Citation: Shin, S.; Kang, M.; Zhang,
G.; Jung, J.; Kim, Y.T. Lightweight
Ensemble Network for Detecting
Heart Disease Using ECG Signals.
Appl. Sci. 2022, 12, 3291. https://
doi.org/10.3390/app12073291
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 3 February 2022
Accepted: 22 March 2022
Published: 24 March 2022
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Article
Lightweight Ensemble Network for Detecting Heart Disease
Using ECG Signals
Siho Shin , Mingu Kang, Gengjia Zhang, Jaehyo Jung * and Youn Tae Kim *
AI Healthcare Research Center, Department of IT Fusion Technology, Chosun University, Gwangju 61452, Korea;
shshin@chosun.kr (S.S.); 20134493@chosun.kr (M.K.); 20175192@chosun.kr (G.Z.)
* Correspondence: jh.jung@chosun.ac.kr (J.J.); petruskim@chosun.ac.kr (Y.T.K.)
Abstract:
Heart disease should be treated quickly when symptoms appear. Machine-learning meth-
ods for detecting heart disease require desktop computers, an obstacle that can have fatal conse-
quences for patients who must check their health periodically. Herein, we propose a MobileNet-based
ensemble algorithm for arrhythmia diagnosis that can be easily and quickly operated in a mobile
environment. The electrocardiogram (ECG) signal measured over a short period of time was aug-
mented using the matching pursuit algorithm to achieve a high accuracy. The arrhythmia data were
classified through an ensemble classifier combining MobileNetV2 and BiLSTM. By classifying the
data using this algorithm, an accuracy of 91.7% was achieved. The performance of the algorithm
was evaluated using a confusion matrix and a receiver operating characteristic curve. The sensitivity,
specificity, precision, and F1 score were 0.92, 0.91, 0.92, and 0.92, respectively. Because the proposed
algorithm does not require long-term ECG signal measurement, it facilitates health management for
busy people. Moreover, parameters are exchanged when learning data, enhancing the security of the
system. In addition, owing to the lightweight deep-learning model, the proposed algorithm can be
applied to mobile healthcare, object detection, text recognition, and authentication.
Keywords: ensemble network; MobileNetV2; BiLSTM; matching pursuit; arrhythmia
1. Introduction
Biosignals are indicators of physical health that allow the management of various
diseases, such as muscle pain, insomnia, and heart disease [
1
–
3
]. The electrocardiogram
(ECG) is the most important signal for confirming the state of the heart [4,5].
In an ECG, the heartbeat is represented by electrical signals. The heart rate of a
healthy person is usually between 60 and 100 beats per minute [
6
]. When a person is
exercising, tense, or excited, the heart beats faster. There are typically no problems in such
cases; however, when the heart beats irregularly for no reason, this symptom is called
arrhythmia. According to NHANES 2015–2018 data, the prevalence of cardiovascular
disease (comprising coronary heart disease, heart failure, stroke, and hypertension) in
adults
≥
20 years of age is 49.2% overall (126.9 million in 2018) and increases with age for
both males and females. The prevalence of cardiovascular disease excluding hypertension
is 9.3% overall (26.1 million in 2018) [
7
]. The rapid increase in the number of heart-disease
patients due to changes in eating habits and reduced exercise has contributed to the most
serious death rate of modern people. The most representative type of heart disease is
arrhythmia. Early detection of arrhythmia is crucial, because arrhythmia causes symptoms
such as dizziness, fainting, chest pain, and difficulty breathing, and can lead to heart
attacks [8].
Methods of diagnosing arrhythmia include periodically visiting a hospital or using a
Holter monitor. However, both of these are inconvenient for patients and can be expensive.
In addition, ECG signals measured by widely used smart watches are acquired over a short
period of <1 min; therefore, it is impossible to identify cardiovascular diseases, including
arrhythmia, using such devices.
Appl. Sci. 2022, 12, 3291. https://doi.org/10.3390/app12073291 https://www.mdpi.com/journal/applsci
