A Machine Learning Approach for Carotid Diseases using Heart Rate

Variability Features

Laura Verde

and Giuseppe De Pietro

Department of Technology, University of Naples Parthenope, 80143 Naples, Italy

Institute of High Performance Computing and Networking (ICAR),

National Research Council of Italy (CNR), 80131 Naples, Italy

Keywords:

Carotid Diseases, Atherosclerosis, HRV Analysis, Machine Learning Techniques, Support Vector Machine.

Abstract:

In the last few years the incidence of carotid diseases has been increasing rapidly. Atherosclerosis constitutes

a major cause of morbidities and mortalities worldwide. The early detection of these diseases is considered

necessary to avoid tragic consequences and automatic systems and algorithms can be a valid support for

their diagnosis. The main objective of this study is to investigate and compare the performances of different

machine learning techniques capable of detecting the presence of a carotid disease by analysing the Heart Rate

Variability (HRV) parameters of opportune electrocardiographic signals selected from an appropriate database

available online on the Physionet website. All the analyses are evaluated in terms of accuracy, precision, recall

and F-measure.

1 INTRODUCTION

During the recent decade the incidence of carotid

atherosclerosis has been increasing dramatically.

This disease accounts for 20% of ischemic stroke

ogberg et al., 2016; Lockau et al., 2015;

Yesilot Barlas et al., 2013) constituting a major cause

of morbidities and mortalities worldwide (Raﬁeian-

Kopaei et al., 2014). Atherosclerosis is a multifacto-

rial vascular disorder with several genetic and envi-

ronmental causes involving multiple arterial vessels.

It is characterized by an accumulation of lipids, ﬁ-

brous materials and mineral in the arteries, that causes

the formation of placque (Riccioni et al., 2003). This,

in turn, causes a decrease of the blood ﬂow and dam-

age to the organs with, in some cases, very serious

consequences.

To prevent and reduce the resulting disabilities,

appropriate medical therapy and risk factor control

can be adopted. In addition, the early detection and

accurate diagnosis of this disorder are necessary.

Several medical techniques are used to diagnose

carotid diseases. Carotid Doppler ultrasonography

(US) is the most frequently used tool for the evolution

of atherosclerosis of the carotid artery (H

ogberg et al.,

2016; Grant et al., 2003). It is preferable to other

diagnostic techniques, such as computed tomography

or magnetic resonance, due to non-invasiveness, easy

repeatability and low cost. It allows you to perform

an accurate morphological and hemodynamic study

of the arterial axis, and to locate and evaluate the site

and severity of the arterial lesion responsible for the

symptomatology. In particular, it is used to measure

the intima-media thickness (IMT), the best biomarker

for atherosclerosis, useful for the placque characteri-

zation.

Unfortunately, there is no standardization for the

execution of carotid US examinations. This can cause

errors in the performance of this examination. The

most common mistakes are due to an incorrect posi-

tioning of the Doppler probe at the wrong Doppler

angle (Grant et al., 2003), with as a result the possi-

bility of serious errors in the diagnosis. In addition,

the Doppler signals can be inﬂuenced by any motion

of the walls of the blood vessels, whose ﬂuctuations

can cause incorrect estimates of these disorders.

To avoid unwanted diagnostic errors and the ex-

cessive costs of other medical examinations, we have

aimed to identify a helpful supplementary diagnostic

supplementary tool that can relate the carotid arterial

wall thickness and Heart Rate Variability (HRV) anal-

ysis using machine learning techniques. Such a tech-

nique represents one of the main estimators of car-

diovascular systems (Camm et al., 1996), whose rela-

tionship with carotid diseases is demonstrated in sev-

eral studies existing in literature (Kwon et al., 2008;

Verde L. and De Pietro G.

A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features.

DOI: 10.5220/0006730806580664

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF 2018), pages 658-664

ISBN: 978-989-758-281-3

Kaufman et al., 2007; Chao et al., 2003). Many of

these report, in fact, a decrease of the HRV parameters

in subjects suffering from atherosclerosis, an associa-

tion that could be due to ischemic damage to the car-

diac nerves (Fakhrzadeh et al., 2012; Gotts

ater et al.,

2006). In addition, due to the easy derivation of HRV

measurements, many commercial devices provide for

their automated measurement.

In detail, in this study we have investigated and

compared the performance of several machine learn-

ing techniques capable of identifying the presence

of carotid diseases using the HRV parameters as the

features for different classiﬁers. The performances

are evaluated in terms of accuracy, precision, recall

and F-measure for each considered machine learning

method considered.

The paper is organized as follows. In Section 2,

we present the main studies about techniques able to

estimate the carotid diseases existing in literature. In

Section 3, we introduce our experimental phase, in

detail we focus on dataset used in data analysis, the

HRV features considered for the classiﬁcation and the

machine learning techniques evaluated. The obtained

results are presented in Section 4, while conclusions

are provided in Section 5.

2 RELATED WORK

The analysis of heart rate variability (HRV) from elec-

trocardiographic (ECG) signals has become an im-

portant method for the assessment of cardiovascular

regulation (Huikuri et al., 1999). Several algorithms

have been developed for the automated characteriza-

tion of coronary artery disease (CAD) using linear

and non-linear (Kim et al., 2007; Verma et al., 2016)

HRV features, often using artiﬁcial intelligence tech-

niques. Kim et al. (Kim et al., 2016), for example,

proposed an extraction methodology from ultrasound

images for carotid diseases and HRV parameters use-

ful in the diagnosis of cardiovascular diseases. Addi-

tionally, Lee et al. (Lee et al., 2008) investigated the

identiﬁcation of cardiovascular diseases by evaluating

various features of HRV and carotid wall thickness,

comparing several machine learning techniques.

There are, however, not many studies concern-

ing the automated characterization of carotid diseases

and, unfortunately, these use private and not public-

ity available databases limiting the reproducibility of

the test. Polat et al. (Polat et al., 2007), for exam-

ple, identify atherosclerosis analysing carotid doppler

signals with the Fuzzy weighted pre-processing and

Least Square Support Vector Machine (LSSVM). The

doppler signals used were acquired from 114 subjects

and analysed to evaluate the power spectral density

and sonogram using the Autoregressive (AR) method.

Additionaly, Dirgenali et al. (Dirgenali and Kara,

2006) evaluated Doppler sonograms to distinguish be-

tween healthy subjects and those with atherosclerosis.

The adopted technique classiﬁed the Doppler signals,

selected from, also in this case, a private database, us-

ing Artiﬁcial Neural Networks (ANN) and Principles

Component Analysis (PCA) for the data reduction.

A combining neural network model was imple-

mented by Ubeyli et al. (

Ubeyli and G

uler, 2005),

instead, for the diagnosis of ophthalmic and inter-

nal carotid arterial disorders. Also in this case

the Doppler signals were selected from a private

database.

3 MATERIALS AND METHODS

In this study we have evaluated the performance of

several machine learning techniques capable of iden-

tifying the presence of atherosclerosis using HRV

features. The analyses have been performed using

WEKA Data Mining toolbox (Witten et al., 2016),

one of the most commonly used systems due to its

reliability, efﬁciency and ease of use.

In our experimental phase, we have selected sev-

eral ECG signals from an appropriate database. These

signals were processing to extract the features of in-

terest useful in the classiﬁcation phase using different

machine learning techniques, thanks to we have esti-

mated the presence of carotid diseases or not. This

procedure, used in the development of the classiﬁca-

tion method, is shown in the Figure 1.

In the following subsections we introduce the

dataset used in data analysis, the HRV features con-

sidered for the classiﬁcation and the machine learning

techniques evaluated.

Figure 1: The ﬂowchart of carotid health state classiﬁcation.

3.1 Dataset

In our research, we selected the ECG Holter record-

ings of 126 patients from the ”Smart Health for As-

Table 1: HRV features.

HRV Features Description

Linear

Features

Mean RR Mean of RR intervals

SDRR Standard deviation of the RR intervals

SDNN Standard deviation of NN intervals

SDSD Standard deviation of the successive difference RR in-

tervals

RMSSD Square root of the mean of the squares of the succes-

sive differences between adjacent NNs

HF Components High Frequency power, from 0,15 Hz to 0,4 Hz

LF Components Low Frequency power, from 0,04 Hz to 0,15 Hz

VLF Components Very Low Frequency power, from 0 Hz to 0,04 Hz

Nonlinear

Features

SD1 Standard deviation of the distance of RR(i) from the

line y=x in the Poincar

e plot

SD2 Standard deviation of the distance of RR(i) from the

line y=-x+2RR in the Poincar

e plot

sessing the Risk of Events via ECG database (Melillo

et al., 2012)” available online on the Physionet web-

site (Goldberger et al., 2000).

It consists of the recordings of 89 subjects suffer-

ing from carotid diseases and 37 healthy subjects, in-

cluding 80 males (59 pathological and 21 healthy),

and 46 females (30 pathological and 16 healthy).

The pathological state was identiﬁed by analysing

the IMT value, evaluated with the B-mode ultrasound

and considered to be a objective maker for the esti-

mate of atherosclerosis.

3.2 Feature Extraction

Heart Rate Variability is, generally, used as a clin-

ical tool to evaluate the cardiac autonomic function

(Camm et al., 1996). It is based on the analysis of RR

intervals, the series of time intervals between heart-

beats.

Traditional HRV measures are distinguished into

two categories: time domain measures and frequency

domain measures. The mean, standard deviation,

square root of the mean of successive RR intervals

difference and other time domain measures are widely

utilized to quantify the overall variability of the heart

rate. Frequency domain features of HRV, instead, pro-

vide information about the cardiac autonomic regula-

tion. Finally, the HRV analysis provides several non-

linear features useful to estimate the variability and

regularity of the regulatory system of the heart rate.

In detail, the HRV features used in our study are

reported in Table 1, where normal-to-normal (NN) in-

tervals are deﬁned as intervals between adjacent QRS

complexes resulting from sinus node depolarizations.

All features are calculated by means of the Pan-

Tompkins algorithm (Pan and Tompkins, 1985) using

the Matlab software.

3.3 Classiﬁcation Methods

To classify the signals we executed several test choos-

ing different machine learning algorithms. These

techniques are:

Support Vector Machine (SVM): The idea of the

SVM algorithm is to create a hyperplane between

datasets and indicate which class it belongs to. The

main advantages of the SVM method are its ﬂexi-

bility, remarkable resistance to overﬁtting and sim-

plicity. The accuracy performance obtained with the

SVM technique can be improved by changing the ker-

nel function K(x,y) (Sch

olkopf et al., 1999; Vapnik,

1999), choosing a polynomial kernel function or ra-

dial basis function (RBF). In this study, we applied the

sequential minimum optimization (SMO) algorithm

(Platt, 1998).

Bayesian Classiﬁcation: This approach takes its

name from Thomas Bayes, who proposed the Bayes

Theorem. The classiﬁcation is based on a probabilis-

tic model that represents a set of random variables

and their conditional dependencies, identiﬁed, respec-

tively, as nodes and strings, by means of an acyclic

oriented graph (John and Langley, 1995).

Decision Tree: This algorithm is one of the most

widely used and practical methods to classify cate-

gorical data based on their attributes, features that

described each considered case. Decision tree algo-

rithms begin with a set of cases and create a tree data

structure. We used J48, an implementation of algo-

rithm C4.5 (Salzberg, 1994).

Table 2: Results obtained for several q values for polynomial kernel.

q 1 2 3 4 5 6

Accuracy (%) 72.22 72.22 70.63 70.63 70.63 70.63

Recall (%) 97.75 98.88 98.88 98.88 100.00 100.00

Precision (%) 72.50 72.13 70.97 70.97 70.63 70.63

F-measure (%) 83.25 83.41 82.63 82.63 82.79 82.79

Table 3: Results obtained for several γ values for RBF kernel.

γ 0.01 0.50 0.70 0.90 1.00 1.50 2.00

Accuracy (%) 70.63 69.84 70.63 69.84 69.05 69.05 70.63

Recall (%) 100.00 98.88 98.88 95.51 94.38 95.51 98.88

Precision (%) 70.63 70.40 70.97 71.43 71.19 70.83 70.97

F-measure (%) 82.79 82.24 82.63 81.73 81.16 81.34 82.63

Multilayer Perceptron: In 1958 Rosenblatt pre-

sented the notion of the single perceptron, concept on

which the Multilayer Perceptron algorithm is based.

He introduced the idea that a single output from mul-

tiple real-valued inputs is calculated by a perceptron,

a network of simple neurons. A linear combination

according to its input weights is developed and the

output is presented through some non-linear activa-

tion functions (Ruck et al., 1990).

Logistic Model Tree: In this technique the logis-

tic regression models is combined with tree induc-

tion. It consists of a standard decision tree structure

where the leaves are the logistic regression functions.

In Weka this algorithm is implemented by the Simple

Logistic class (Landwehr et al., 2005).

Instance-based Learning algorithm: This ap-

proach generates classiﬁcation predictions using only

speciﬁc instances. The algorithms used are k-nearest

neighbor (k-NN) (Aha et al., 1991), that looks at the

k nearest neighbors of a new instance to decide which

class the new instance should belong to (Ibk in Weka)

and K* (Cleary et al., 1995), an instance-based clas-

siﬁer that uses an entropy-based distance function un-

like other instance-based learners (kStar in Weka).

4 RESULTS AND DISCUSSION

Due to the limited number of samples, cross-

validation was used to validate the feature vector cal-

culated. In detail we used a 10-fold cross-validation,

such as we partitioned randomly the dataset into k=10

equal size subsdatasets. From these latter, a single

subset is considered as the validation set for testing

the model while the remaining k-1 subdatasets consti-

tute the training data. This process is repeated k times

where each k subdatasets is used to validate data.

We deﬁned the following measurements:

• True positive (TP): the input sample is pathologi-

cal and the algorithm recognizes this;

• True Negative (TN): the input sample is healthy

and the algorithm recognizes this;

• False Positive (FP): the input sample is healthy but

the algorithm recognizes it as pathological;

• False Negative (FN): the input sample is patho-

logical but the algorithm recognizes it as healthy.

The ﬁnal results were evaluated in terms of accu-

racy, precision, recall and F-measure, deﬁned as fol-

lows:

Accuracy =

(T P + T N)

(T P + T N + FP + FN)

(1)

Precision =

T P

(T P + FP)

(2)

Recall =

T P

(T P + FN)

(3)

F − measure =

(2 ∗ Precision ∗ Recall)

(Precision + Recall)

(4)

In the ﬁrst phase of the experimental analysis we

performed a series of tests using the Support Vector

Machine classiﬁer, one of the most usually used ma-

chine learning algorithms. As indicated in the Sub-

section 3.3, it is possible to improve the classiﬁca-

tion accuracy of this algorithm by changing the Ker-

nel value and expression. The Kernel function can,

in fact, be indicated in the polynomial form or with

the radial basis function (RBF) (Burges, 1998). In

the ﬁrst case, the kernel is expressed as a polynomial

function of degree q, that is:

K(x

, x

) = (x

+ 1)

(5)

Table 4: Results achieved with the main machine learning techniques.

Classiﬁer Accuracy (%) Recall (%) Precision (%) F-measure (%)

SMO 72.22 98.88 72.50 83.25

Bayesian Classiﬁcation 70.63 100.00 70.63 82.79

Decision Tree (J48) 69.05 97.75 70.16 81.69

Multilayer Perceptron 71.43 96.63 72.27 82.69

Ibk 66.67 74.16 77.65 75.86

Kstar 52.38 62.92 67.47 65.12

Logistic Model Tree 72.22 97.75 72.50 83.25

Figure 2: Results achieved with the main machine learning techniques.

where x

and x

are two input samples, x

is trans-

posed and q is the degree of the polynomial function,

which can be selected by the user. When q = 1, we

have the linear kernel that corresponds to the original

formulation.

The RBF kernel, instead, is indicated as:

K(x

, x

) = exp[−

||x

− x

] (6)

where x

and x

are two input samples and s is se-

lected by user. It can be, also, expressed as:

K(x

, x

) = exp[−γ(||x

− x

||)

], γ > 0 (7)

deﬁning the parameter γ as:

γ =

|1|

(8)

In this work the q parameter for the kernel poly-

nomial and the γ one for RBF were experimentally

investigated to achieve the best classiﬁcation result.

Several performances obtaining changing q pa-

rameters for the kernel polynomial are indicated in the

Table 2. Overall, the best performances were obtained

for q = 2 in terms of accuracy, recall and F-measure,

although, the precision result is slightly lower than

that obtained for q = 1.

In the Table 3 several results for different γ values

are shown. In this case the several obtained perfor-

mance are similar. The best performance are obtained

with a γ value equal to 0.01.

Analysing the results obtained using both the

polynomial kernel and RBF kernel, the best perfor-

mance in terms of accuracy was achieved by using

the polynomial function.

The results obtained were compared with results

achieved with the main machine learning techniques

considered. These results are shown in the Table 4 and

in the Figure 2. The lowest performances were ob-

tained using the Instance-based Learning algorithms

(Ibk and kStar), while the best results were achieved

using the SMO classiﬁer.

5 CONCLUSIONS

Nowadays, the incidence of degenerative atheroscle-

rosis is rapidly increasing. Automated classiﬁcation

systems can constitute a valid support for the early

detection of this disorder, offering several advantages:

they are fast, non-invasive, easy to perform and inex-

pensive.

In this study, a comparison between the most fre-

quently used machine learning techniques has been

carried out, by evaluating their performance in clas-

sifying the presence of carotid diseases using HRV

features. HRV analysis represents one of the main

approach able to estimate functionality of cardiovas-

cular system, in relationship with carotid diseases

as demonstrated by several studies existing in liter-

ature. We have considered several machine learning

methods including Support Vector Machine, Bayesian

Classiﬁers, Decision Tree, Multilayer Perceptron, Lo-

gistic Model Tree and Instance-based Learning algo-

rithms. The Support Vector Machine has proved to be

more effective than any of the other methods consid-

ered.

In future work, we will explore the possibility of

improving the classiﬁcation obtained by using a hy-

brid system constructed by combining several ma-

chine learning techniques and methods for data reduc-

tion, such as for example the Principles Component

Analysis (PCA), necessary to reduce computational

complexity and execution times.

ACKNOWLEDGEMENTS

This work has received funding from the European

Unions Horizon 2020 Framework Programme for

Research and Innovation under grant agreement no

727528 (KONFIDO). The authors would like to ac-

knowledge Prof. Luigi Romano, of the Department

of Technology, University of Naples Parthenope for

his technical contribution.

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