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Artículo Científico / Scientific Paper |
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https://doi.org/10.17163/ings.n29.2023.07 |
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pISSN: 1390-650X / eISSN: 1390-860X |
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PREDICTION OF ARRHYTHMIAS AND ACUTE MYOCARDIAL
INFARCTIONS USING MACHINE LEARNING |
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PREDICCIÓN DE ARRITMIAS E INFARTOS AGUDOS DE MIOCARDIO USANDO APRENDIZAJE AUTOMÁTICA |
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Darwin Patiño1,*
Alfonso Guijarro1 |
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Received: 16-11-2022, Received after review:
06-12-2022, Accepted: 12-12-2022, Published: 01-01-2023 |
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Abstract |
Resumen |
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Cardiovascular
diseases such as Acute Myocardial Infarction is one of the 3 leading causes
of death in the world according to WHO data, in the same way cardiac
arrhythmias are very common diseases today, such as atrial fibrillation. The
ECG electrocardiogram is the means of cardiac diagnosis that is used in a standardized way throughout the world.
Machine learning models are very helpful in classification and prediction
problems. Applied to the field of health, ANN, and CNN artificial and neural
networks, added to tree-based models such as XGBoost,
are of vital help in the prevention and control of heart disease. The present
study aims to compare and evaluate learning based on ANN, CNN and XGBoost algorithms by using the Physionet
MIT-BIH and PTB ECG databases, which provide ECGs classified with Arrhythmias
and Acute Myocardial Infarctions respectively. The learning times and the
percentage of Accuracy of the 3 algorithms in the 2 databases are compared separately, and finally the data are crossed
to compare the validity and safety of the learning prediction. |
Las enfermedades cardiovasculares como el Infarto Agudo de Miocardio es una de las 3 principales causas de muerte en el mundo según datos de la OMS, de igual forma las arritmias cardíacas son enfermedades muy comunes en la actualidad, como la fibrilación auricular. El electrocardiograma ECG es el medio de diagnóstico cardíaco que se utiliza de forma estandarizada en todo el mundo. Los modelos de aprendizaje automático son muy útiles en problemas de clasificación y predicción. Aplicadas al campo de la salud, las redes artificiales y neuronales ANN y CNN, sumadas a modelos basados en árboles como XGBoost, son de vital ayuda en la prevención y control de enfermedades del corazón. El presente estudio tiene como objetivo comparar y evaluar el aprendizaje basado en los algoritmos ANN, CNN y XGBoost mediante el uso de las bases de datos de ECG Physionet MIT-BIH y PTB, que proporcionan ECG clasificados con arritmias e infartos agudos de miocardio, respectivamente. Se comparan por separado los tiempos de aprendizaje y el porcentaje de Exactitud de los 3 algoritmos en las 2 bases de datos, y finalmente se cruzan los datos para comparar la validez y seguridad de la predicción del aprendizaje. |
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Keywords: arrhythmias, acute myocardial, infarction,
machine learning, artificial neural network, convolutional neural network,
extreme gradient boosting |
Palabras clave: arritmias, infarto agudo miocardio, aprendizaje automático, red neuronal artificial, red neuronal convolucional, impulso de gradiente extremo |
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1,*Universidad de Guayaquil, Ecuador. Corresponding
author ✉: darwin.patinop@ug.edu.ec. 2University of Villanova, Pensilvania, Estados Unidos. Suggested citation: Patiño, D.; Medina, J.; Silva, R.; Guijarro, A. and Rodríguez, J. “Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning,” Ingenius, Revista de Ciencia y Tecnología, N.◦ 29, pp. 79-89, 2023, DOI: https://doi.org/10.17163/ings.n29.2023.07. |
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1. Introduction A multiplicity of
devices (personal computers, smartphones, tablets, cell phones, etc.) are used today to accumulate and process bigdata about human behavior. This bigdata
is available for a multiplicity of purpose including medicine [1,2]. Mobile Health (mHealth) and
smart devices enable early detection and prompt intervention for patients
with Atrial Fibrillation (AF). Single- and multi-lead ECG, photoplethysmography (PPG), and oscillometric,
with validated diagnostic capability, can be integrated
within the clinical practice to detect AF. Existing clinical practice
guidelines suggest that pulse assessment with ECG screening for the high-risk
population, for patients >65 years of age, is appropriate to reduce
complications. However, easy-to-use, and affordable consumer health and smart
devices may be a good alternative screening tool not only for the elderly
population with comorbidities, but also for the general low-risk population
with frequent monitoring [2–7]. mHealth devices that are capable of
monitoring heart rate and/or heart rhythm come in multiple forms such as,
smartphone apps, smart watches, rings, necklaces, wearable sensors, and
patches [8–10]. Companies have created products capable of producing a point-of-care ECG registries, such as the AliveCor Kardia Monitor series
of devices [8]. In a large cohort study conducted in Hong Kong, research on
the Kardia singlelead ECG
device found that a review cardiologist confirmed that 65% of AFs detected by
the device were accurate. In this study of more than 10000 patients with a
mean age of 78 years, the number needed to make an accurate new diagnosis of
AF was 145 participants [8].36 The sensitivity and specificity of the Kardia monitor were found to be 99, 6% and 97.8%,
respectively [11]. The
term Ubiquitous Health (u-Health) defined by Weiser as the integration of
computing into human actions and behaviors at “anytime” and “anywhere” has
been gaining prominence [1,2]. The main attribute of u-Health is the capacity
for interaction between individuals and devices in such a way that the
technology is transparent to the user [12]. It is not clear what is the best algorithm to detect cardiovascular diseases by
means of u-Health devices. The technology needs to be robust, reliable
and with low computational cost so that it can be run directly in the devices
even when offline. The goal of this paper is to evaluate the best alternative
for arrhythmia detection with u-Health Devices. This
work is the product of an ongoing collaboration between the University of Guayaquil
and the University of Villanova where multiple artificial intelligence
strategies are being developed for real time
detection of arrhythmias. The current work uses existing arrhythmia databases
to validate the strategie future
work intends to process real time data |
from wearable
devices. Results from this research are highly encouraging and will be further discussed throughout the article. 2. Materials and Methoda 2.1.Methodology Two Physionet databases of ECG MIT electrocardiograms (arrhythmias)
with 109444 records (normal 21891 and abnormal 87553) and PTDB (infarcts)
with 14550 records (normal 4045 and abnormal 10505) were used. MIT database
has 4 categories Normal “N” 0, Supraventricular “S” 1, Ventricular “V” 2,
Ventricular Fibrillation “F” 3, Other unclassified “Q” 4. PTDB database has 2
categories “N” 0 and with cardiac problems “A” 1. The SMOTE was used to regularize the categories and avoid
Overfitting and Underfitting. The
80% of the records are used for training and 20% for
tests, 20% of the 80% of training is taken again to evaluate the data
prediction. This process was carried out separately
for both the MIT and PTDB databases, using in both cases ANN artificial
neural networks and CNN convolutional neural networks in addition to the
decision tree-based algorithm called XGBoost or
extreme gradient boosting, the 3 algorithms were evaluated with the 2
databases. 2.2.
Cardiovascular Diseases and Artificial Intelligence Cardiovascular
diseases (CVD) are the leading cause of mortality worldwide, accounting for
31% of all deaths [13]. One of the main causes is acute myocardial infarction
(AMI). There is an emerging need to study a wide range of cutting-edge
techniques for its analysis and diagnosis of heart diseases. To judge the
specific situation of the patient, doctors often look at the ECG
(electrocardiograph) signal to get enough information to help them diagnose.
Many researchers have applied Machine Learning algorithms to study the
arrhythmia [14] classification problem. Advances in data processing, storage
capacity and Machine Learning ML methods have been transforming the field of
medicine, including cardiology [15]. AF
is one of the most common types of arrhythmias which
is characterized by a rapid and irregular heartbeat [13]. Ischemic heart
disease (IHD) is a condition in which there is an inadequate supply of blood
and oxygen to a part of the heart muscle [16]. This condition usually occurs
when there is an imbalance between oxygen supply and demand to the heart
muscle (myocardium), usually due to atherosclerotic heart disease [17].
Patients usually do not show typical signs and symptoms (asymptomatic) until
ischemic heart |
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disease manifests as angina, myocardial
infarction, or sudden cardiac death [18]. Heartbeat
classification, in ECG analysis, is the most common way to automate
arrhythmia [19] diagnoses. The common machine learning-based ECG learning
flow includes signal noise analysis, heartbeat recognition, feature
extraction, and heartbeat classification. For deep learning, feature
extraction can be replaced by storing fragments of
beats from a complete ECG sequence [20]. Existing ECG signal identification
algorithms in the literature incorporate three important points: preprocessing,
classification, and imbalance of the data set. To analyze characteristics
that can be directly related to physiological factors on the development of
diseases, three Deep Learning algorithms were considered:
CNN convolutional neural network, ANN artificial neural network, and the
reinforced tree eXtreme Gradient Boost o XGBoost. Artificial Neural
Networks are learning algorithms that can identify complex relationships in
data. ANNs are designed to mimic human nervous
system. Typical ANN’s are comprised of 3 layers: input, output, and hidden
layers. Each layer is made up of neurons [21].
Convolutional neural networks (CNN), recurrent neural networks (RNN), and
naïve Bayes (NB) are used as classifiers. There are
techniques that use different combinations such as Discrete Wavelet Transform
(DWT) and ANN combination were obtained for ECG
arrhythmia classification [22]. When the number of features is greater than
the number of samples, ANNs can handle multiple classes, there is no effect
of large data sets on ANNs, and extensive memory is not required [23]. ANN-based study
classifies IHD using heart rate variability (HRV) parameters along with a
clinical data such as left ventricular ejection fraction (LVEF), age, and
gender. A series of networks with different number of input nodes (varying
between 7 and 15), hidden nodes (between 2 and 10) and two output nodes were
tested. The training and test ranges were respectively 75% and 25% of the
total amount of data [24]. Various investigators have also used artificial
neutral network (ANN)-based approaches for diagnostic classification of ECG
signals [16]. Modern Deep Neural
Network DNN techniques are used to solve the problem
of manual feature selection and extraction in conventional automatic systems
for MI Image diagnostics [25]. The Neural Network backpropagation algorithms are used to train deep learning [26]. CNN is most applied to analyze visual images. Myocardial
infarction is predicted using the characteristic
images before and after the attack obtained as input image of a CNN [27].
Commonly used layers in CNN are convolution (Conv),
rectified linear unit (ReLU), pooling, batch
normalization, and fully connected layer [28]. An input matrix is fed into a
detection model that is composed of CNNs and a bidirectional Long short-term
memory network (bi-LSTM) |
with 5-fold cross-stratified
validation [29]. DNN
have shown success in several domains, including images, audio, and text
[30]. In real-world applications, the most common data type is tabular data,
which comprises sample (rows) with the same set of features (columns).
Tabular data is used in many fields, including medicine, finance,
manufacturing, climate science, and many others [31]. Traditional
machine learning methods, such as gradient-powered decision trees (GBDT)
[32], dominate tabular data modeling and show superior performance to deep
learning. Despite their theoretical advantages [33–35], DNNs pose many
challenges when applied to tabular data, such as lack of locality, sparseness
of data (missing values), mixed feature types (numerical, ordinal, and
categorical) and lack of prior knowledge about the structure of the data set
(as opposed to text or images). Ensemble-of-trees algorithms, such as XGBoost, are considered the
recommended choice for real-life tabular data problems [32], [36]. XGBoost has been used to
classify Atrial Fibrillation [37]. One study proposes ECG signals classifier
based on XGBoost and ensemble empirical mode
decomposition (EEMD) that takes advantage of functions based on time, frequency,
and morphological characteristics [38]. Another study proposes to create a
set of five-dimensional morphological features regarding QRS complexes and RR
intervals, as well as some wavelet coefficient features, to build the feature
vector for highly efficient heartbeat classification [21]. Performance
measures are trained to find features that are correctly classified and those
that are not well classified, then their
relationship is used to find the efficiency of the classifier. We can get a
high ratio even if all the important classes are misclassified. To overcome
this, the data must be properly balanced [39]. SMOTE, “Synthetic Minority
Oversampling Technique” can overcome some classification disadvantages [40].
This method has been shown to be better than other
mixtures of under-sampling and oversampling. A
study conducted in the year 2021 compared the performance of XGBoost and the DNN using the Adamax
optimizer and binary cross-entropy loss function with four hidden layers. The
results showed that the XGboost outperformed the
DNN by achieving a learning accuracy of 100%, while its prediction accuracy
was 95.60% and 93.08%, for the same phases [41]. The overall learning
performance of the DNN model was 89.42% and 81.23%, while the prediction
accuracy was 80.50% and 77.36%, respectively, for the same variables [41].
The goal of our study was to compare the algorithms to determine the most
cost-effective solution for real-time arrhythmia detection. Single-lead
ECG monitors are frequently used because of their highly productive nature,
short run time, and low cost [42]. However, single-lead ECG cannot |
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capture all the information due to the
great diversity of CVD features that can cause misdiagnosis [43]. 2.3.
Artificial
Intelligence The object of the
work is to select an algorithm for the classification of cardiac alterations
that can be executed in real time, while the
electrocardiographic signal is being acquired. Given that the evaluated
algorithms are based on the identification and
classification of a single cardiac cycle, the ideal would be to have an
algorithm capable of capturing and classifying the signals during the period
between waves, better known as the T-P segment or interval, as shown in
Figure 1. To
execute the algorithm in real time, the execution time should be less than
the T-P interval or, in other words, less than 200 ms.
We analyzed files, from the Physionet databases
regarding spread and available for research of electrocardiograms ECG. Physionet was developed by the
Beth Israel Hospital in Boston (now the Beth Israel Deaconess Medical Center)
in conjunction with the Massachusetts Institute of Technology: (MIT-BIH) [44]
and the Physikalisch - Technische
Bundesanstalt, the National Metrology Institute of
Germany: (PTB). The MIT-BIH Base has 109444 ECGs and the PTB Base has 14550
ECGs [45].
Figure 1.
Two Independent Cardiac Cycles A and B within their respective detection
window with the T-P interval identified MIT-BIH is an
arrhythmia database, so it has a classification labeled “N”: 0, “S”: 1, “V”:
2, “F”: 3, “Q”: 4, where 0 is NORMAL and from 1 to 4 are arrhythmias
which are classified as Bradyarrhythmia’s
and Tachyarrhythmias; subclassified
as Supraventricular Tachyarrhythmias and Ventricular
Tachyarrhythmias. In the PTB database, the
classification is 0 NORMAL and 1 ABNORMAL, where severe heart disease such as
myocardial infarction (mostly), heart failure and bundle branch block are
considered. The csv files available in Kaggle have 187
columns that represent the ECG bio-signal and an additional 188 column that
classifies the ECG. This field is available in both databases and allows the
applicability of machine learning. Three
algorithms were considered: CNN convolutional neural
network, ANN artificial neural netw and the reinforced tree eXtreme
Gradient Boost o XGBoost. |
For the neural
network algorithms, the works published by Premanand
S, available at Analytics Vidhya, were taken as reference. To
avoid underfitting and overfitting problems in
machine learning, the SMOTE function was applied to
both databases separately, which creates new ECGs based on the original data
and balances the categories. A
division by 80 is performed, -20-20 to both databases obtaining:
289878-72470-90587 ECGs and for PTB 13446-3362-4202 in Training, Test and
Validation ECG data for MIT and PTB respectively. ECG samples from both
databases and in the different categories of normal and abnormal have been plotted in Figures 2 and 3.
Figure 2. MIT-BIH classified signals We
have proceeded to train with ANN, CNN and XGBoost
both the MIT Database and the PTB separately to make a comparison in terms of
training times, and levels of precision in the prediction: accuracy,
precision, recall, generating the required confusion matrices. The training results were first validated using the test and validation data from both MIT and PTB separately. And then the prediction level is validated by crossing data between both databases. A CNN architecture proposes to select an optimal group of individual layers and the size of the filters. The following |
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values were the chosen: 2 dense layers,
layer size 128, number of 2D convolutional layers and MaxPooling
2D [12N] [46].
Figure 3. MIT-BIH classified signals 3. Results and discussion We
proceeded to train with ANN, CNN and XGBoost both
the MIT Database and the PTB separately to make a comparison in terms of
Training times, and levels of precision in the prediction: Accuracy,
Precision, Recall, are presented in the required confusion matrices, Figures
4 and 5.
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Figure 4. PTB classified results
Figure 5. Resultados de clasificación sobre la MIT-BIH
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Accuracy
vs. Loss for each of the algorithms for the PTB and MIT databases are presented in Figures 6 and 7.
Figure 6.
Accuracy vs. Loss for each of the algorithms using PTB dataset
Figure 7.
Accuracy vs. Loss for each of the algorithms using MIT dataset FIT Times and
Accuracy obtained for each of the databases is presented in Figure 8.
Figure 8. Fit and Accuracy for PTB and MIT datasets After
evaluating the training results separately prediction algorithms level was
cross validated by exchanging data between both databases, as shown in |
Figure 9. For
validation purposes, the PTB database was categorized as 0 = normal and 1 =
abnormal and processed by the learning models based on XGBoost
and ANN.
Figure 9. PTB data Cross Validated
on MIT dataset using ANN and XGBoost The training phase
was completed through trialand-error definitions,
the hyperparameters were properly configured
according to Table 1 so that they reach the expected level of accuracy ECGs from the PTB
database were validated for prediction to machine
learning models based on ECGs from the MIT-BIH database, obtaining Accuracy
levels of 85% and 86% for normal ECGs. Regarding the validation of
abnormalities, XGBoost had an Accuracy of 11% and
ANN 15%. This is because they handle different heart diseases MIT-BIH
arrhythmias and PTB acute myocardial infarctions. Table 1. Hyper- Parameter
This proves that
normal signals can be recognized cross platform, but
abnormal ECG data is not interoperable between one database and another. |
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Finally, using
supervised learning, the 2 artificial neural network models and the XGBoost algorithm for prediction by classification, were
compared using a weight matrix, considering criteria such as prediction |
accuracy, sensitivity of medical data
(false positive/- false negative), the learning time, the prediction time, as
shown in Table 2. |
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Table 2. PTB
data Cross Validated on MIT dataset using ANN and XGBoost
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As previously
defined, the object of the work is to select an algorithm for the
classification of cardiac alterations that can be executed
in real time, while the electrocardiographic signal is being acquired. Given
that the results from Table 2 a point comparison can be
obtained to determine what is the best algorithm for a u-health
solution (Figure 10).
Figure 10.
Point comparison between the three algorithms For the learning
phase of the models, the 123994 electrocardiographic records were used, among
the predictive values obtained, the model that provides the highest
prediction accuracy was determined; Since artificial intelligence has managed
to learn how to properly |
classify conditions with cardiac
pathologies, the most suitable and applicable model for the diagnosis of
people or population groups was selected. In matters of prevention, the
prediction of risk for the general population is of vital importance since it
can reduce the impact of deaths due to cardiac pathologies, as well as reduce
the costs related to these cases. 4. Conclusions From the discussion, a weight
matrix was used to compare the quality of the 3
prediction algorithms. Based on such results we conclude that CNN
(convolutional neural networks) are much more accurate than other algorithms
(99%), however, training time is high (in terms of hours), when compared to
the XGBoost training that is obtained within
minutes. Since we are dealing with Human Health, precision and accuracy in
prediction have more weight than speed in training. As an intermediate
we have the artificial neural network ANN that with 97% accuracy is very
acceptable. XGBoost, given the tabular nature of
the data, is the best choice as seen from Figure 10. Prior conclusion
indicates that it is possible to obtain information about arrhythmia within
the RR interval. Since the goal of the project was to process data real time,
the results are highly encouraging. For future work we intend to use ECG data
from smart watches that are being generated as part of a doctoral |
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research. Arrhythmia detection from smart
watches would be a great tool for early detection of potential
life-threatening events such as fibrillation. False positives however need to
be reduced and since we could process data in real
time, the joint probability distribution can be used in future work to
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