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Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

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Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

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Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.
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electronics
Article
Multiclass ECG Signal Analysis Using Global Average-Based
2-D Convolutional Neural Network Modeling
Muhammad Wasimuddin 1, Khaled Elleithy 1, Abdelshakour Abuzneid 1, Miad Faezipour 1,2,* and
Omar Abuzaghleh 1


Citation: Wasimuddin, M.; Elleithy,
K.; Abuzneid, A.; Faezipour, M.;
Abuzaghleh, O. Multiclass ECG
Signal Analysis Using Global
Average-Based 2-D Convolutional
Neural Network Modeling.
Electronics 2021,10, 170. https://
doi.org/10.3390/electronics10020170
Received: 3 December 2020
Accepted: 11 January 2021
Published: 14 January 2021
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional clai-
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Copyright: © 2021 by the authors. Li-
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This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://
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4.0/).
1Department of Computer Science & Engineering, University of Bridgeport, Bridgeport, CT 06604, USA;
mwasimud@my.bridgeport.edu (M.W.); elleithy@bridgeport.edu (K.E.); abuzneid@bridgeport.edu (A.A.);
oabuzagh@bridgeport.edu (O.A.)
2Department of Biomedical Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
*Correspondence: mfaezipo@bridgeport.edu; Tel.: +1-203-576-4702
Abstract:
Cardiovascular diseases have been reported to be the leading cause of mortality across
the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of
main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions
and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable
diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring
systems with advanced machine learning methods provide information about the health status in
real-time and have improved user’s experience. However, advanced machine learning methods
have put a burden on portable and wearable devices due to their high computing requirements. We
present an improved, less complex Convolutional Neural Network (CNN)-based classifier model
that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in
real-time. The proposed model is presented as a three-layer ECG signal analysis model that can
potentially be adopted in real-time portable and wearable monitoring devices. We have designed,
implemented, and simulated the proposed CNN network using Matlab. We also present the hardware
implementation of the proposed method to validate its adaptability in real-time wearable systems.
The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and
achieved an accuracy of 99.23%, outperforming most existing solutions.
Keywords: ECG Signal Analysis; Cardiac Arrhythmia; ST Detection; ECG Classification; CNN
1. Introduction
1.1. Background
Coronary Heart Disease (CHD), also known as Cardiovascular Disease (CVD) is a
result of lack of blood supply to the heart organ. CHD is attributed to many different
types of arrhythmia, which are generally defined as irregular, slow, or rapid heart beats.
Acute Myocardial Infarction (MI) can result in death, but fatalities usually depend on the
severity of an arrhythmia. The rising mortality rates due to these heart diseases have
demanded early diagnosis of cardiac conditions before they progress to acute MI and
leading to death. The most common tool to diagnose different types of arrhythmia is
Electrocardiogram (ECG). Thorough analysis of ECG has gained much attention among
researchers to accurately and effectively diagnose arrhythmia and critical cardiac conditions.
Traditional diagnosis involves bedside ECG recording and the physician’s presence to
analyze and diagnose a condition. However, such methods are time consuming and in
most severe cardiac cases, time is crucial in diagnosing such a condition.
Real-time monitoring has overcome this shortcoming by sending the acquired ECG
data from the body-attached portable and wearable device to a central location for auto-
mated diagnosis using advanced machine learning methods and algorithms. Real-time
monitoring systems have reduced the requirement of patients traveling to the clinic and the
Electronics 2021,10, 170. https://doi.org/10.3390/electronics10020170 https://www.mdpi.com/journal/electronics
Electronics 2021,10, 170 2 of 29
doctor’s or physician’s presence. It has improved the telehealth experience for most users
and facilitates early diagnosis and treatment towards saving lives. Early diagnosis can save
costs in the healthcare industry, as about 17.9% of the national cost is due to CVD [
1
]. ECG
analysis and classification in real-time monitoring systems is carried out through multiple
processes described by the stages-based model in Reference [
2
]. These processes can be
grouped into three steps, that is, data acquisition, preprocessing and feature engineering
and classification for machine learning approaches. One such approach is proposed in this
study.
In this study, we aim to detect Normal (N), ventricular ectopic (V), and ST-segment
changes that contribute towards the diagnosis of MI. We build on top of our prior work [
3
]
and present a three-layer model for the classification of ECG into three classes of Normal,
ST-change, and V-change types. The model is based on a Convolutional Neural Network
(CNN) that works at layer three of the model and trained on 2-D ECG images; a snapshot
of the ECG wave between two consecutive R-peaks. The extracted ECG within every two
consecutive R-peaks is referred to as an image for the purpose of the machine learning
experiments in this study. The proposed model is referred to as our 2-D CNN throughout
this paper. This model can be used for real-time monitoring and integrated with portable
and wearable devices for ECG acquisition and preprocessing. The images can be sent
to a central location for classification by applying our proposed 2-D CNN. Devices like
smartwatches and smartphones are now an integral part of our daily lives. These devices
can be leveraged as portable and wearable devices for real-time monitoring of the health
status. This encourages the users’ cooperation to accept this as a diagnostic support tool
towards improving health and the quality of life.
To evaluate our 2-D CNN classifier, we have acquired ECG signals from the publicly
available European ST-T database (ESCDB) [
4
]. Though the standard 12-lead ECG and/or
ECG collected from several leads provide most effective results, recent research has shown
that ECG data acquired from a single lead, mostly used in portable real-time monitoring
devices such as smartphones and smartwatches [
5
7
], has started to gain acceptance for
detecting certain arrhythmia types and pathological rhythms such as atrial fibrillation.
Consecutive recording of multiple single-lead ECG can also be effective for detecting
ischemic diseases and MI [
5
]. Reduced number of leads has tremendous benefits in real-
time monitoring systems as less number of sensors will be attached to the body surface,
making it more convenient for any user to employ. To this end, ESCDB is an appropriate
database for evaluating algorithms and techniques in this context, as it has single-lead
ECG recordings. The CNN algorithm in this work is implemented in Matlab and the
preprocessing of ECG data is achieved in Python 3.7.
1.2. Electrocardiography
Electrocardiography, although invented more than a century ago by a Dutch physiolo-
gist Willem Einthoven, still remains the most useful and readily available investigation in
the field of cardiology throughout the world. The Electrocardiogram (ECG) is the graph
showing the recorded cyclic electrical activity of the heart, received through the electrodes
of the ECG leads attached to the body surface, as shown in Figure 1. The electrical ac-
tivity is generated by the cardiac tissues, as small potentials, which are amplified by the
electrocardiograph and recorded on graph paper as ECG. The ECG is usually recorded
with twelve leads, known as conventional leads. The first set of six leads is called limb
leads, which record the potential across the frontal plane. These six limb leads are further
divided into three standard limb leads, also known as bipolar leads and three unipolar
limb leads, named AVR, AVL, and AVF . The second set of six conventional leads is called
precordial leads, also known as chest leads. These leads record the potential across the
horizontal plane and are named V1 to V6. These twelve conventional leads are oriented
towards the heart in such a manner that reflect, together a two-dimensional view in two
perpendicular planes, the frontal plane and the horizontal plane. A third dimension in the
sagittal plane can be added, for example, by an esophageal ECG or the measurement may
Electronics 2021,10, 170 3 of 29
be done using three orthogonal vectorcardiographic leads such as in the Frank’s orthogonal
lead system containing seven electrodes [
8
]. However, with the growing technology and
computer vision techniques, the diagnosis of heart conditions from analyzing the ECG
signal recorded with a single lead, or recorded sequentially from multiple single-lead
ECG, has started to gain clinical acceptance in the recent years, especially using portable
monitoring devices [
5
]. These advances can further be employed to significantly improve
the real-time detection and automated diagnosis of different cardiac conditions such as MI,
also known as “heart attack” and ischemic heart disease.
Figure 1. Electrocardiogram (ECG) conventional leads orientation.
The importance and significance of electrocardiography is well recognized not only
in the diagnosis of MI, but also in detecting conduction disturbances or abnormalities
and various types of arrhythmia. The advent of modern medical therapy of unstable
angina and acute MI, and the development of interventional cardiology, has increased
the significance of electrocardiography in contemporary cardiology. The ECG waves and
fiducial points are named in alphabetical order. These are called the P, QRS-complex,
and T-waves, as shown in Figure 2. The R-peak is generally known as the focal point of
an ECG beat, and the time interval between two consecutive R-peaks is called the R-R
interval (also referred to as the RR interval). The amplitude, shape, and time interval
of these waves provide significant information about the state of the heart. The QRS-
complex reflects the ventricular depolarization generated by the positive wave when
impulse spreads towards the positive pole of the respective ECG lead [
9
,
10
]. MI usually
affects the ventricles, and therefore the QRS abnormalities are associated with the ST-T
abnormalities. The ST-segment elevation is mostly the earliest change in acute MI and its
early detection is of much significance from the medical treatment point of view. Premature
ventricular complexes (PVC), also known as ventricular ectopic (V), are the premature
beats originating from slow ventricular activation and reflected as a wide QRS complex in
ECG. These may reflect underlying cardiac diseases, such as the ischemic heart disease.
According to the Association for the Advancement of Medical Instrumentation (AAMI),
the non-life threatening arrhythmia types can be categorized into five classes: non-ectopic
or Normal (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and other
unknown (Q) categories [
11
]. The process of detecting different types of arrhythmia into its
appropriate cardiac condition is called classification.
Electronics 2021,10, 170 4 of 29
Figure 2. ECG R-R interval and fiducial points.
1.3. Three-Layer Process
Diagnosis of a cardiac condition requires proper detection of the arrhythmia with
Computer-Aided Design tools such as machine learning techniques. The detection process
involves ECG signal acquisition, removal of noise by preprocessing, identifying the fiducial
points in the ECG wave such as the QRS-complex and ST-segment, as shown in Figure 2,
and classifying it into different types of arrhythmia. We present these steps as a structured
model consisted of a three-layer process, shown in Figure 3. Layer 1 behaves as the sensor
layer responsible for data acquisition. Layer 2 acts as a coordinator between the first and
third layer and is responsible for any preprocessing. Layer 3 has the role of a central location
responsible for identifying and classifying the ECG signal. We explain the implementation
of this three-layer model as follows:
The very first step in any ECG analysis is to acquire the ECG signal. For evaluation
of algorithms and techniques, ECG signals are usually acquired from publicly available
databases. We have acquired ECG signals from the ESCDB database at the first layer of this
process and evaluated our method. The selection of datasets is discussed in Section 5.3. We
have also evaluated our method in real-time with ECG signals acquired from the AD8232
sensor discussed in Section 6.4. The second layer consists of ECG preprocessing, since
the acquisition of the ECG signal inherits embedded circuit noise and external power-line
noise. It is very crucial to filter these noise sources for better detection of the fiducial
points, classification and reducing false negatives which are more critical compared to
false positives. We have used average filtering to denoise the ECG signal and to perform
different experiments in this study, discussed in Section 5.3. However, there are other
methods to filter noise from ECG data, such as the ones presented in References [
12
16
].
Since our model uses 2-D images with a CNN, we need to convert the ECG signal from
one-dimension (1-D) to a (two-dimensional) 2-D image at this preprocessing layer. This is
achieved using Python by plotting the ECG signal between every two consecutive R-peaks,
and saving it as an image. Layer 3 takes these images as input to our proposed 2-D CNN,
trains the network with automated feature engineering and classifies the ECG image into
three classes representing either “Normal”, “ST-change”, or “V-change”. The proposed 2-D
CNN model is discussed in detail in Section 5.4.
Electronics 2021,10, 170 5 of 29
Figure 3. Layer-based ECG Analysis Process.
1.4. Motivation
Ever since the COVID-19 pandemic has started, it has not only flooded the hospitals
with patients and limited the number of intakes, but has also changed the way treatment
is provided. Patients, now, have to make an appointment, wait in the parking lot for the
room to be available and sanitized, and confront limited availability of physicians. This
has introduced another layer of hurdle in the early diagnosis and treatment of cardiac
conditions. The rapid rise of COVID-19 has intrigued many researchers to perform research
in this area and develop real-time continuous monitoring systems that can identify these
cardiac conditions at the user’s residence. This motivated us towards this study as a
contribution to the ongoing research of real-time ECG analysis and classification.
In the recent years, numerous methods and techniques have been developed for real-
time monitoring systems, but these are evaluated with ECG data recorded with multiple
leads [
17
19
], available on public databases such as MIT-BIH (MITDB) [
20
]. Real-time
monitoring systems heavily depend on portable and wearable devices such as body-
attached sensors, patches, smartwatches and smartphones. Such sensors and devices
usually record and acquire the ECG data with a single lead to improve user experience,
convenience, and acceptance. This is our second motivation to perform this study to
analyze ECG data recorded with a single lead for real-time monitoring. We have used the
single-lead ECG data provided by the ESCDB database to evaluate our method and to
support the reliability and accuracy of real-time diagnosis, detection, and monitoring.
Most advanced machine learning techniques have analyzed ECG data in one-dimension
(1-D) to classify different arrhythmia types. However, their models generally employ many
layers in the form of a neural network or CNN structure. The computational complexity
of such structures is discussed later in Section 7. It is seen that complexity grows with
more layers in the CNN structure. Complex designs and systems do not perform at fairly
high and acceptable accuracy levels. CNN and deep learning are known best for computer
vision and image classification. It is worth exploring the smart and automated feature
engineering capability of a CNN to learn ECG fiducial points and classify based on super-
vised learning. This motivated us even further to perform this study by taking the 1-D
ECG signal and converting it into a two-dimensional (2-D) image to train and evaluate our
proposed 2-D CNN model and classify ECG into three classes.
Electronics 2021,10, 170 6 of 29
2. Key Contributions
This study aims to contribute to the growing area of research in ECG analysis and
detection of different arrhythmia types in real-time to prevent various cardiac conditions
and to improve telehealth practices. In this paper, a less complex CNN structure is proposed
that can be feasible for real-time ECG monitoring, particularly useful for detecting the
onset of a heart attack, with extensive experiments and analysis performed on the ESCDB
database. Our contributions to this area of research can be summarized as follows:
1.
Present an overview of ECG and its significance in detecting different arrhythmia
types and cardiac conditions.
2.
Present a layer-based model for ECG analysis including acquisition, preprocessing
and classification processes and summarize the components.
3.
Present an optimized CNN network based on a global averaging technique to improve
the classification accuracy significantly.
4.
Present a detailed literature review of ECG analysis and classification algorithms
using traditional and machine learning approaches for both offline simulations and
real-time systems.
5.
Discuss and present the proposed CNN architecture and summarize its components
and parameters for our simulation results.
6.
Present detailed results for three simulation experiments performed using our pro-
posed model and its comparison with related work in this area.
7.
Present a hardware implementation of our proposed model in accordance with the
three-layer ECG analysis process.
8.
Discuss ECG classification and outline applications for real-time monitoring systems,
including portable and wearable devices and ECG sensor networks for the adaptation
of our proposed model.
3. Paper Organization
This paper is organized as follows—in Section 4, the detailed literature and related
work is presented for this paper. This section is further divided into two sections. In
Section 4.1
, we provide detailed related work of traditional approaches for ECG classifi-
cation.
Section 4.2
describes the related work of machine learning approaches for ECG
classification. The proposed model is explained in Section 5with four subsections, dis-
cussing ECG acquisition and preprocessing in Sections 5.1 and 5.2, respectively. Dataset
preparation for the experiments is described in Section 5.3, and the architecture of the
proposed CNN model is explained in Section 5.4. In Section 6, we provide details of the
experiments performed in this study, followed by four subsections that present details of
four experiments. Sections 6.16.3 present details of the first, second, and third experiments,
including results and graphs, respectively. In Section 6.4, we present a real-time monitoring
system of our proposed model using hardware implementation. In Section 7, results of the
experiments are discussed, and performance evaluations are presented. Research tools and
applications of our proposed model are presented in Section 7.1. Section 8concludes this
paper and provides insights for future directions.
4. Related Work
Typically, an output of a system is based on a function applied to the input. Traditional
algorithms work in such scenarios where outputs are generated by applying predefined
rules or functions to the inputs. These rules or functions remain the same. Traditional
algorithms require manual observation and optimization to achieve the desired results.
Whereas machine learning algorithms generate the output, optimize the underlying func-
tion and then regenerate the output that is close to the expected output in an automated
fashion. Both traditional and machine learning algorithms have applications towards
clinical diagnosis. We present related work in this section that have used these methods to
analyze the ECG signal for the detection and classification of its different arrhythmia types.
Traditional approaches of ECG analysis generally refer to conventional signal processing
Electronics 2021,10, 170 7 of 29
techniques that employ various filters and time and/or frequency domain transforms. This
is while machine learning based ECG analysis techniques are relatively newer compared to
traditional approaches. These techniques involve machine intelligence to learn the trend of
data and make predictions.
ECG analysis and classification rely upon proper identification of fiducial points
such as the ST-segment and QRS-complex. The process of fiducial points detection is
called Feature Engineering (FE). Traditionally FE is performed by manually observing
the ECG graph by a doctor, which results in a diagnosis. In the recent development
of modern technology with real-time monitoring systems, fiducial points such as the
QRS complex and arrhythmia detection is now an automated FE process performed by
mathematical techniques such as the Tompkins Wavelet Transform (WT) [
21
,
22
], machine
learning techniques such as CNN [
23
], and arrhythmia detectors by Long-Short Term
Memory (LSTM) [
24
] evaluated with data acquired by sensors. Fiducial points detection
methods that are evaluated with the ESCDB dataset include thresholding and windowing
techniques [
25
,
26
], time-domain techniques [
27
29
] for ST-segment detection, and position-
based QRS detectors [
30
]. Other methods include WT [
31
33
], Discrete Wavelet Transforms
(DWT) [
34
37
], Windowing Algorithms [
38
] and Finite Impulse Response (FIR)-based
adaptive filters [39].
However, our proposed 2-D CNN model eliminates the need of FE as it learns these
features on the fly during the training cycle with convolutions and feature maps. Therefore,
we find it unnecessary to report performance metrics for the FE methods discussed above.
One can refer to Reference [
2
], a recent survey paper on ECG signal analysis, for further
review of these FE methods and their performances reported in the literature. In this study,
we glance at traditional and machine learning approaches for classification of ECG into
different arrhythmia types and detection of ST-segment changes and ischemia (i.e., MI).
Furthermore, a comparison is provided between our proposed approach and others.
4.1. Traditional Approaches
Ischemia detection by analyzing the ST-segment deviation using Isoelectric Energy
Function (IEF) was introduced in Reference [
40
] and has been evaluated on the ESCDB
database. In Reference [
41
], the authors presented a method that uses the Pan-Tompkins
algorithm to detect the ST-segment deviations with a success rate of 97.03% and error of
2.97% on the ESCDB database. ST deviation (elevation or depression) based classification
of arrhythmia into normal and abnormal classes was presented by Reference [
42
] and
achieved a sensitivity of 98.2%, and 97.17% positive predictive value (ppv) when evaluated
with the ESCDB database. A Time-frequency based approach to classify MI was proposed
by Reference [
43
] and achieved 94.23% accuracy, 95.72% sensitivity and 98.15% specificity
when evaluated with the ESCDB database. Another ischemic beat classification with
Genetic Algorithm (GA) and Multicriteria Decision Analysis (MDA) was presented by
Reference [
44
] and achieved 91% for both sensitivity and specificity. A rule-based method
to classify ST morphology into normal and abnormal was introduced in Reference [
38
]
and achieved 90.1% accuracy and 98.9% sensitivity when evaluated with ESCDB. Another
method has been introduced by Reference [
45
] to detect ischemia based on statistical
features of the ST-segment deviation and performed classification of normal and abnormal
beats with 97.71% sensitivity and 96.89% ppv on the ESCDB database.
4.2. Machine Learning Approaches
Using machine learning techniques, the authors of Reference [
46
] have proposed
employing Decision Trees (DT) and Random Under Sampling (RUS) boosting-based tech-
niques to detect the ST-segment and T-wave anomalies in ECG from the same ESCDB
database with a sensitivity of 86%. In Reference [
36
], ST-deviation is detected by an
ensemble classifier-based backpropagation neural network. The deviation is obtained
by subtracting the detected ST-segment from the isoelectric level of its beat. They have
achieved sensitivity of 90.75%. The basic Support Vector Machine (SVM) is a kind of
Electronics 2021,10, 170 8 of 29
administered learning model, which is generally known as a binary classifier and groups
information into two classes using isolating hyperplanes. SVM was proposed by Vapnik,
an algorithm that extracts a function to classify unknown data [
47
] and mainly separates
data into two classes based on supervised learning. This makes SVM a strong classifier
candidate for application towards ECG signal classification into two classes of normal and
abnormal [
48
]. There are variations of SVM such as a Multiclass Support Vector Machine
(MSVM) and Complex Support Vector Machine (CSVM) that can be used to classify ECG ar-
rhythmia types into multiple classes, as presented by Reference [
49
]. In Reference [
35
], the
authors have detected the ST-segment episodes and changes and have classified arrhythmia
into six classes using SVM. The Rule-Based Decision Tree (RBDT) approach to classify
ischemic and arrhythmic beats into normal and abnormal was introduced as a fuzzy expert
system by the authors of Reference [
50
]. Rules are derived based on the ST-segment value
depending on the time between the R-peak and the start of the ST-segment slope. In Refer-
ence [
51
], the authors have used an ensemble learning technique called Adaptive Boosting
(AdaBoost) also known as meta-learning, used to enhance binary classification efficiency in
detecting abnormal beats from the ECG signal and have evaluated on three databases of
MITDB, QT [
52
], and ESCDB. Studies have shown that Artificial Neural Networks (ANN)
are powerful data analysis tools. Analysis of ECG with an ANN-based approach to detect
ischemic episodes was presented by Reference [
53
]. In Reference [
54
], the authors presented
a Multi-Module Neural Network System (MMNNS) to classify S and V heartbeats evalu-
ated on the MITBIH and ESCDB databases. A Densely connected CNN (DenseNet) based
classifier which classifies four ECG patterns was presented by Reference [
55
] and evaluated
on two databases, including the ESCDB database. Classification of ST-segment into normal,
depressed, and elevated levels using multiple features extracted with the Random Forest
(RF) technique was achieved in Reference [
56
] with 86.9% accuracy, 85.18% sensitivity (ST
normal), 87.35% sensitivity (ST depressed), and 88.06% sensitivity (ST elevated) on the
ESCDB database. CNN is best known for computer vision applications and works great in
image classification. Two dimensional (2-D) CNN model to classify arrhythmia types using
ECG signal as a converted image has been presented by References [
57
59
] and evaluated
on the MITBIH database.
5. Proposed Model
Our proposed model works in four steps as follows:
1. ECG data acquisition, which is explained in Section 5.1
2.
Preprocessing of the acquired data for the denoising process and conversion of 1-D
ECG signal to 2-D image, explained in Section 5.2
3.
Data is organized into multiple datasets. Description of this procedure is explained in
Section 5.3. The organized datasets are used to train the proposed CNN architecture
to perform multiple experiments described in Section 6.
4.
A 2-D CNN model is trained on the organized datasets. Explanations are provided in
Section 5.4.
Each of these steps of the proposed model are further explained in their respective sec-
tions.
5.1. Data Acquisition
Automatic detection and classification of cardiac conditions such as ischemia and
MI with advanced machine learning techniques requires evaluation of these methods,
techniques and algorithms for better accuracy to avoid false positives and false negatives.
For research and evaluation purposes, clinically pre-recorded ECG signals are publicly
available to evaluate the efficiency and performance of these methods. In this study, we
have used the ESCDB available on Physionet data bank website https://www.physionet.
org. This database contains ECG waveforms recorded by a Holter Machine with recordings
of 2 h per patients and includes 70 males and 8 females, aged 30 to 84 years old. Each
patient was suspected of myocardial ischemia as diagnosed. Each annotated recording
Electronics 2021,10, 170 9 of 29
contains ECG data collected with two ambulatory chest leads; Lead 3 (L3) and Lead 5 (L5);
sampled with 250 Hz as the sampling frequency, and 5
µ
V as the amplitude of the smallest
step (precision) measured in voltage. The annotations provide the beat type, gender, age,
clinical outcome, imbalance in electrolytes, and a summary of its pathology. This database
was coordinated by the Institute of Clinical Physiology of the National Research Council
(Pisa, Italy) and the Thoraxcenter of Erasmus University (Rotterdam, Netherlands). While
this database provides annotations per beat, it is found to have nonischemic ST-segment
changes due to drifts in the ST-segment deviation level or postural changes leading to false
positives. On the other hand, it may contain beats detected as ischemic but with no ST-T
change in nature, leading to false negatives. Finally, the definition of ischemia has been
updated since this database has been posted. Examples of the ECG waveforms of Lead 3
and Lead 5 from the ESCDB database are shown in Figure 4.
Figure 4. ECG signals within R-R interval extracted from Lead 3 (top) and Lead 5 (bottom).
5.2. Preprocessing
In real-time ECG monitoring, once the ECG signal is acquired at Layer 1 of the ECG
signal analysis process, it is sent to a coordinator at Layer 2 for further processing. This
coordinator can be a Personal Digital Assistant (PDA), smart App on a smartphone, or a
microcontroller device that processes this ECG data. The second layer is mainly responsible
for cleaning (denoising) the ECG signal and the detection of R-peaks so that the ECG wave
can be captured between two consecutive R-peaks called R-R interval and transformed
into a 2-D image for classification with our 2-D CNN model at Layer 3.
The running ECG signal is first cleaned from any noise sources (such as internal/
embedded or external noise) that may have been introduced by sensors or location at
the time of acquisition. There are many methods available to denoise the ECG signal
such as State Space Recursive Least Square (SSRLS) adaptive filter [
13
], Adaptive Notch
Filter (ANF) [
14
] and Fast Fourier Transform (FFT) [
15
]. However, for removing Gaussian
noise, impulse noise or salt and pepper noise from 1-D signals and/or 2-D images, linear
filters such as the average and weighted filters can be used. Weighted filters can reduce
high frequency components, but sharp details in the signal or image may be lost [
60
].
As this study proposes a method for real-time systems, the acquired ECG signal may be
contaminated with power-line, power supply or radio signal noise. Therefore, we have
used a rather simple and less complex average window filter for denoising such noise
sources [
61
]. A moving average window filter takes the average of the neighboring values
while moving along the ECG signal. The number of neighboring values becomes the
window size of the filter. It removes fluctuations and smooths out the noise by working as
a low pass filter. After many iterations, the optimized window size of 5 (
N=
2) has been
found to provide the optimum trade-off between greater amount of noise reduction and
loss of signal details such as compromising the signal shape and/or morphology of the
fiducial points [
60
] to produce a clean ECG signal. This denoising filter process is applied in
Electronics 2021,10, 170 10 of 29
real-time on the running ECG signal regardless of the position of the R-peaks and the other
fiducial points, and is performed before transforming the 1-D ECG signal into a 2-D image
representation. The moving average filter can be mathematically defined by Equation (1).
xi=1
2N+1
j=N
j=N
x(i+j). (1)
As the cleaned ECG signal between every two consecutive R-peaks is converted to a
2-D image in this preprocessing layer, the R-peaks should be detected accurately after de-
noising and before signal to image conversion. There are methods such as Discrete Wavelet
Transform (DWT) [
62
], Windowing Algorithm [
63
], Empirical Mode Decomposition (EMD)
based RR detector algorithm [
64
] and selective decomposition [
65
] for R-peak detection
in real-time monitoring systems. A complete cycle of the ECG can be detected based on
discrete data presented in Reference [
66
]. Adaptive thresholding and local maximums with
search-back mechanisms can be used effectively to detect the R-peaks. However, for the
purpose of simulation, we have used the R-peak annotations on the pre-recorded ECG
signals. Using Python 3.7 IDE, the “rdsamp” function of the WaveForm-DataBase (WFDB)
package reads the record file that contains the cleaned ECG signal and uses the starting and
ending sample numbers to plot the ECG signal in a two dimensional image. These starting
and ending sample numbers are the sample numbers of two consecutive R-peaks and are
provided to “rdsamp” function for each R-R interval to generate the image containing one
complete cycle of the ECG wave.
Figure 5depicts an example of a noisy and cleaned Lead 3 signal after the moving
average denoising filter is applied. It can be observed that the quality of the signal has
improved without compromising the characteristics of the ECG wave. The extracted
cleaned ECG wave between every two consecutive R-peaks is then converted into a 2-
D image using the Python code and sent to Layer 3 for classification into one of the
three classes. The conversion of 1-D ECG signal to 2-D ECG image can be assumed as
a snapshot (screenshot) taken between every two consecutive R-peaks and stored as an
image whose pixel intensity values, throughout the image, altogether compose the shape
of the ECG wave.
Figure 5. Average filter denoising.
5.3. Dataset Preparation
Initially, we have selected thirty records from the ESCDB database, which are grouped
into four collections. We have then selected images (ECG data between two consecutive
Electronics 2021,10, 170 11 of 29
R-peaks converted to 2-D image) from each collection and created four datasets, which we
have used in our experiments. These thirty records are organized in collections as follows.
The first collection has fifteen records and consists of ECG signals recorded with Lead 3.
The second collection has six records containing ECG signals recorded with Lead 5. The
third collection has six records containing ECG signals recorded with Lead 3. The fourth
collection has nine records containing ECG signals recorded with Lead 3.
Three datasets are created for intra-patient analysis [
67
] using collection 1, 2, and 3
named Dataset1 (DS1), Dataset2 (DS2), and Dataset3.1 (DS3.1). Additionally, Dataset3.2
(DS3.2) is created for inter-patient analysis [
68
,
69
], as follows. In the intra-patient division
scheme, data extracted from one patient may appear in both training and test sets of the
machine/deep learning model. Whereas, in the inter-patient division scheme, data derived
from one patient appears in only one set; either training or test set of the machine/deep
learning model. DS1 consists of a total of 600 images, 300 images per class taken from the
first collection. DS2 contains a total of 600 images, 300 images per class taken from the
second collection. DS3.1 has a total of 900 images, 300 images per class taken from the third
collection, and DS3.2 also has a total of 900 images, 300 images per class taken from fourth
collection. However, as DS3.2 is created for inter-patient analysis [
67
72
], it is further
grouped into two datasets, DS3.2 TrainingSet containing 200 images from six records for
training the classifier and DS3.2 TestingSet contains 100 images from three records for
validation of the classifier. These images are the ECG waves within two consecutive R-
peaks processed at Layer 2 of our proposed three-layer ECG signal analysis process shown
in Figure 3. Table 1shows these database collections and dataset selection. The record
numbers and the number of images are also presented.
Table 1. Database collection and dataset selection.
Database Collection Filename Class # of Images Per Class Datasets
Collection 1
E0106 Normal, Abnormal 70
DS1 300 images
E0107 Normal, Abnormal 38
E0111 Normal, Abnormal 61
E0113 Normal, Abnormal 244
E0115 Normal, Abnormal 53
E0129 Normal, Abnormal 161
E0133 Normal, Abnormal 119
E0154 Normal, Abnormal 55
E0155 Normal, Abnormal 323
E0205 Normal, Abnormal 277
E0405 Normal, Abnormal 126
E0417 Normal, Abnormal 34
E0501 Normal, Abnormal 135
E0606 Normal, Abnormal 172
E1304 Normal, Abnormal 150
Collection 2
E0104 Normal, Abnormal 23
DS2 300 images
E0105 Normal, Abnormal 77
E0118 Normal, Abnormal 192
E0119 Normal, Abnormal 362
E0602 Normal, Abnormal 480
E0605 Normal, Abnormal 1277
Collection 3
E0105 Normal, Abnormal 77
DS3.1 300 images
E0108 Normal, Abnormal 117
E0113 Normal, Abnormal 245
E0114 Normal, Abnormal 299
E0147 Normal, Abnormal 73
E0162 Normal, Abnormal 143
Collection 4
E0105 Normal, Abnormal
DS3.2 TrainingSet 200 images
DS3.2 300 images
E0108 Normal, Abnormal
E0113 Normal, Abnormal
E0114 Normal, Abnormal
E0411 Normal, Abnormal
E0413 Normal, Abnormal
E0147 Normal, Abnormal
DS3.2 TestingSet 100 imagesE0162 Normal, Abnormal
E0306 Normal, Abnormal
5.4. CNN Architecture
A CNN-based model typically consists of an input layer, CNN kernel layers also
known as filters or feature maps, pooling layers, a fully connected layer and an output
layer. The size of each layer depends on the problem and its optimization defines the
Electronics 2021,10, 170 12 of 29
efficiency of the model [
73
]. We present a 2-D CNN based classifier model that performs
the automated feature engineering and learns the fiducial points with global averaging
presented at the feature map level of the CNN. The proposed method, integrates the
feature engineering and classification capabilities as compared to traditional approaches
that requires pre-extracted features. This minimizes the complexity, time, and overhead of
the diagnosis process. The CNN model consists of 7 layers as shown in Figure 6and uses
the Adaptive Moment (ADAM) method for backpropagation [
74
]. ADAM is a stochastic
optimizer and updates weights based on value and gradient only. It calculates the gradients
for weights optimization during the 2-D CNN training. ADAM works great with CNN
and its other variations as it combines the advantages of Adaptive Gradient (AdaGrad)
and Root Mean Square Propagation (RMSProp).
Figure 6. Proposed 2-D Convolutional Neural Network (CNN) architecture.
The proposed CNN architecture takes image as the input with the optimized size of
28 ×28 pixels
. This size has been found after several trials and iterations. The size of the
2-D image need not be zero-padded to the duration of the longest R-R interval. Therefore,
the ECG data between two consecutive R-peaks of a long R-R interval (e.g., corresponding
to bradycardia), is transformed to an image with the fixed size of 28
×
28 pixels, similarly as
in the case of an ECG with shorter R-R intervals. However, the ECG morphologies would
appear more condensed or spread out. Thus, after the training process, the system will
learn to differentiate such ECG images from normal ones or other classes. It is important
to note that larger image sizes require more number of neurons and makes the system
computationally ineffective. This in turn puts more computational burden during the
training process. The “augmentedImageDatastore” function of Matlab is used to covert the
input image into 28 ×28 pixels.
Convolution with the optimized kernel size of 5
×
5 is applied to the image, and
generates four feature maps. These feature maps get rectified by the Rectified Linear Unit
(ReLU) activation function expressed in Equation (2). ReLU rectifies the gradient vanishing
problem and is less complex as compared to TanH and Sigmoid functions [
75
]. The rectified
feature maps go through the pooling layer. We have used global average pooling that
is known for object localization. This layer generates the average value per output class
which becomes a regression problem for the next layer of the fully connected network. The
fully connected layer’s output is normalized to a probability distribution by the Softmax
activation function (calculated by Equation (3)). Softmax generates predicted output values
between 0 and 1 for each class.
During the training process, the amount that the weights are updated is referred
to as the step size or the learning rate. The learning rate is, specifically, a configurable
Electronics 2021,10, 170 13 of 29
hyper-parameter used in the training of neural networks. The Loss Function or error is
another important component of neural networks and points to the prediction error of a
neural network. The method to calculate the loss is called Loss Function. It is also referred
to as the cost function as a measure of error between the value the model predicts and the
actual value. In a neural network, the cost (loss) function is generally minimized and the
gradient of the loss with respect to the weight parameters are updated in several iterations
to converge to a final validation loss (error). In in this work, the predicted and labeled
values are used to calculate the error using the cross-entropy (loss) function expressed
by Equation (4). The backpropagation process then optimizes the weights to minimize
the error by calculating the gradients and updating the weights with the ADAM resolver
algorithm , as shown in Algorithm 1.
Algorithm 1: Adaptive Moment ADAM
M0=0, R0=0(Initialization)
For t =1, . . . , T:
Mt=β1Mt1+ (1β1)Lt(Wt1) (1st moment estimate)
Rt=β2Rt1+ (1β2)Lt(Wt1)2(2nd moment estimate)
ˆ
Mt=Mt/1(β1)t(1st moment bias correction)
ˆ
Rt=Rt/1(β2)t(2nd moment bias correction)
Wt=Wt1αˆ
Mt
ˆ
Rt+e(Update)
Return Wt
Hy per-parameters :
α>0 — learning rate
β1[0, 1]— 1st moment decay rate
β2[0, 1]— 2nd moment decay rate
e>0 — numerical term
Lloss/error calculated f rom cross entro py (Equation (4))
RRMSProp output
WWeights
The details of the proposed architecture including CNN and its parameters, are
summarized in Table 2. By designing a CNN with the structure of Figure 6and the listed
parameters in Table 2, the proposed 2-D CNN ECG classification idea of this paper can be
reconstructed and the results presented in Section 6can be reproduced.
ReLU(x) = (x,x>0
0, x0(2)
Softmax(xi) = exp(xi)
jexp(xj)(3)
Loss =
N
i=1
Pilog(xi), (4)
where
P
is the probability of each class,
N
is the total number of classes, and
x
is the
calculated output after forward pass.
The details of the ADAM algorithm and how the weights are updated during the
training process of a CNN architecture, is expressed below.
Electronics 2021,10, 170 14 of 29
Table 2. CNN Structure and Parameter Details.
Name Layer Type Size Learnables
Imageinput Image input 28 ×28 -
1-4 Layer1_Conv Convolution 5 ×5Weights 5 ×5×3×4,
Bias 1
×
1
×
4, Total (304)
1-4 Layer1_Relu Activation Function - -
1-4 Layer1_Pooling Global Average 1 ×1 -
1-4 FullyConnected Neural Network 1 ×1×3Weights 3 ×4, Bias 3 ×1,
Total (15)
1-4 Softmax Activation Function - -
1-4 Output Layer Classification Output - -
6. Results
We performed three experiments based on simulations with variants of the CNN
architecture based on our proposed 2-D CNN classifier. In this section, we present the
results of each experiment, including the classified output for each class, accuracy, and
loss graph. The performance metrics are discussed in Section 7. Each of these experiments
uses a Matlab augmented function to convert the ECG data between every two consecutive
R-peaks into a 28
×
28 pixel, gray-scaled image as an input to the 2-D CNN from its
appropriate dataset. Datasets DS1, DS2, and DS3.1 are based on the intra-patient division
scheme and are further split into 70/30 training and testing sets with labels for training
and validation of the CNN algorithm. The “splitEachLabel” function in Matlab is used
that takes a ratio (0.7 in our case) as an argument and creates two separate image datasets,
one for training and the other for testing for DS1, DS2 and DS3.1. Dataset DS3.2 is based
on the inter-patient division scheme and is split into training set (DS3.2 TrainingSet) and
testing set (DS3.2 TestingSet) with a ratio close to 70/30 (approximately 66.6/33.3). Records
used to construct the DS3.2 TrainingSet are different from the records used for the DS3.2
TestingSet (as shown in Table 1), making both sets an independent collection of records.
The training options and 2-D CNN parameters are shown in Table 2.
6.1. First Experiment
The first experiment classifies the ECG signal into two classes: Normal and Abnormal.
This experiment uses dataset DS1 containing a total of 600 images from our database
collection 1 as shown in Table 1. The 2-D CNN architecture/network shown in Figure 6is
trained on the training set derived from DS1. Validation is performed using the testing set,
and Figure 7shows the output of the forward pass of an ECG image classified as Normal
or Abnormal after the validation is completed. Figure 8shows the accuracy graph, and
Figure 9shows the loss function for the training progress.
Electronics 2021,10, 170 15 of 29
Figure 7. Experiment 1: Classification of Normal (a) and Abnormal (b) beats using 2-D CNN.
Figure 8. Experiment 1: Training and validation accuracy progress for two classes: Normal and Abnormal using DS1.
Figure 9. Experiment 1: Validation error convergence progress for two classes: Normal and Abnormal using DS1.
Electronics 2021,10, 170 16 of 29
6.2. Second Experiment
The second variant of the CNN architecture performs the second experiment to classify
the ECG signal images into two classes of Normal and Abnormal using dataset DS2 from
our database collection 2, shown in Table 1. DS2 has a total of 600 images recorded with
Lead 5 and is further split into training and testing sets and to train and validate the
proposed network. Validation yields classification results shown in Figure 10.
Figure 10. Experiment 2: Training and validation accuracy progress for two classes: Normal and Abnormal using DS2.
6.3. Third Experiment
The third experiment is performed for both intra-patient and inter-patient classification
of the ECG signal into three classes: Normal (N), Ischemic beat (ST-change), and V-change
using datasets DS3.1 and DS3.2 from our database collection 3 and 4 respectively, shown
in Table 1. DS3.1 contains a total of 900 images, and our proposed 2-D CNN is trained
on the training set from DS3.1. Figure 11 shows the classification results of three images
classified as Normal, ST-change, and V-change based on the intra-patient division scheme.
Figures 12 and 13
show the accuracy and loss progress graphs, respectively. We have
repeated the third experiment and used DS3.2 to classify ECG signal into the three classes
for the inter-patient scheme. Figure 14 shows the classification results of three images
classified as Normal, ST-change, and V-change based on inter-patient scheme. The accuracy
and loss progress graphs for the inter-patient scheme using DS3.2 are shown in Figure 15.
Electronics 2021,10, 170 17 of 29
Figure 11.
Experiment 3 Intra-patient scheme using DS3.1: Classification of Normal (
a
), ST-Change (
b
), and V-change (
c
)
beats using 2-D CNN.
Figure 12.
Experiment 3 Intra-patient scheme using DS3.1: Training and validation accuracy progress for three classes:
Normal, ST-change, and V-change.
Electronics 2021,10, 170 18 of 29
Figure 13.
Experiment 3 Intra-patient scheme using DS3.1: Validation error convergence progress for three classes: Normal,
ST-change, and V-change.
Figure 14.
Experiment 3 Inter-patient scheme using DS3.2: Classification of Normal (
a
), ST-Change (
b
), and V-change (
c
)
beats using 2-D CNN.
Electronics 2021,10, 170 19 of 29
Figure 15.
Experiment 3 Inter-patient scheme using DS3.2: Training and validation accuracy and error progress for three
classes: Normal, ST-change, and V-change.
6.4. Fourth Experiment
We have also performed a fourth experiment with hardware implementation, illus-
trated in Figure 16. This experiment follows the process of our proposed three-layer ECG
signal analysis model presented in Figure 3of Section 1.3. At Layer 1, ECG data is acquired
with the AD8232 (Analog Devices, Inc., Norwood, MA, USA) ECG measurement board,
that is directly attached to an Arduino mega 2560 with wires. AD8232 is an analog single
lead, low-power integrated frond-end heart monitor that is used for a variety of vital signs
monitoring applications. It is a 3-pin lightweight portable sensor from Analog Devices
that operates on 3.3V DC voltage and gives an analog output. Other sensors such as Zio
Patch [
76
] and Shimmer [
77
], a Bluetooth (BT)-based wireless sensor, can also be used to
acquire ECG. At Layer 2, the coordinator Arduino mega microcontroller device receives
the data for preprocessing. The ECG signal is sent to the smartphone for graphical rep-
resentation using the IEEE802.15.1 Bluetooth protocol and displayed with a smartphone
app designed in the open source visual studio code IDE. The smart app is programmed
in Javascript using the “react” library and “react-native” framework. However, wireless
IEEE802.11x and Zigbee IEEE802.15.4 [
78
] protocols can also be used to send the data
from the controller to the smartphone app. The coordinator then sends the preprocessed
data to Layer 3 at a central location in the form of images using the IEEE802.11x based
wireless connection for classification using our trained 2-D CNN algorithm running on
Amazon Web Services (AWS) cloud. These images occupy bandwidth when traveling over
networks such as wireless or Global System for Mobile Communication (GSM). Bandwidth
requirements can cause latency and become a hurdle in a successful data transfer process.
Data compression can overcome this problem by compressing the data to reduce the overall
packet size during the transfer. The data is not compressed between the coordinator and
Layer 2 in this experiment for the purpose of simplicity. However, lossless compression
techniques such as Quad Level Vector (QLV) [
79
,
80
] and Huffman coding are available to
address the bandwidth requirements. The purpose of the fourth experiment is to show that
our proposed architecture can be adopted for real-time monitoring systems using portable
and wearable devices.
Electronics 2021,10, 170 20 of 29
Figure 16. Real-time ECG signal analysis and classification.
7. Discussion
We performed multiple experiments (Exp), and in the first three simulation experi-
ments, preprocessing was repeated to create augmented images in gray-scale with the size
of 28
×
28 pixels. Images were shuffled every epoch during the training for each simulation
experiment to achieve better training. Table 3shows the optimized parameters used during
the training process for the simulation experiments. The Validation Frequency is calculated
from Equation (5).
Validation Frequency =N umber o f Trainin g im ages
MiniBatch Size . (5)
Observing the results of Figure 12 yields that our method of 2-D CNN has achieved
the best accuracy of 99.26% in detecting three classes of Normal, ST-change, and V-change
with the intra-patient scheme. Our method in the second experiment has also improved
the classification accuracy shown in Figure 10 when identifying two classes of Normal
and Abnormal, as compared to other methods which attempted to classify ECG signal
into Normal and Abnormal classes. It can be concluded that our proposed method of 2-D
CNN has outperformed other machine learning and traditional methods when tested with
ESCDB. Following the notion that intra-patient division may result in a biased system [
70
],
inter-patient division is thus, recommended for cases where the classification module
will be applied on new patient data [
67
72
]. Figure 14 shows the results of a preliminary
experiment performed for the inter-patient scheme, and is yet to be explored as future
work for adaptation in real-time monitoring systems.
We achieved better results with less number of layers in the CNN network structure;
thereby yielding much less complexity. The complexity of a CNN can be calculated in the
form of Big O notations [81], expressed by Equation (6).
O(
d
l=1
nl1×s2
l×nl×m2
l), (6)
Electronics 2021,10, 170 21 of 29
where
d
is the number of convolutional layers,
nl
denotes the filter’s width in a given
l
th
layer and sland mlare the sizes of the filter and output feature map, respectively.
Table 3. Training Parameter Details.
Option Exp1: Value Exp2: Value Exp3: Value
MiniBatch Size 60 40 40
1-4 No. of Epochs 250 250 250
1-4 Iterations per Epoch 7 10 15
1-4 Maximum Iteration 1750 2500 3750
1-4 Validation Frequency 10 10 15
1-4 Initial Learning Rate (LR) (α) 0.1 0.1 0.1
1-4LR Schedule piecewise piecewise piecewise
1-4 LR Drop Factor 0.07 0.06 0.08
1-4 Gradient Threshold Method l2 norm l2 norm l2 norm
1-4 GradientDecayFactor (β) 0.9 0.9 0.9
1-4 Shuffle Every Epoch Every Epoch Every Epoch
1-4 Execution Environment Two 2080Ti GPUs Two 2080Ti GPUs Two 2080Ti GPUs
Table 4shows the reported performance metrics including F1-score (f1), success-rate
and positive predictive value (ppv) of the related work in comparison with our study.
We have calculated the accuracy (acc), sensitivity (sen), and specificity (spe) performance
metrics based on the True Positives (TP), True Negatives (TN), False Positives (FP) and
False Negative (FN) values, using Equations (7)–(9), respectively, to evaluate our method.
These metrics are summarized in Table 5and are additionally shown in the confusion
matrices of Figure 17 for our simulation experiments.
Accuracy =TP +TN
TP +T N +F P +FN ×100 (7)
S peci f ici ty =TN
TN +FP ×100 (8)
Sensitivity =TP
TP +F N ×100. (9)
Table 4. Comparison of traditional and machine learning approaches for ECG classification.
Approach Class [Ref.], Year Detection Method Performance Metrics Dataset
Traditional
Ischemia [44], 2004 GA + MDA 91%sen, 91%spe
Normal, Abnormal [38], 2015 Rule Based 90.1%acc, 98.9%sen
Normal, Ischemic [40], 2016 Threshold based 98.12%sen, 98.16%spe
ESCDB
ST-Segment changes [41], 2016 Pan-Tompkins 97.03%success-rate, 0.0297err
Normal, Abnormal [42], 2017 Iso-electric Level 98.2%sen, 97.17%ppv
Normal, Abnormal [45], 2018 Statistical Features 97.71%sen, 96.89%ppv
MI [43], 2020 Time-Frequency 94.23%acc ,95.72%sen,98.15%spe
Normal, Abnormal Proposed (2-D
CNN)
98.89%acc,
[97.8%sen,100%spe ]N,
[100%sen,97.8%spe ]Abnormal
Electronics 2021,10, 170 22 of 29
Table 4. Cont.
Approach Class [Ref.], Year Detection Method Performance Metrics Dataset
Machine Learning
Ischemic [53], 2002 ANN+PCA 90%sen , 90%spe
Ischemic [44], 2004 MDA-based GA 91%sen, 91%spe
Normal, Ischemic [50], 2007 DT+Fuzzy Model 91.7%acc, 91.2%sen , 92.2%spe
QRS-Complex delineation [36],
2008 DWT 90.75%sen, 89.2%ppv
ST-Segment changes,
Multiclass [35], 2011 SVM [93.33%acc]ST,
[96.35%acc]Multiclass
Normal, Abnormal [51], 2014 ANN 98.73%acc
N, V, S, F [49], 2015 MSVM+CSVM [86%acc]MSVM,
[94%acc]CSVM
ST-Segment and T-Wave
anomalies [46], 2016 DT and RUSBoost 86%sen , 94.85%ppv, 77%acc,
0.6f1
Control, AF, VF, ST [55], 2018 CNN 97.23%acc, 97.02%sen,
97.76%ppv, 97.35%f1
Normal, ST-Changes [56], 2018 RF
86.9%acc,
85.18%sen[ST-Normal],
87.35%sen[ST-depressed],
88.06%sen[ST-elevated]
S, V [54], 2019 ANN+MMNNS 98.8%acc, 91%sen , 99.3%spe,
90%ppv
Normal, ST-Change, V-Change Proposed (2-D
CNN)
99.26%acc,
[100%sen,100%spe ]N,
[97.8%sen,100%spe ]ST,
[100%sen,97.8%spe ]V
ESCDB
Table 5. Performance results of experiments.
Experiment # of
Classes
Accuracy
(acc) Sensitivity (sen) Specificity (spe) Final Learning
Rate
Final
Validation
Loss (Error)
Exp1: Lead 3
with DS1 2 98.89% 100%Abnormal,
97.8%Normal
97.8%Abnormal,
100%Normal 1.607 0.0195
Exp2: Lead 5
with DS2 2 97.77% 100%Abnormal,
95.7%Normal
95.6%Abnormal,
100%Normal 7.708 0.15
Exp3: Lead 3
Intra-patient
scheme with DS3.1
3 99.26%
100%Normal,
97.8%ST-Change,
100%V-Change
100%Normal,
100%ST-Change,
97.8%V-Change
3.2707 0.0371
Exp3: Lead 3
Inter-patient
scheme with DS3.2
3 87.33%
79.2%Normal,
83%ST-Change,
100%V-Change
84%Normal,
78%ST-Change,
100%V-Change
4.0906 0.2647
7.1. Research Tools and Applications
There are PC-based and hardware-based tools available to test and evaluate our
model. As for PC-based tools, we used Matlab for our simulation experiments, as well as
for designing, training and evaluation of our proposed 2-D CNN model. Other PC-based
tools such as Python and Labview provide libraries that can be used in simulations. We
used Python for database analysis and dataset preprocessing. On the other hand, the “R”
tool provides a more robust view of analyzing datasets. System dynamics modeling can
also be used to evaluate the effectiveness of our model for health monitoring tools, in
Electronics 2021,10, 170 23 of 29
general, in a broader societal perspective [
82
,
83
]. As for hardware-based tools, we used
the ECG sensor AD8232, and coordinator Arduino Mega 2560 for our fourth hardware
experiment presented in Section 6.4, and shown in Figure 16. ECG sensors with clips, cup
electrodes or patches can be used with our proposed model for ECG acquisition. Other
hardware tools such as System on Module (SoM), System on Chip (SoC), emulator boards
such as AM335X and NXP Nexperia 8550 can be used in addition to other Open Source
HardWare (OSHW)-based emulators such as Arduino, ADuCM361 and Duino Olimexino,
to analyze and classify ECG with our proposed 2-D CNN classifier and three-layer process
architecture. However, the implementation of real-time ECG monitoring systems will
introduce some difficulties in the daily workflow of clinicians, especially when many
patients are referred to the clinic with suspected pathology. Cloud servers should be
restructured effectively to aid prioritizing the notifications sent to clinicians and patients.
In addition, legal issues, regulatory standards and security, privacy and confidentiality
protocols play vastly important roles here as well. Particularly, the systems should be
highly accurate in ECG acquisition, processing and analysis so that suspected pathology is
not missed to reduce the warning threshold for the owner (user) of the monitoring device
(e.g., smartwatch). The devised ECG algorithms implemented on these systems should be,
therefore, able to reduce the false negatives to as low as possible to avoid missing suspected
cardiac rhythms. Clinical trials of such devised systems can be further investigated for
their performance, but are beyond the scope of this paper.
The applications of the proposed model presented in this study are not limited to
ECG analysis but rather have a wide range of applications including the telehealth and the
electronics industry. Besides diagnosis of cardiac conditions in real-time for adults, it can
also be used to monitor fetus ECG and detect abnormalities. A method in Reference [
84
]
shows how to achieve a cleaned ECG signal of the fetus. Our proposed method is scalable
and can be implemented on microcontroller-based devices such as TI MSP430 and TMS320-
6713 that can later be adopted and used in portable simulators such as Fluke Prosim and
TriSmed TSM3000B for research purposes. Our method can further be implemented and
integrated within smart devices such as smartwatches and smartphones for real-time
monitoring and diagnosis. This method can also be implemented in electronic circuits to
monitor sinusoidal signals and detect abnormalities such as noise interference or intruder
information tapping in the signal. Electronic signatures are usually an image of a person’s
handwritten signature. These electronic signatures can be validated using our proposed
method once trained on the signatures dataset.
Figure 17. Confusion Matrices.
8. Conclusions and Future Work
ECG monitoring is vital to diagnose any abnormality in the heart. Early detection
and treatment of ischemia and MI can save lives. ST-segment changes are early signs of
a heart attack and are classified with better accuracy with our proposed method. Initial
Electronics 2021,10, 170 24 of 29
and timely diagnosis of cardiovascular diseases can drive the acceptance of a solution and
plays an essential role in a patient’s health status during an active cardiac condition. This
study showed that we had improved the diagnosis time by presenting a less complicated
system for real-time monitoring performed with both simulation-based experiments (Ex-
periments 1–3) and a hardware-based experiment (Experiment 4). We have introduced a
three-layer process to analyze and classify ECG in both simulations and in real-time as
presented in Experiment 1 for Normal and Abnormal classes. We have proposed a 2-D
image-based CNN classifier that classifies three classes including ST-changes. We have
presented detailed literature reported on ECG classification based on both traditional and
machine learning techniques and compared their performance evaluation metrics with
those that are achieved with our approach. Multiple experiments were performed to evalu-
ate our model and the best accuracy of 99.26% with an error of 0.0371 was achieved with
the intra-patient division scheme and the accuracy of 87.33% with an error of 0.2647 was
achieved with the inter-patient division scheme when evaluated on the ESCDB database.
We presented the research tools used in this study and shed light on other PC-based and
hardware-based tools available for the research community to further explore and improve
ECG classification. We presented real-life applications in the health industry, electronics
industry and others that our proposed model can be used for. The need for feature engi-
neering has been eliminated with our approach since CNN learns features automatically
during the training process. Our proposed method has much less complexity as compared
to others in this area of research, making our model feasible to be implemented in real-time
monitoring systems.
As a future direction, we plan to evaluate our model with multiple databases using the
inter-patient division scheme and classify more arrhythmia types such as fusion and other
unknown beats. In addition, we plan to collect new images of ECG and create a dataset
of our own to reflect real-life scenarios, and further, evaluate our method on a real-time
system presented in this study. Moreover, we plan to convert the trained network from
Matlab to C-code to implement in microcontroller-based systems and to test it for a portable
and wearable device to perform real-time monitoring and classification. Furthermore, we
plan to develop an application, integrated with a microcontroller-based system, to monitor
the health of a person’s heart.
Author Contributions:
Supervision, K.E., A.A., M.F. and O.A.; Writing—original draft preparation,
M.W.; Writing—review and editing, K.E. and M.F.; Conceptualization, M.W., K.E. and A.A.; Method-
ology, M.W., M.F. and O.A.; Software, M.W. and O.A; Validation, K.E., A.A, M.F. and O.A.; Formal
Analysis, M.W., M.F. and O.A.; Investigation, K.E., A.A., M.F. and O.A.; Resources, K.E., A.A., M.F.
and O.A.; Data Curation, M.W.; Visualization, M.W., K.E. and M.F.; and Project Administration K.E.,
A.A., M.F. and O.A. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded in part by the UB Partners CT Next Innovation Grant 2019–2020.
Also, the authors acknowledge funds received from the University of Bridgeport to buy equipment
to support this research.
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can
be found here: https://physionet.org/content/edb.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
Electronics 2021,10, 170 25 of 29
Abbreviation Full form
1-D One Dimensional
2-D Two Dimensional
AAMI Association for the Advancement of Medical Instrumentation
acc Accuracy
AdaBoost Adaptive Boosting
AdaGrad Adaptive Gradient
ADAM Adaptive Moment
AF Atrial Fibrillation
ANF Adaptive Notch Filter
ANN Artificial Neural Network
AWS Amazon Web Services
CHD Coronary Heart Disease
CNN Convolutional Neural Network
CSVM Complex Support Vector Machine
CVD Cardiovascular Disease
DenseNet Densely connected CNN
DS1 Dataset1
DS2 Dataset2
DS3.1 & DS3.2 Dataset3
DT Decision Trees
DWT Discrete Wavelet Transform
ECG Electrocardiogram
EMD Empirical Mode Decomposition
ESCDB European ST-T Database
f1 F1-score
FE Feature Engineering
FFT Fast Fourier Transform
FIR Finite Impulse Response
FN False Negative
FP False Positive
GA Genetic Algorithm
GSM Global System for Mobile Communication
IEF Isoelectric Energy Function
LSTM Long-Short Term Memory
MDA Multicriteria Decision Analysis
MI Myocardial Infarction
MITDB MIT-BIH Arrhythmia Database
MMNNS Multi-Module Neural Network System
MSVM Multiclass Support Vector Machine
OSHW Open Source HardWare
PDA Personal Digital Assistant
ppv Positive Predictive Value
QLV Quad Level Vector
RBDT Rule Based Decision Tree
ReLU Rectified Linear Unit
RF Random Forest
RMSProp Root Mean Square Propagation
RUS Random Under Sampling
sen Sensitivity
spe Specificity
SSRLS State Space Recursive Least Square
SVM Support Vector Machine
TD Time Domain
TN True Negative
TP True Positive
WFDB WaveForm-DataBase
WT Wavelet Transform
Electronics 2021,10, 170 26 of 29
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... The disadvantage of this work is that considerable computational capacity is required if ECG segments ranging from hours to days are used, as the signals are first converted to a representation that the CNNs can understand, and then the CNN itself requires more computational resources. Other examples include [11][12][13]. Rajpurkar, P. et al. decomposed the signal into its frequency components to create images and then fed them to CNN. Other works focus on feature selection and feature extraction. ...
... MI is highly related to ST-elevation; therefore, in future work, we plan to perform the modeling of such a disorder and compare more objectively this work. In [13], the authors used the same database and also had an excellent result with an accuracy of 99.23%; being more exact, they obtained 97.8% sensitivity, 100% specificity for the detection of ST-segment changes, against 99.11% of us 94.3% respectively. However, we are considering three classes and not only the division between normal and abnormal. ...
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... As shown in Figure 2, the recursive wavelet fuzzy neural network is structurally divided into five layers, with three implicit layers, namely the affiliation function layer, the rule layer, and the recursive wavelet function layer [15]. e neural network controller combines fuzzy logic, wavelet processing, and recursive structure to improve its processing capability and accuracy and solve the shortcomings of static mapping. ...
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It has been widely used in signal processing, image processing, speech recognition and synthesis, pattern recognition, machine vision, machinery fault diagnosis and monitoring, and other scientific and technological fields and has achieved great results. The application potential in nonlinear system identification is increasing. According to the theory of "overload recovery" and "functional reserve", the mathematical model of "load-fitness state" is established to understand the adaptation characteristics and individual characteristics of athletes to sports training. The model is used to simulate the values and time required to reach the maximum fitness state for four types of precompetition reduction plans and to provide a reference for the development of precompetition training plans. The data required for parameter estimation were the actual training data of six outstanding basketball athletes (mean age 18.2 ± 0.75, mean training years 4.6 ± 0.49). And the coaches' training plan was not intervened during the test. In order to further reduce the biaxial synchronization error of the sports platform and improve the stability of the system, the wavelet transformation capable of time-varying signal analysis and the recursive structure with dynamic capability were combined with the fuzzy neural network, and the learning ability of the neural network was used to learn and adjust the scaling and translation factors in the wavelet function, the mean and standard deviation in the fuzzy structure, and the connection weights between the layers, according to the biaxial synchronization. The simulation results show that the designed global sliding mode controller can improve the convergence speed of tracking error and ensure the single-axis tracking accuracy of the H-type motion platform compared with the traditional sliding mode controller, and the tracking accuracy and synchronization accuracy of the system can be further improved after adding the cross-coupled synchronization controller, but the improvement of synchronization control accuracy is not very satisfactory due to the fixed selection of the parameters of the cross-coupled controller. Further improvement is needed.
... Exemplary performance of deep neural networks (DNNs) on ECG [16] and especially the performance of CNN using ID convolution [17] and 2D convolution [18] has recently attracted attention of many researchers. Deep learning models are capable of automatically learning invariant and hierarchical features directly from the data and employ end-to-end learning mechanism that takes data as input and class prediction as output. ...
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... Exemplary performance of deep neural networks (DNNs) on ECG [16] and especially the performance of CNN using ID convolution [17] and 2D convolution [18] has recently attracted attention of many researchers. Deep learning models are capable of automatically learning invariant and hierarchical features directly from the data and employ end-to-end learning mechanism that takes data as input and class prediction as output. ...
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Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the stateof-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN). In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information necessary for better performance of classifier. These informational features are finally used to train a Support Vector Machine (SVM) classifier for ECG heart-beat classification. We demonstrate the superiority of the proposed fusion models by performing experiments on PhysioNets MIT-BIH dataset for five distinct conditions of arrhythmias which are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for Myocardial Infarction (MI) classification. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively.
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Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this article. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.
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This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.
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Objective The AliveCor KardiaBand (KB) is an Food and Drug Administration-approved smartwatch-based cardiac rhythm monitor that records a lead-Intelligent ECG (iECG). Despite the appeal of wearable integrated ECG devices, there is a paucity of data evaluating their accuracy in diagnosing atrial fibrillation (AF). We evaluated whether a smartwatch-based device for AF detection is an accurate tool for diagnosing AF when compared with 12-lead ECG. Methods A prospective, multi-centre, validation study was conducted in an inpatient hospital setting. The KB paired with a smartwatch, generated an automated diagnosis of AF or sinus rhythm (SR). This was compared with a 12-lead ECG performed immediately after iECG tracing. Where an unclassified or no-analysis tracing was generated, repeat iECG was performed. Results 439 ECGs (iECGs (n=239) and 12-lead ECG (n=200)) were recorded in 200 patients (AF: n=38; SR: n=162) from three tertiary centres. Sensitivity and specificity using KB was 94.4% and 81.9% respectively, with a positive predictive value of 54.8% and negative predictive value of 98.4%. Agreement between 12-lead ECG and KB diagnosis was moderate when unclassified tracings were included (κ=0.60, 95% CI 0.47 to 0.72). Combining the automated device diagnosis with blinded electrophysiologists (EP) interpretation of unclassified tracings improved overall agreement (EP1: κ=0.76, 95% CI 0.65 to 0.87; EP2: κ=0.74, 95% CI 0.63 to 0.86). Conclusion The KB demonstrated moderate diagnostic accuracy when compared with a 12-lead ECG. Combining the automated device diagnosis with EP interpretation of unclassified tracings yielded improved accuracy. However, even with future improvements in automated algorithms, physician involvement will likely remain an essential component when exploring the utility of these devices for arrhythmia screening. Clinical trial registration URL: https://www.anzctr.org.au/ Unique identifier: ACTRN12616001374459.
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