An Investigation of How Wavelet Transform Can Affect the Correlation

Performance of Biomedical Signals

The Correlation of EEG and HRV Frequency Bands in the Frontal

Lobe of the Brain

Ronakben Bhavsar, Neil Daveya and Yi Sun and Na Helian

The School of Computer Science, University of Hertfordshire, Hatﬁeld, U.K.

Keywords:

EEG, HRV, Biomedical Signal Processing, Time series Data Analysis, Pearson Correlation, Wavelet Trans-

form, Independent Component Analysis, Feature Extraction, Fast Fourier Transform.

Abstract:

Recently, the correlation between biomedical signals, such as electroencephalograms (EEG) and electrocar-

diograms (ECG) time series signals, has been analysed using the Pearson Correlation method. Although

Wavelet Transformations (WT) have been performed on time series data including EEG and ECG signals, so

far the correlation between WT signals has not been analysed. This research shows the correlation between

the EEG and HRV, with and without WT signals. Our results suggest electrical activity in the frontal lobe of

the brain is best correlated with the HRV. We assume this is because the frontal lobe is related to higher mental

functions of the cerebral cortex and responsible for muscle movements of the body. Our results indicate a

positive correlation between Delta, Alpha and Beta frequencies of EEG at both low frequency (LF) and high

frequency (HF) of HRV. This ﬁnding is independent of both participants and brain hemisphere.

1 INTRODUCTION

Biomedical signals are a record of electrical activity

within human body, and they may indicate the state

of health of human. Among many biomedical sig-

nals, Electroencephalograph (EEG) and Electrocar-

diograph (ECG) signals are considered in this work.

EEG signals provide a measure of brain nerve cell

electro-physiological activity, that is accessible on the

surface of the scalp (Lewis et al., 1988), thus pro-

vide information about different types of brain activ-

ity. Identifying changes in EEG signals has improved

our understanding of the relationship of these signals

to people

s moods, and behaviour (Han et al., 2012),

(Ebersole and Pedley, 2003). ECG signals contains

a plethora of information on the normal and patho-

logical physiology of the heart and its health. Fur-

thermore, ECG signals provide vital information with

regards to the function and rhythm of the heart. The

heart rate variability (HRV) has been extracted from

the ECG signals. HRV describes the variation in time

between consecutive heart beats, which is commonly

referred to as the RR (R wave to R wave) or NN (Nor-

mal beat to normal beat) intervals.

In recent years, the correlation between the EEG

and the ECG have been conducted to analyse their

functionality under certain conditions and to check

whether this functionality is related to each other. Re-

search (Kim et al., 2013), (Chua et al., 2012), (Ab-

dullah et al., 2009), (Sakai et al., 2007), (Berg et al.,

2005), (Edlinger and Guger, 2006), suggests that the

correlation between spectral bands of EEG and HRV

has been conducted to assess the interaction between

them, and achieved remarkable correlation.

The recent research on correlation between these

two signals as mentioned earlier has focused on the

Fourier analysis of the frequencies presents in these

signals. Whilst, the wavelet transform (WT), acts on

frequency and time of the recorded signals. There-

fore, WT has widely utilized for analysing biomedi-

cal or time series signals. The WT of the signal can

be thought of as an extension of the classic Fourier

transform (FT) - it works on multi-scale basis, instead

of working on a single scale (Time or Frequency) as

FT, and gives detailed and clear information of the

signals. Therefore, WT of the signals is an important

method not only to analyse EEG and ECG/HRV sig-

nals individually, but also to analyse the correlation

between them. According to recent research (Thomas

and Moni, 2016), (Chandra et al., 2017), (Mirsadeghi

Bhavsar, R., Davey, N., Sun, Y. and Helian, N.

An Investigation of How Wavelet Transform Can Affect the Correlation Performance of Biomedical Signals - The Correlation of EEG and HRV Frequency Bands in the Frontal Lobe of the

Brain.

DOI: 10.5220/0006551001390146

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 139-146

ISBN: 978-989-758-279-0

139

et al., 2016), (Mporas et al., 2015), (Valderrama et al.,

2012), (Nasehi and Pourghassem, 2011), (Cvetkovic

et al., 2008), WT has been used to analyse either

EEG or ECG signal, but the correlation between these

transformed signals has not yet been conducted. In

this paper we are not only focusing on the correlation

between without wavelet transform signals but also

between wavelet transformed signals.

2 RELATED WORK

A series of data points in time order, or time series,

provide the view of a signal as it evolves over time, in

the Time domain (TD). TD analysis is used to anal-

yse the signal in its actual state - it is utilised to anal-

yse changes in biomedical signals, such as the power

(or amplitude) over time. In addition, the frequencies

present in the signal are open to investigation (for ex-

ample, by using the Fast Fourier Transform (FFT)).

Such an analysis is said to take place in the Frequency

domain (FD). The FD analysis is used to identify fre-

quencies present in the signals. Furthermore, it can be

utilized to establish the relationship between frequen-

cies and its corresponding power (amplitude), and so

the energy distributions in signals.

In recent research, the correlation between EEG

and ECG/HRV signals have been analysed in the FD

,as shown in Table 1, which indicates that the Pear-

son correlation is the best method for the FD analy-

sis. In addition, different numbers of EEG electrodes

have been used to analyse the relationship with the

ECG/HRV. To the best of our knowledge, very lim-

ited work has been done on the correlation between

EEG and ECG/HRV signals using 19 EEG electrodes.

Moreover, no one has analysed these signals under the

same condition (i.e. with TEAS acupuncture applied)

that utilised in this research. This paper investigates

the correlation between EEG and ECG/HRV signals

in FD using Pearson correlation considering all 19

EEG electrodes under the same condition.

Based on the research as shown in Table 2 on WT,

it is straightforward that the DWT based methods are

well known for EEG and ECG feature extraction and

analysis. Furthermore. Among the DWT based meth-

ods mentioned, db wavelet method has been consid-

ered by the researchers. It is obvious from the re-

search on WT that key features of EEG and ECG sig-

nal can improve the analysis performance. Therefore,

it is important to analyse not just either EEG or ECG

as shown in Table 2, but also the correlation between

EEG and ECG. To our knowledge, we have not yet

found information on the correlation between wavelet

transformed signals. In this work, we describes such

an analysis.

3 DATASET INFORMATION

Two different datasets were obtained with each of

them containing different numbers of participants,

stimulation location, and total time length as shown in

Table 3. All of these datasets follow the 10-20 elec-

trode placement system shown in Figure 1. The 10-20

system is the recognized method to describe the lo-

cation of electrodes (Klem et al., 1999). The values

of 10 and 20 percentage shown in Figure 1 refer to

the distances between adjacent electrodes: either 10

or 20 percentage of the total front-to-back or right-to-

left distance over the skull - front-to-back distance is

based on the measurement from the Nasion (point be-

tween forehead and nose) to the Inion (lowest point

of the skull from the back of the head indicated by a

prominent bump), and right-to-left distance is based

on the measurement between the left and right preau-

ricular ear points.

Dataset 1 and 2 consist of EEG and ECG recordings

from 16 and 7 participants, respectively. These data

were obtained over ten 5 minutes slots with eyes open

using Transcutaneous Electro Acupuncture (TEAS)

method, including resting state data in the ﬁrst and

the last slot. The EEG and ECG recording were made

simultaneously. 19 electrodes (Fp1, Fp2, F7, F3, Fz,

F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1,

and O2) for EEG recording were used, following the

10-20 system. The sampling rate used for EEG was

250Hz, and the reference was to linked ear electrodes.

For ECG data, two electrodes were placed on both

side of the wrist (having one electrode as ground) to

record the electrical activity of the heart over time,

and the sampling rate used was 256Hz.

Table 3: Information about the Datasets.

Label Dataset 1 Dataset 2

Number of Participants 16 7

EEG-Electrodes 19 19

EEG-Sampling Rate 250Hz 250Hz

ECG-Electrode 1 1

Stimulation Location 1 4

ECG-Sampling Rate 256Hz 256Hz

Total Time Length 50 minutes 45 minutes

Slot Time Length 5 minute 5 minute

The difference between these datasets, other than

the participants, is the body location where TEAS

stimulation has been performed. For Dataset 1, only

one body location (Dominant Hand), and for Dataset

2, four different body location (Left Hand, Below Left

Knee, Right Hand, and Below Right Knee) has been

used to perform TEAS stimulation.

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

140

Table 1: Summary of Correlation Research on Biomedical Signals since 2003 to 2017.

RefDetail TD FD Pearson Correlation Method Other Correlation Method EEG Electrodes Investigated

(Miyashita et al., 2003) - X X - 4

(Yang et al., 2002) - X X - 2

(Ako et al., 2003) - X X - 1

(Jurysta et al., 2003) - X - Coherency Analysis 3

(Takahashi et al., 2005) - X X - 6

(Edlinger and Guger, 2006) - X X - 2

(Berg et al., 2005) - X X - 2

(Sakai et al., 2007) - X X - 19

(Abdullah et al., 2010) - X - Cross-correlation 1

(Chua et al., 2012) - X - X 4

(Kim et al., 2013) - X - Coherency Analysis 19

(Prinsloo et al., 2013) X - X - 3

(Liou et al., 2014) - X X - 19

(Triggiani et al., 2016) - X X - 19

Table 2: Summary of Research on Well known Wavelet Transformation Methods for Biomedical Signals since 2012 to 2017.

RefDetail EEG ECG/HRV TD FD Feature Extraction Method

(Kutlu and Kuntalp, 2012) - X X - DWT-Daub Wavelet

(Thomas et al., 2015) - X X - DWT-Daub Wavelet

(Sudarshan et al., 2017) - X X - DWT-Daub Wavelet

(Acharya et al., 2017) - X - X DWT-Daub Wavelet

(Dolatabadi et al., 2017) - X X X Principal Component Analysis (PCA)

(Kumari et al., 2014) X - X X DWT-Daub Wavelet

(Mumtaz et al., 2017) X - X X DWT-Daub Wavelet

(Kevric and Subasi, 2017) X - - X DWT-Daub Wavelet

(Faust et al., 2015) X - X - DWT-Daub Wavelet

Figure 1: The international 10-20 system seen from A (left side of the head) and B (above the head). The letter F, T, C, P, O,

A, Fp and Pg stands for frontal, temporal, central, parietal, occipital, earlobes, frontal polar, and nasopharyngeal, respectively.

The ﬁgure is obtained from (Klem et al., 1999).

4 METHODS

4.1 Pearson Correlation

The Pearson

s correlation coefﬁcient measures how

closely two different observables are related to each

other. Correlation co-efﬁcient range between 1 (when

the matching entities are exactly the same) and −1

(when the matching entities are inverses of each

other). A value of zero indicates no relationship ex-

isting between the entities.

4.2 Wavelet Transform

The Wavelet Transform (WT) is designed to direct

the problem of signals with nonstationarity. It in-

An Investigation of How Wavelet Transform Can Affect the Correlation Performance of Biomedical Signals - The Correlation of EEG and

HRV Frequency Bands in the Frontal Lobe of the Brain

141

cludes representation of time function in terms of sim-

ple blocks, termed wavelets. These blocks are derived

from a signal generating function called the mother

wavelet by translation and dilation operations. Dila-

tion, also known as scaling, compresses or stretches

the mother wavelet and translation shifts it along the

time axis (Daubechies, 1990), (Akay, 1997), (Unser

and Aldroubi, 1996). The WT can be categorized into

continuous and discrete. Continuous wavelet trans-

form (CWT), implies that the scaling and translation

parameters change continuously, and thus, represent

considerable effort and vast amount of data calcula-

tion for every possible scale. Therefore, we used dis-

crete wavelet transform (DWT). The WT of the sig-

nal can be thought of as an extension of the classic

Fourier transform (FT) - it works on multi-scale ba-

sis, instead of working on a single scale (Time or Fre-

quency) as FT. This is achieved by decomposition of

the signal over dilated (scale) and translated (time)

version of wavelet. An input signal is decomposed by

using low pass ﬁlter and high pass ﬁlter followed by

down sampling in each stage. The output of the ﬁrst

stage high pass ﬁlter gives the detail coefﬁcient (D1),

whereas the low pass ﬁlter gives the approximation

coefﬁcient (A1).

The prototype wavelet used in this study is

Daubechies wavelet of order 4 (db4) based on our re-

search on biomedical/time series signal analysis, as

mentioned in Table 2.

5 EXPERIMENTAL SET-UP

The experimental steps are shown in Figure 2. The

EEG signals were pre-processed to remove artefacts

caused by the electrical activity in muscles includ-

ing eye, jaw and muscle movements using Indepen-

dent Component Analysis (ICA). It was straightfor-

ward to remove these using ICA (Hyv

arinen and Oja,

2000). The power spectrum for each frequency band

of EEG - Delta (0.3-4 Hz), Theta (4-7.5 Hz), Alpha

(7.5-13 Hz), Beta (13-30 Hz), and Gamma (30-50

Hz) were then obtained by Power Spectrum Density

(PSD) (Stoica and Moses, 1997).

To extract HRV from ECG signals, we used

method designed by Lin et al. (Lin et al., 2010).

The results of the automatic analysis were reviewed

and any errors in R-wave detection and QRS labelling

were then removed manually. R-R interval data ob-

tained from the edited time sequence of R-wave and

QRS labelling were then transferred to a personal

computer. In order to remove artefact from extracted

HRV signal, each R-R interval has been compared

against a local average interval. If an R-R interval

differs from the local average more than a speciﬁed

threshold (Threshold in seconds) value, then that R-

R interval is deﬁned as an artefact and is replaced

with an interpolated value using a cubic spline in-

terpolation. The power spectrum for each frequency

band of HRV - Very Low Frequency (VLF) ranges 0-

0.04 Hz, Low Frequency (LF) ranges 0.04-0.15 Hz,

and High Frequency 0.15-4 Hz were then obtained by

PSD (Power Spectrum Density).

The sampling rate is 1Hz for the extracted HRV,

and 250Hz for the EEG. In order to perform corre-

lation between these different sampling rate signals,

it was required to change the sampling rate for either

the EEG or HRV signals. Therefore, we decided to

segmenting EEG signals using 1 second window and

represent each window by its means value (the mean

value from each 250 samples), unlike normal down

sampling, where much of the data is thrown away.

For each participant’s EEG data, this process has been

repeated for all 5 minutes slots. After windowing,

the spectral analysis was performed. From each fre-

quency bands of the EEG and the HRV, the mean of

the amplitude value within the frequency range has

been measured, single value for each of these fre-

quency band, and for each 5 minute is obtained. Then,

the correlation between these frequency values is per-

formed.

In order to perform correlation based on wavelet

transformed EEG and/or HRV signal, the WT-

Daubechies (db) Wavelet up to level 5 is performed

on the signals before extracting frequency bands as

mentioned in Figure 2. For the datasets we have, the

low pass ﬁlter worked very well. Therefore, we con-

sidered low passed WT signals to perform the corre-

lation.

Figure 2: Experiment steps for the correlation performance.

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6 EXPERIMENTAL RESULTS

AND DISCUSSION

For each dataset, we investigated the correlation be-

tween each of the EEG frequencies (Delta, Theta, Al-

pha and Beta) with each frequencies of the HRV fre-

quencies (LF and HF) in three different experiments:

1). The Correlation between Pre-processed Signals,

2). The Correlation between Pre-processed and WT

signals of the EEG and HRV, and ﬁnally 3). The

Correlation between Pre-processed HRV with Pre-

processed and WT signals of the EEG. The Gamma

frequency of EEG did not give us the correlation ef-

fect. Therefore, it is not considered in the result

shown in Figure 3 and Table 4.

For both datasets, the experiment 2). correlation

between both WT signals did not give better results,

because HRV is tend to be less noisy. Therefore, when

the WT has been performed on HRV, information has

been lost and the signal became more ﬂat. The most

interesting result has been found from experiments 1).

and 3).

For each frequency combination correlation, the

average of participants for each EEG electrode has

been calculated. Then the best performance electrode

has been ranked- where, the ranking has been given

based on electrode correlation result. The average of

electrode ranking for each frequency combination is

then gathered and ﬁve best performance electrodes re-

sult has been looked closely. We have found some

common electrodes in all of the frequency combina-

tion we have investigated. Figure 3 shows the result

of this investigation for Dataset 1 and 2.

As shown in Figure 3, for dataset 2, some elec-

trodes from the back side of the brain are giving

stronger result than dataset 1. This is due to more

randomness in the EEG signals from dataset 2. Also,

the location where TEAS has been performed might

contributed to this result.

Based on results shown in Figure 3, it can be

seen that the frontal lobe of the brain is correlated

with the heart. The frontal lobe involved in higher

mental functions, such as concentration, creativity,

speaking, muscle movement and in making plans and

judgements, is a part of cerebral cortex (body’s ulti-

mate control and information processing) of the brain

(McCraty et al., 2009). The usual Heart-Brain com-

munication path is through spinal cord. In order to

have relationship between frontal lobe of the brain

and heart, we assume the communication might have

done through ’Medulla’(cardiovascular center placed

in medullacontrols the heart beating) which is part of

brain stem. The signal has been then directed to the

Thalamus and then to the cerebral cortex (Lane et al.,

2001), (ATKINSON and BRADLEY, 2004).

Table 4 shows the average correlation result of

participants for each frequency comparison from

dataset 1 and 2. Where, Level 0 means correla-

tion between pre-processed data, and Level 1 to 5

means, correlation between pre-processed HRV with

pre-processed and WT EEG. The heat map of these

result (”Red” is strongest and ”Dark-Blue” means

weakest) as shown in Table 4, indicates the correla-

tion performance changes with the levels of WT. We

found the signal became ﬂat after level 2, and lost in-

formation when levels has been increased. Therefore,

we have not considered result of levels 3, 4 and 5 in

Figure 3 (b) and (d).

Results shown in Table 4 are indicative and not

statistically signiﬁcant, according to these, three fre-

quencies of EEG have shown some correlation, such

as Delta, Alpha, and Beta, have shown correlation at

both LF and HF of HRV. Each of these frequencies

represent the activities of these signals. For example,

Delta will be higher if the person is in deep sleep, Al-

pha will appear if the person is calmed, relaxed or

in creative visualisation, and Beta will show if the

person is working or feeling more alert. For HRV,

LF and HF represent the sympathetic and parasympa-

thetic activities of autonomic nervous system (ANS),

respectively.

7 CONCLUSIONS

The main conclusion of this work is that electrical ac-

tivity in the frontal lobe of the brain is correlated with

the HRV for the given two datasets. To the best of

our knowledge this is a new result. This suggests that

most probably the electrical signals could be trans-

mitted through the cerebral cortex, Thalamus, and

Medulla of the brain (Saper et al., 2005). The possi-

ble path of the key neuronal projections that maintain

alertness is shown in Figure 4.

The second conclusion from this work is that, WT

signals also give correlation from the frontal lobe of

the brain. To the best of our knowledge, the correla-

tion between WT signals of EEG and ECG/HRV has

not yet been investigated.

A more tentative conclusion of this work is that

three frequencies of the EEG Delta, Alpha and Beta

are correlated with the LF and HF of HRV, for dataset

1 and dataset 2, respectively. Whereas, most of previ-

ous studies, (Yang et al., 2002),(Ako et al., 2003),(Ju-

rysta et al., 2003),(Abdullah et al., 2010) and (Chua

et al., 2012), have shown negative correlation between

these frequency bands due to the condition in which

these signals have been analysed.

An Investigation of How Wavelet Transform Can Affect the Correlation Performance of Biomedical Signals - The Correlation of EEG and

HRV Frequency Bands in the Frontal Lobe of the Brain

143

Figure 3: Best Electrodes Correlation Performance, highlighted in yellow colour: (a) Dataset 1 Correlation performance

on pre-processed HRV and EEG, (b) Dataset 1 Correlation performance on pre-processed HRV and WT signals of EEG,(c)

Dataset 2 Correlation performance on pre-processed HRV and EEG, (d) Dataset 2 Correlation performance on pre-processed

HRV and WT signals of EEG.

Table 4: Heat Map Results of Averaged participants correlation performance: Dataset 1 (Left), and Dataset 2 (on Right).

Colour coding from Red to Dark Blue, Red=Strongest, Dark-Blue=Weakest).

In summary, the number of EEG electrodes used

by other people to investigate correlation was limited.

Our results cover a gap in the research concerning the

correlation between the EEG and the HRV using all

EEG electrodes. Our work suggests a correlation be-

tween the frontal lobe of the EEG and the HRV, with

and without WT signals. We assume this is because

the frontal lobe is related with higher mental functions

of cerebral cortex and responsible for muscle move-

ments of the body (Stuss and Benson, 1986).

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Figure 4: Key neuronal projections that maintain alertness,

and possibly the path from cardiovascular center to the

frontal lobe of the barin’s communication. The ﬁgure is

obtained from (Saper et al., 2005).

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