From ECG signals to images: a
transformation based approach for deep
learning
Mahwish Naz1, Jamal Hussain Shah1, Muhammad Attique Khan2,
Muhammad Sharif1, Mudassar Raza1 and Robertas Damaševičius3
1
COMSATS University Islamabad, Wah, Pakistan
2
HITEC University, Taxila, Pakistan
3
Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
ABSTRACT
Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular
tachyarrhythmia is an irregular and fast heart rhythm that emerges from
inappropriate electrical impulses in the ventricles of the heart. Different types of
arrhythmias are associated with different patterns, which can be identified.
An electrocardiogram (ECG) is the major analytical tool used to interpret and record
ECG signals. ECG signals are nonlinear and difficult to interpret and analyze.
We propose a new deep learning approach for the detection of VA. Initially, the ECG
signals are transformed into images that have not been done before. Later, these
images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3
deep learning models. Transfer learning is performed to train a model and extract the
deep features from different output layers. After that, the features are fused by a
concatenation approach, and the best features are selected using a heuristic entropy
calculation approach. Finally, supervised learning classifiers are utilized for final
feature classification. The results are evaluated on the MIT-BIH dataset and achieved
an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).
Subjects Artificial Intelligence, Computer Vision, Data Mining and Machine Learning
Keywords ECG, Deep features, Image processing, Deep learning, Convolutional neural networks,
Submitted 13 October 2020 Feature fusion
Accepted 18 January 2021
Published 10 February 2021
INTRODUCTION
Corresponding author
Robertas Damaševičius,
In 2015, according to the United Nations report, the world is facing an aging population.
robertas.damasevicius@vdu.lt The number of people aged 60 years or more will rise to 56.00% by 2030 or double by
Academic editor 2050 (Escobar, 2011). One of the main fatalities throughout the world is cardiovascular
Consolato Sergi ailments. The human cardiovascular system weakens as we grow older and it is more
Additional Information and likely to suffer from arrhythmias. A ventricular arrhythmia is an irregular heartbeat of
Declarations can be found on
ventricular rhythm. If not treated in time, it can cause life in danger. Ventricular
page 15
fibrillation (Vfib), atrial fibrillation (Afib) and atrial flutter (Afl) are the recurrent
DOI 10.7717/peerj-cs.386
dangerous arrhythmias that can disturb the aging population (Van Huls Van Taxis, 2019).
Copyright
2021 Naz et al. Ventricular arrhythmias (VA) reduces ventricular function. It may cause the need for
Distributed under
implanting a fixed cardioverter defibrillator due to the occurrence of VA during long-
Creative Commons CC-BY 4.0 standing follow up in patients affected with hypothetical myocarditis (Sharma et al.,
2019b).
How to cite this article Naz M, Shah JH, Khan MA, Sharif M, Raza M, Damaševičius R. 2021. From ECG signals to images: a
transformation based approach for deep learning. PeerJ Comput. Sci. 7:e386 DOI 10.7717/peerj-cs.386
, Different types of arrhythmias are associated with different heartbeat patterns. It is
possible to identify these patterns and their types. An electrocardiogram (ECG) is the
prime diagnostic tool that works to interpret ECG signals. ECG is a non-invasive recording
by skin electrodes that is processed by an ECG device. An ECG shows a voltage between
electrode pairs and the muscle activities of the heart that are measured from different
directions (Bosznai, Ender & Sántha, 2009). The ECG is an analytic apparatus that
processes the electrical action and records the actions of the heart. Interpretation of these
subtleties permits determination in a comprehensive scope of heart ailments. These heart
ailments can differ from insignificant to hazardous (Elola et al., 2019). To thoroughly
see how an ECG uncovers essential data about the state of your heart requires a
fundamental comprehension of the life systems and physiology of the heart (Cunningham
et al., 2016; Ionasec et al., 2016; Izci et al., 2019). These different kinds of arrhythmias
can further be categorized into two major categories. The first one is a single irregular
heartbeat, formed arrhythmias, which are called morphological arrhythmias. The second
one forms by a set of irregular arrhythmias (Luz et al., 2016).
Patients who are suffering from cardiac disease need intervention immediately.
For this automated recognition of unusual heartbeats, translation by ECG signals is
fundamental. The manual evaluation of these signals is time-consuming and tedious
(Acharya et al., 2018). According to new research, (Nigussie & Tadele, 2019) heart attacks
and ventricular tachyarrhythmia (VTA) once categorized as “old man’s disease” are
now gradually occurring in younger people, especially in women. These irregular rhythms
can cause damage to the heart muscle from cardiomyopathy. Now, the major issue is that
as we grow older, the human cardiovascular system is more receptive to diseases and
becomes weaker (Krbcová et al., 2016). Vfib and VTA are the major arrhythmias reported
in the elders (Chow, Marine & Fleg, 2012). While cardiologists can recognize distinctive
heartbeat morphologies precisely among various patients, the manual assessment is
repetitive and tedious (Srinivas, Basil & Mohan, 2015). The standard deferral between
the atria and ventricles contraction of the heart is 0.12–0.20 s. This deferral is superbly
coordinated to represent the physical path of the blood from the upper chamber to
the ventricle. Intervals can be longer or shorter than this range show potential issues
(Madhavapeddi, Verrier & Belardinelli, 2018; Rabey, Cohen & Belhassen, 2018; Sharma
et al., 2019a). Figure 1 shows a visualization of the QRS complex.
The most dangerous rhythm is a type of polymorphic ventricular tachycardia (VT)
called Vfib (Ibtehaz, Rahman & Rahman, 2019). There are many techniques for the
detection of VTA. The most common is the modified Karhunen–Loeve transform, which
has been done using a pattern recognition method. Prediction of arrhythmias by applying
pattern recognition techniques on ECG data is an emerging and important task in
biomedical engineering (Mishra, Arora & Vora, 2019). However, it requires continuous
observation of a patient using, for example, wearable sensors (Girčys et al., 2020).
Cardiovascular cycle elements reflect basic physiological changes that could predict
arrhythmias; however, are obscured by high complexity, no stationary and large
inter-individual differences (Sabherwal, Agrawal & Singh, 2019).
Naz et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.386 2/18
, Figure 1 Graphical representation of QRS complex in ECG.
Full-size DOI: 10.7717/peerj-cs.386/fig-1
Recently, deep learning shows huge success in the medical domain (Anjum et al., 2020).
It is used for many imaging (Khan et al., 2020a, 2020b; Fernandes, Rajinikanth & Kadry,
2019), disease recognition (Sahlol et al., 2020a, 2020b; Capizzi et al., 2020), analysis of
biomedical signals (Bakiya et al., 2020), Internet-of-Things domain (Huifeng, Kadry & Raj,
2020; Muthu et al., 2020) and epidemic disease spread forecasting (Wieczorek., Siłka &
Woźniak, 2020; Wieczorek et al., 2020) tasks.
This paper introduces a new approach to predict VTA and classify various arrhythmias
using a novel technique. In this technique, we transform ECG signals into binary images.
Our approach differs from other approaches know from the literature as commonly
ECG signals are transformed into series data. As a result, deep learning models such as
convolution al neural network (CNN) does not work properly on ECG signals data because
the minor value of signals data is ignored in the QRS complex thus preventing from
accurate recognition of arrhythmias. It is a big challenge to convert the serial signals data
into images and further proceed for the detection of VTA.
The novelty and contribution of the article are as follows:
A novel approach to convert ECG signals into 32 32 binary images.
A fusion of features from several deep CNNs for VTA recognition.
The entropy-based feature selection is employed for obtaining the best feature subset.
The selected features are finally trained using different classifiers, and higher accuracy is
attained as compared to the existing method.
Here are the key advantages that are achieved using our proposed methods:
No need for complex pre-processing of ECG signals.
No need for the QRS complex detection.
Higher accuracy than previous CNN based arrythmia detection techniques.
Less time consumption for arrythmia detection.
Naz et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.386 3/18
