each other, the final accuracy is justified

using the Law of Large Numbers (LLN).

Another validation for the result is leave-one-

subject-out (LOSO) validation. The main idea is to

evaluate if the transformed features are general

enough to work well with features from new subjects.

4 RESULTS

Table 1 to 4 show the best results from each target

dimensionality of each DR algorithm with 10-fold

cross validation and 100 iterations.

Table 1: Accuracy after applying LDA DR for valence and

arousal.

Dimensionality Valence Arousal

2D 55.1 ± 7.4

59.9 ± 6.8

Since this is 3-class problem, the highest

dimensionality that the LDA can yield is two.

Surprisingly, the accuracy for both valence and

arousal are close to (Ferdinando, et al., 2016). An

improvement, however, is less storage and faster

calculation than standard kNN.

Table 2: Accuracy after applying NCA DR for valence and

arousal.

Dimensionality Valence Arousal

2D 61.3 ± 7.2 65.6 ± 6.2

3D 57.0 ± 8.0 66.0 ± 8.1

4D 65.3 ± 6.5 60.1 ± 7.7

5D 64.5 ± 6.7 61.0 ± 8.1

6D 53.2 ± 7.6 61.5 ± 7.5

7D 60.4 ± 6.6 61.2 ± 7.2

Results from the NCA for both valence and

arousal are promising, since the best accuracies for

valence and arousal are even higher than in

(Ferdinando, et al., 2016).

Table 3: Accuracy after applying MCML DR for valence

and arousal.

Dimensionality Valence Arousal

2D 54.5 ± 7.9 60.5 ± 7.5

3D 54.6 ± 7.4 48.9 ± 7.3

4D 41.8 ± 6.9 49.3 ± 7.2

5D 41.9 ± 7.2 49.3 ± 7.1

6D 42.1 ± 7.6 49.2 ± 7.0

7D 43.5 ± 7.3 48.4 ± 8.9

The best results based on the MCML DR from

both valence and arousal are close to the ones in

(Ferdinando, et al., 2016). It also results in less

Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction

115

storage for the data and faster computation than

standard kNN.

Table 4 compares the results among LDA, NCA,

and MCML side-by-side. It shows that the NCA

outperforms the other methods. The difference is

roughly 10% and 5% for valence and arousal,

respectively.

Table 4: Best accuracies of the dimensionality reduction

methods.

LDA NCA MCML

Valence

55.1 ± 7.4 65.3 ± 6.5

(4D)

64.5 ± 6.7

(5D)

54.6 ± 7.4

(3D)

54.5 ± 7.9

(2D)

Arousal

59.9 ± 6.8 66.0 ± 8.1

(3D)

65.6 ± 6.2

(2D)

60.5 ± 7.5

(2D)

Since the most promising results in some cells in

Table 4 are close to each other, the Law of Large

Numbers is used to estimate accuracies as close as

possible to the true ones. After 1000 iterations, the

best results are in Table 5.

Table 5: Applying LLN based on Table 4.

LDA NCA MCML

Valence 54.2 ± 7.4 64.1 ± 7.4

(4D)

53.6 ± 7.3

(3D)

Arousal 59.8 ± 7.3 66.1 ± 7.4

(3D)

59.5 ± 7.1

(2D)

It is obvious that the NCA method outperforms

the others. The rest of the experiments are related to

LOSO validation. Table 6 to 8 summarizes these

experiments for valence and arousal.

Table 6: Accuracy after applying LDA DR for valence and

arousal in LOSO validation.

Dimensionality Valence Arousal

2D 56.5 ± 10.7

60.6 ± 9.1

The accuracies for both valence and arousal based

on LOSO validation reveal the same pattern as in 10-

fold cross validation (see Table 1), i.e. accuracy for

arousal is higher than that for valence. These

accuracies are also close to ones in Table 1. For

valence, the result came from the same window size

and overlap parameters in the spectrogram analysis,

but not for arousal.

By comparing Table 2 and Table 7, one can

observe that the best result from arousal came from

the same dimensionality. Looking into detail of the

experiments, one finds out that the best result also

came using the same window size and overlap

parameters in the spectrogram analysis. However, the

valence did not show this pattern.

Table 7: Accuracy after applying NCA DR for valence and

arousal in LOSO validation.

Dimensionality Valence Arousal

2D 61.7 ± 14.1 69.6 ± 12.4

3D 59.4 ± 11.6 51.1 ± 9.5

4D 44.0 ± 12.0 53.3 ± 11.0

5D 40.1 ± 12.0 47.3 ± 11.9

6D 40.0 ± 13.0 51.5 ± 8.6

7D 38.7 ± 11.1 45.7 ± 12.3

Table 8: Accuracy after applying MCML DR for valence

and arousal in LOSO validation.

Dimensionality Valence Arousal

2D 55.9 ± 9.3 61.7 ± 12.3

3D 56.3 ± 12.1 50.2 ± 9.8

4D 41.9 ± 10.6 50.2 ± 10.0

5D 38.8 ± 10.6 50.5 ± 10.4

6D 39.3 ± 11.0 50.3 ± 10.5

7D 39.1 ± 10.8 48.4 ± 8.9

Similar to the NCA result, the accuracy for

arousal also came from the same dimensionality and

parameters of the spectrogram analysis, but not for

valence.

Table 9: Accuracies of all dimensionality reduction

methods in LOSO validation.

LDA NCA MCML

Valence 56.5 ± 10.7 61.7 ± 14.1 56.3 ± 12.1

Arousal 60.6 ± 9.1 69.6 ± 12.4 61.7 ± 12.3

Significance assessment was performed using t-

test with significance level 0.05 for valence between

LDA and NCA methods. The p-value was 0.035

indicating that NCA was superior to LDA. For

arousal, the test showed (p-value 0.0016) that NCA

was superior to MCML.

5 DISCUSSION

As mentioned in the Supervised Dimensionality

Reduction section, DR with the LDA has a limitation

that it can only reduce the dimensionality to a number

not higher than the number of the classes. The other

algorithms can try to search for any dimensionality as

long as it is smaller than the dimensionality of the

original feature space. With this limitation, the LDA

did not provide any improvement for the accuracy but

ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods

116

can only save some storage space and computational

load.

The MCML, inspired by the NCA (Globerson and

Roweis, 2006), most of the time failed to find the

optimum projection matrix for the features. There

was no improvement to the accuracy of the system

compared to using the original feature set. It reduced

the dimensionality from four to three for valence and

from three to two for arousal. Similar to the LDA, the

contribution of the MCML is saving the storage space

slightly.

The NCA significantly improved the accuracy of

emotion recognition. The dimensionalities of the

feature set were reduced from twenty-three to four

and from twenty-two to three for valence and arousal,

respectively. For small number of samples, this might

be not significant but it will be different for the big

data analysis.

The result of this study is compared to the

accuracies from the previous study (Ferdinando, et

al., 2016), see Table 10.

Table 10: Comparison of results to a reference paper, 10-

fold cross validation.

Reference

(Ferdinando, et al.,

2016)

DR experiment

(NCA)

Valence 55.8 ± 7.3 64.1 ± 7.4 (4D)

Arousal 59.7 ± 7.0 66.1 ± 7.4 (3D)

We verify whether applying DR to features indeed

improves the accuracy of the system using t-test

method with significant level 0.05 and null

hypothesis that both are from the same distribution.

The p-values for both valence and arousal are close to

zero indicating that the improvements are significant.

Table 11: Comparison of results to a reference paper, LOSO

validation.

Original

(Ferdinando, et al.,

2016)

DR experiment

(NCA)

Valence 59.2 ± 11.4 61.7 ± 14.1

Arousal 58.7 ± 9.1 69.6 ± 12.4

We used t-test again to verify that applying DR

can improve the performance of the system in LOSO

validation with significant level 0.05. The p-values

were 0.1873 and 0.0001 for valence and arousal,

respectively, indicating that there is no significant

difference between the original and DR experiment

for valence but there is a significant improvement

with the arousal recognition.

During this study, the algorithms were modified

such that they are iterative with a simple stopping

criterion. Further studies related to iterative

algorithms is needed in order to get more benefits

from the supervised dimensionality reduction. It

might be possible also to investigate how to initialize

matrix

A without random number generator.

6 CONCLUSIONS

This paper explored supervised DR in emotion

recognition based on the Mahnob-HCI database. It

was shown that the supervised DR based on NCA

increased the accuracy from 55.8% to 64.1% and

from 59.7% to 66.1% for a 3-class problem in valence

and arousal respectively using 10-fold cross

validation. Compared to the initial baseline

(Ferdinando, et al., 2014), the accuracies improved

significantly by around 20%.

With LOSO validation, the supervised DR based

on NCA increased the accuracy of arousal recognition

from 58.7% to 69.6% for 3-class problem. However,

it failed to improve the accuracy for valence as

indicated by statistical significance test.

The generalisability of these results is subject to

certain limitations. For instances, the iterative

algorithm was very simple such that the whole system

failed to gain more benefits from the supervised

dimensionality reduction techniques. Another

important limitation is about matrix

A initialization

process which used random number generator. Using

a more sophisticated initialization might improve the

performance.

Among the three methods explored in this paper,

the NCA showed its superiority when it was applied

to the Mahnob-HCI database, although the MCML

was developed to improve the performance of the

NCA. Yet, it will be very interesting to explore the

same methods with other databases and various

applications in order to draw more comprehensive

conclusions for the supervised DR applied to emotion

recognition based on physiological signals.

ACKNOWLEDGEMENTS

This research was supported by by the Directorate

General of Higher Education, Ministry of Higher

Education and Research, Republic of Indonesia, No.

2142/E4.4/K/2013, the Finnish Cultural Foundation

North Ostrobothnia Regional Fund and the

Optoelectronics and Measurement Techniques unit,

University of Oulu, Finland.

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117

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