Article
k-Labelsets Method for Multi-Label ECG Signal Classification
Based on SE-ResNet
Jihye Yoo, Yeongbong Jin , Bonggyun Ko * and Min-Soo Kim *
Citation: Yoo, J.; Jin, Y.; Ko, B.; Kim,
M.-S. k-Labelsets Method for
Multi-Label ECG Signal Classification
Based on SE-ResNet. Appl. Sci. 2021,
11, 7758. https://doi.org/
10.3390/app11167758
Academic Editor: Keun Ho Ryu
Received: 26 July 2021
Accepted: 19 August 2021
Published: 23 August 2021
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4.0/).
Department of Mathematics and Statistics, Chonnam National University, 77, Yongbong-ro, Buk-gu,
Gwangju 61186, Korea; jihye0205.yoo@gmail.com (J.Y.); 198010@jnu.ac.kr (Y.J.)
* Correspondence: bonggyun.ko@jnu.ac.kr (B.K.); kimms@jnu.ac.kr (M.-S.K.)
Abstract:
Cardiovascular diseases are the leading cause of death globally. The ECG is the most
commonly used tool for diagnosing cardiovascular diseases, and, recently, there are a number of
attempts to use deep learning to analyze ECG. In this study, we propose a method for performing
multi-label classification on standard ECG (12-lead with duration of 10 s) data. We used the ResNet
model that can perform residual learning as a base model for classification in this work, and we
tried to improve performance through SE-ResNet, which added squeeze and excitation blocks on
the plain ResNet. As a result of the experiment, it was possible to induce overall performance
improvement through squeeze and excitation blocks. In addition, the random k-labelsets (RAKEL)
algorithm was applied to improve the performance in multi-label classification problems. As a result,
the model that applied soft voting through the RAKEL algorithm to SE-ResNet-34 represented the
best performance, and the average performances according to the number of label divisions
k
were
achieved 0.99%, 88.49%, 92.43%, 90.54%, and 93.40% in exact match, accuracy, F1-score, precision,
and recall, respectively.
Keywords:
computer aided diagnosis; ECG classification; multi-label classification; squeeze and
excitation network
1. Introduction
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity
worldwide, and are a generic term for disorders of the heart or blood vessels. According
to the World Health Organization (WHO), approximately 17.9 million people died from
CVDs in 2019, accounting for 32% of global deaths [
1
]. In particular, about 80% of these
sudden cardiac deaths are the result of ventricular arrhythmias [
2
,
3
]. Arrhythmias are
when the electrical signals that control the heart’s rhythm are out of sequence. In other
words, arrhythmia is an abnormality in the rhythm of the heart, which can be slow, fast,
or irregular [
3
]. Arrhythmias are accompanied by various symptoms and have various
risks ranging from mild fluttering to death. Because of the high mortality rates of CVDs,
early detection and accurate identification of arrhythmias are essential for treatment of
patient [
4
]. The electrocardiogram (ECG), which records the electrical activity of the
heart, is the most commonly used tool to detect arrhythmias due to its low cost and
non-invasive characteristics.
The standard ECG refers to the 12-lead ECG with a short duration of 10 s, which can
provide sufficient information for the diagnosis of various disease [
5
]. Therefore, a method
that allows for an accurate interpretation of the ECG is required. However, the diagnosing
of arrhythmias through ECG records requires a time-consuming process by an experienced
physician. Furthermore, there may be subtle changes in the ECG that have not been
detected. To overcome these problems, computer-aided diagnosis (CAD) algorithms have
been used to automate the diagnosis of arrhythmias. Traditional CAD methods require the
use of manually processed features, which are the most important step for classification [
6
].
Kernel-based [
7
–
9
], wavelet transform [
10
–
12
], and Fourier transform [
13
,
14
] methods
Appl. Sci. 2021, 11, 7758. https://doi.org/10.3390/app11167758 https://www.mdpi.com/journal/applsci
