Cardiac Arrhythmia Detection in Electrocardiogram Signals with

CNN-LSTM

Igor Lopes Souza

and Daniel Oliveira Dantas

Departamento de Computac¸

ao, Universidade Federal de Sergipe, S

ao Crist

ao, SE, Brazil

Keywords:

CNN, Electrocardiography, ECG, Atrial Fibrillation.

Abstract:

Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. Electrocardiog-

raphy is essential in the clinical evaluation of patients who have heart disease. Through the electrocardiogram

(ECG), medical doctors can identify whether the cardiac muscle dysfunctions presented by the patient have

an inﬂammatory origin and diagnose early serious diseases that primarily affect the blood vessels and the

brain. The basis of arrhythmia diagnosis is the identiﬁcation of normal and abnormal heartbeats and their

classiﬁcation into different diagnoses based on ECG morphology. Traditionally, ECG signals are classiﬁed

manually, requiring experience and great skill, while being time-consuming and prone to error. Thus, machine

learning algorithms have been widely adopted because of their ability to perform complex data analysis. The

objective of this study is to develop a classiﬁer capable of classifying a patient’s ECG signals for the detection

of arrhythmia in clinical patients. We developed a convolutional neural network (CNN) with long short mem-

ory (LSTM) to identify ﬁve classes of heartbeats in ECG signals. Our experiment was conducted with ECG

signals obtained from a publicly available MIT-BIH database. The number of instances was even out to ﬁve

classes of heartbeats. The proposed model achieved an accuracy of 98.12% and an F1-score of 99.72% in the

classiﬁcation of ventricular ectopic beats (V), and an accuracy of 97.39% and an F1-score of 95.25% in the

classiﬁcation of supraventricular ectopic beats (S).

1 INTRODUCTION

According to the World Health Organization, cardio-

vascular diseases (CVDs) are the leading cause of

death in the world (Wold Health Organization, 2023).

Arrhythmia, a heart rhythm disorder, is considered

one of the most common disorders of the heart. Ar-

rhythmias can lead to tachycardia or even sudden car-

diac arrest. Heartbeat classiﬁcation based on ECG

signal has become a valuable and promising tech-

nique for early warning of arrhythmias. However,

variations in ECG signals can be signiﬁcant among

different subjects. Under different circumstances, the

waveform and rhythms produced by the arrhythmia

symptoms can be quite different as well. Experi-

enced cardiologists can easily distinguish abnormal

heartbeats from normal sinus rhythms by observing

the ECG. However, this is still challenging for pa-

tients who wish to accompany their clinical symp-

toms. Computer-driven signal processing is an im-

portant tool to diagnose arrhythmia in the ﬁeld of

https://orcid.org/0000-0002-2499-4607

https://orcid.org/0000-0002-0142-891X

biomedical engineering. Today, biomedicine has ad-

vanced to the stage of the practical application of sig-

nal processing and pattern analysis techniques.

Many approaches to arrhythmia heartbeat classi-

ﬁcation with convolutional neural networks (CNN)

have been proposed. Ozaltin (Ozaltin and Yeniay,

2022) proposed a novel CNN architecture to detect

ECG types. In addition, the proposed CNN can au-

tomatically extract features from images. He clas-

siﬁes an ECG dataset using a CNN with 34 layers.

While this dataset is composed of 1D signals, these

are transformed into images using continuous wavelet

transform (CWT). In addition, the proposed CNN

is compared to known architectures, AlexNet and

SqueezeNet, for classifying ECG images. Ozaltin not

only performed CWT but also implemented a short-

time Fourier transform. Ozaltin obtained an accuracy

of 99.21% from the proposed CNN–SVM when using

CWT.

Li (Li et al., 2018) proposed a generic convo-

lutional neural network (GCNN) trained ﬁrst using

heartbeats without distinguishing patients. Based on

the GCNN, the ﬁne-tuning technique is applied to

304

Souza, I. and Dantas, D.

Cardiac Arrhythmia Detection in Electrocardiogram Signals with CNN-LSTM.

DOI: 10.5220/0012362600003654

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2024), pages 304-310

ISBN: 978-989-758-684-2; ISSN: 2184-4313

modify the GCNN to a tuned dedicated CNN (TD-

CNN) for the corresponding individual. Fine-tuning

is done in several seconds rather than dozens of min-

utes, necessary for the TDCNN to be used to monitor

the long-term ECG signals in clinics. To accelerate

the ECG classiﬁcation, only the original ECG heart-

beat is input to the CNN without other extended in-

formation from the adjacent heartbeats or FFT repre-

sentation.

Cao (Cao et al., 2022) proposed a novel nonlinear

adaptive noise-canceling framework (ANC) based on

a temporal CNN to effectively extract fetal ECG sig-

nals from mothers’ abdominal ECG recordings. The

proposed framework consists of a two-step network,

using the ANC architecture; one network is for the

maternal ECG component elimination and the other

is for the residual noise component removal of the

extracted fetal ECG signal. Then, joint approxima-

tion diagonalization of eigenmatrices (JADE), one of

the blind source separation algorithms, is applied as

a post-processing step to produce a clean fetal ECG

signal.

Kamozawa (Kamozawa et al., 2023) proposed

a method for detecting atrial ﬁbrillation (AF) from

an electrocardiogram (ECG) measured by a 24-hour

Holter electrocardiograph (Holter-ECG) using CNN.

In the preprocessing step, artifacts and noises on

Holter-ECG are removed by a bandpass ﬁlter. The

detection method consists of extraction of abnormal

waveforms using 1D CNN trained with segmented

ECG waveform, spectral entropy, and identiﬁcation

of AF.

Xiong (Xiong et al., 2017) created a data-driven

deep learning pipeline using a 16-layer CNN for the

automatic classiﬁcation of ECG signals. Xiong used

a large dataset recorded from patients and labeled by

medical experts in ECG for developing and validating

his approach.

Kiranyaz (Kiranyaz et al., 2017) created a sys-

tem that can detect early occurrences of arrhythmias,

by modeling common causes of arrhythmias in a pa-

tient’s ECG signal. The causes of arrhythmia are

modeled as a degradation from normal ECG beats.

The CNN was trained using real normal beats and

synthesized abnormal beats.

Han (Han and Shi, 2020) proposed a multi-lead

attention (MLA) mechanism integrated with a CNN

to detect myocardial infarction (MI) in 12-lead ECG

recordings. The MLA automatically assigns weights

to different leads and a 2D CNN extracts discrimina-

tive features from all the 12 leads. The robustness of

the MI detection was tested in both intra-patient and

inter patient schemes.

Martis (Martis et al., 2014) investigated four dif-

ferent methods for atrial ﬁbrillation and atrial ﬂut-

ter feature extraction: the principal components

of discrete wavelet transform coefﬁcients, indepen-

dent components of discrete wavelet transform co-

efﬁcients, principal components of discrete cosine

transform coefﬁcients, and independent components

of discrete wavelet transform coefﬁcients methods.

Martis explored three different classiﬁcation tech-

niques: K-nearest neighbor, decision tree, and artiﬁ-

cial neural network. The methodology used data from

MIT-BIH arrhythmia and atrial ﬁbrillation databases.

Discrete cosine transform coupled with independent

component analysis and K-nearest neighbor yielded

the highest average sensitivity of 99.61%, average

speciﬁcity of 99.99%, and classiﬁcation accuracy of

99.45% using tenfold cross-validation.

We believe that it is possible to further improve

the accuracy, sensitivity, speciﬁcity, precision, and

F1-score of CNN heartbeat classiﬁers of our previous

works. Our study aims to improve the classiﬁcation

metrics of our previous study by using LSTM com-

bined with CNN models (Souza and Dantas, 2023).

We used a ﬁne-tuning step for further optimization

of the classiﬁcation of arrhythmia. The improved

classiﬁcation of ECG signals will generate more ac-

curate responses in the detection of cardiac arrhyth-

mias, facilitating the health care of patients. The

proposed neural network architecture provides a bet-

ter F1-score when compared to the previously listed

works, and provides a higher F1-score and less train-

ing time when compared to our previous work.

2 METHODOLOGY

In this study, we created classiﬁers based on CNN-

LSTM and AlexNet capable of distinguishing the dif-

ferent types of heartbeats and detecting cardiac ar-

rhythmia. Their architecture was ﬁne-tuned so that

the models achieved the highest validation accuracy

possible and were compared to the decision tree, ran-

dom forest and extra trees classiﬁers (Kumar, 2022).

Our models were based on a previous study (Souza

and Dantas, 2023) and evaluated in the test set. The

ECG heartbeat classiﬁer is composed of two main

steps: preprocessing and classiﬁcation. The CNN-

LSTM and AlexNet network architectures are shown

in Figure 1 and Figure 2 respectively. The implemen-

tation of this methodology is publicly available

and

was coded in Python using Tensorﬂow, Keras, and

Numpy.

https://github.com/Igor-Lopes-Souza/2023-CNN-

LSTM

Cardiac Arrhythmia Detection in Electrocardiogram Signals with CNN-LSTM

305

Figure 1: CNN-LSTM architecture.

Figure 2: AlexNet architecture.

In the following subsections we will describe the

dataset used, the preprocessing step, which includes

ﬁltering and data augmentation, the classiﬁer archi-

tecture, and classiﬁer training.

2.1 Dataset

In this study, we used the ECG Heartbeat Catego-

rization Dataset, freely available in the Internet

. We

used only the portion of the dataset derived from the

Physio Bank MIT-BIH Arrhythmia database (Mark

and Moody, 1988). This database consists of a 48

half-hour long ECG recordings from 47 subjects—

obtained with a Lead II ECG conﬁguration—that

https://www.kaggle.com/datasets/shayanfazeli/heartbeat

Figure 3: Triple-convolution.

were band-pass ﬁltered over the frequency range from

0.1 to 100Hz and digitized at 360 samples per second.

Furthermore, these recordings were interpreted and

validated by at least two cardiologists. The database

consists of annotations for both heartbeat class in-

formation and R-peak position information veriﬁed

by two or more expert cardiologists. The 17 beat

types can be grouped into ﬁve beat classes deﬁned by

the Association of Advancement for Medical Instru-

mentation (AAMI) which follows the American Na-

tional Standard for Ambulatory ECGs (ANSI/AAMI

EC38:2007) recommendations. The ﬁve beat types

are the non-ectopic beat (N), supraventricular ectopic

beat (S), ventricular ectopic beat (V), fusion beat (F),

and unknown (Q).

2.2 Preprocessing

The MIT-BIH dataset is unbalanced, difﬁculting the

analysis of the signal. The original dataset contains

a total of 109,446 data rows. Each data row contains

a fraction of the ECG signal with duration of 10 sec-

onds and its class, speciﬁed in the last column by a

number from 0 to 4 representing N, S, Q, F, and Q.

There are 70,043 data rows for training, 17,511 for

validation and 21,892 for testing, making the propor-

tions 65/15/20. We augmented the training and vali-

dation datasets to match the value of the biggest class

from the ﬁve types of heartbeats.

The raw MIT-BIH signal is corrupted by myo-

electric interference, power line interference, and line

drift. We added noise through the Gaussian func-

tion, by creating a random variable and adding it to

our train dataset in order to simulate the myoelec-

tric interferences and better train our model (Yong

et al., 2011). The noise serves to better train our

model by adding a varying variable to our dataset,

acting as a probability distribution. To remove the

noise, the raw ECG signal is ﬁltered using wavelet

ﬁlters (Audhkhasi et al., 2016). The raw signal is de-

composed by Daubechies wavelet 6(db6) at six levels,

and wavelet coefﬁcients from the third to the sixth

level were retained and used for signal reconstruc-

tion (Shi et al., 2019).

ICPRAM 2024 - 13th International Conference on Pattern Recognition Applications and Methods

306

Table 1: Hyperparameter values chosen in classiﬁer ﬁne-tuning.

Parameters Values Chosen Value

Dropout 0.10, 0.20, 0.30, 0.40, 0.50 0.50

Optimizer Adam, Adamax, SGD Adam

Activation

function

Relu, Softmax, Softplus Relu

Batch

size

10, 32, 54, 76, 98 98

Loss

function

Binary cross-entropy, Categorical

cross-entropy, Poisson,

Kullback-Leibler divergence, Huber

Categorical

cross-entropy

Table 2: Number of samples in the training, validation, and test sets.

Before data augmentation After data augmentation

Training Validation Test Training Validation Test

N 57974 14493 18118 57974 14493 N/A

S 1778 450 556 57974 14493 N/A

V 4630 1155 1448 57974 14493 N/A

F 520 127 162 57974 14493 N/A

Q 5141 1286 1608 57974 14493 N/A

Total 70043 17511 21892 289870 72465 N/A

2.3 Classiﬁer Architecture

Figure 1 shows the CNN-LSTM classiﬁer architec-

ture, which comprises convolutional layers, subsam-

pling layers, fully connected layers, batch normaliza-

tion layers, LSTM layers and a dropout layer. Usu-

ally, each convolution layer is followed by a subsam-

pling layer. In order to facilitate mapping between

the heartbeat category and its waveform, we use a

triple-convolution structure to achieve a more pow-

erful ﬁtting capability (Uchida et al., 2018) in our

CNN model. Figure 3 shows the structure of a triple-

convolution layer sequence.

Figure 2 show the schematic of our AlexNet clas-

siﬁer. The standard AlexNet classiﬁer (Krizhevsky

et al., 2012) is used for 2D image classiﬁcation,

while we modify its architecture for the analysis of

ECG signals, which are 1D. The AlexNet classiﬁer

comprises convolutional layers, subsampling layers,

fully connected layers, batch normalization layers,

and dropout layers. We altered the convolution, max-

pooling, and dropout layers to use their 1D versions.

In our AlexNet architecture when compared to the

standard format, we doubled the number of triple-

convolutions to obtain better accuracy and used the

batch-normalization layer to normalize the interlayer

outputs into a standard format.

In order to compare the performance of the pro-

posed models with standard classiﬁcation algorithms,

our CNN-LSTM and AlexNet models are compared

with extra trees, random forest and decision tree clas-

siﬁers (Alom et al., 2018; Yu et al., 2019) that were

trained with the sklearn default conﬁguration and our

training dataset (Kramer and Kramer, 2016). The ex-

tra trees, decision tree, and random forest classiﬁers

architecture use the following parameters in their de-

fault form:

• Minimal Number of Leaves: 1

• Minimal Number of Samples Split: 2

• Criterion: gini,

• Maximum Depth: None,

• Maximum Number of Features: sqrt,

• Maximum Number of Leaf Nodes: None,

• Maximum Number of Samples: None,

• Number of Estimators: 100

In this study, we use the ReLu activation function

in both convolutional layers and fully connected lay-

ers (Nair and Hinton, 2010; Girosi et al., 1995). In the

output layer, we use the softmax activation function to

obtain the ﬁve heartbeat classes.

2.4 Training Method

The goal of training is to reduce the value of the

loss function L, i.e., to decrease the CNN-LSTM and

AlexNet models loss and adjust the weights and bi-

ases so that Equation 1 ﬁts the model training set. The

cross-entropy function is used as the loss function (Xu

and Liu, 2020):

Cardiac Arrhythmia Detection in Electrocardiogram Signals with CNN-LSTM

307

Table 3: Comparison of the proposed algorithm classiﬁcation using ventricular ectopic beats (V).

ACC SEN SPE PRE F1S

Martis (Martis et al., 2014)

99.45% 99.61% 99.99% 99.99% 99.80%

Proposed classiﬁer:

CNN-LSTM

98.12% 99.00% 98.85% 99.39% 99.72%

Souza (Souza and Dantas, 2023)

99.33% 99.59% 99.30% 99.12% 99.44%

Sellami (Sellami and Hwang, 2019)

99.48% 96.97% 99.87% 98.83% 97.80%

Acharya (Acharya et al., 2017)

94.03% 96.71% 91.54% 97.85% 97.27%

Zhai (Zhai and Tin, 2018)

99.10% 96.40% 99.50% 96.40% 96.40%

Jiang (Jiang and Kong, 2007)

98.80% 94.30% 99.40% 95.30% 94.70%

Xiang (Xiang et al., 2018)

99.20% 93.70% 99.60% 94.80% 94.20%

Ince (Ince et al., 2009)

97.60% 83.60% 98.10% 87.40% 85.40%

Proposed classiﬁer:

AlexNet

96.66% 99.45% 99.10% 96.85% 80.07%

Table 4: Comparison of proposed implementations using ventricular ectopic beats (V).

ACC SEN SPE PRE F1S

CNN-LSTM

98.12% 99.00% 98.85% 99.39% 99.72%

Decision tree

classiﬁer

99.11% 99.22% 99.16% 99.00% 99.36%

Random forest

classiﬁer

95.32% 95.73% 98.83% 96.39% 95.40%

Extra trees

classiﬁer

95.40% 95.27% 94.70% 95.25% 95.35%

AlexNet

96.66% 99.45% 99.10% 96.85% 80.07%

We update the weights and offsets using the Adam

optimizer (Kingma and Ba, 2014). First, a batch

of samples was sent to calculate the gradient of the

Equation 1, and we set the batch size to 98:

g =

∇

∑

L( f (x

(i)

;θ), y

(i)

)

. (1)

The g is the gradient value, m is the batch size, θ is

the parameter to be updated, f (x

(i)

;θ) is the heartbeat

type predicted by the i-th sample, y

(i)

is the actual type

of the i-th sample, and L is the loss function.

After deﬁning the architecture, ﬁne-tuning was

performed to obtain the best values of dropout, op-

timizer, activation function, loss function, and batch

size. A grid search in the hyperparameter space tested

each possible combination with 20 epochs. Table 1

shows the tested hyperparameter values and the ones

that maximized accuracy.

3 RESULTS AND DISCUSSION

We performed classiﬁcation experiments on 44

recordings from the MIT-BIH arrhythmia database,

among the 48 recordings obtained from 47 patients

studied by the BIH arrhythmia laboratory, and the

heartbeats were classiﬁed according to the recom-

mendation of the AAMI.

The training dataset contains a total of 109,466

data rows of representative beats from all classes:

type-N, non-ectopic beats; type-S, supraventricular

ectopic beats; type-V, ventricular ectopic beats; type-

F, fusion beats; and type-Q, unknown beats. Clas-

siﬁcation performance is measured using the statisti-

cal error metrics found in the literature (Chen et al.,

2022): accuracy (ACC), sensitivity (SEN), speciﬁcity

(SPE), precision (PRE), and F1-score (F1S). The F1-

score measures the overall performance of the beat

classiﬁcation, as shown in Table 3.

Our CNN-LSTM and AlexNet models were im-

plemented using the TensorFlow framework. The

CNN-LSTM and AlexNet models training time of

each epoch was approximately 20s, and the maximum

epoch number was set to 100. Table 3 shows that the

CNN-LSTM model has an F1-score value compara-

ble to those of other studies, presenting the second

best results. Table 4 shows the results of the differ-

ent proposed architectures. In this study, our CNN-

LSTM model achieved an accuracy of 98.12%, sen-

sitivity of 99.00%, speciﬁcity of 98.85%, precision

of 99.39%, and F1-score of 99.72% and our AlexNet

model achieved an accuracy of 96.66%, sensitivity of

99.45%, speciﬁcity of 99.10%, precision of 96.85%,

ICPRAM 2024 - 13th International Conference on Pattern Recognition Applications and Methods

308

Table 5: Comparison of the types of heartbeats.

ACC SEN SPE PRE F1S

Normal (N)

99.45% 99.98% 92.83% 90.89% 99.44%

Supraventricular ectopic beats (S)

97.39% 88.61% 98.92% 88.92% 95.25%

Ventricular ectopic beats (V)

98.12% 99.00% 98.85% 99.39% 99.72%

Fusion Beats (F)

87.40% 77.27% 84.70% 95.25% 82.35%

Unknown Beats (Q)

99.32% 99.65% 98.72% 97.20% 99.10%

Figure 4: Confusion matrix for heartbeat classiﬁcation on

the test set.

and F1-score of 80.07%.

Figure 4 shows the confusion matrix of the clas-

siﬁcation results of the CNN-LSTM test set. The

model is able to make accurate predictions and distin-

guish different classes. The main reason behind this

might be that we have ﬁne-tuned our model, as unre-

ﬁned tests with ventricular ectopic beats (V) obtained

an average accuracy of 89.99% and an F1-score of

88.54%.

4 CONCLUSIONS

In this study, we designed an ECG signals classiﬁer

for cardiac arrhythmia detection using CNN-LSTMs.

The proposed model achieved an accuracy of 98.12%

and an F1-score of 99.72% in the classiﬁcation of ven-

tricular ectopic beats (V), and an accuracy of 97.39%

and an F1-score of 95.25% in the classiﬁcation of

supraventricular ectopic beats (S) as shown in Table 5.

In order to optimize our model, we ﬁne-tuned the hy-

perparameters. The selected values compose our ﬁ-

nal version of the classiﬁer and are displayed in Ta-

ble 1. Compared with the methods in the literature,

our model performed better in terms of ventricular ec-

topic beat classiﬁcation precision and F1-score, only

being surpassed by Martis (Martis et al., 2014).

Our trained CNN heartbeat classiﬁer model can

be used for real-life and real-time applications. It

can also be used to analyze other 1D biosignals. Fu-

ture work may reﬁne this approach with a better set

of hyperparameter values and different augmentation

strategies and training methods.

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