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International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(2): 138-144

Published online June 25, 2020

https://doi.org/10.5391/IJFIS.2020.20.2.138

© The Korean Institute of Intelligent Systems

Prediction Method of Periodic Limb Movements Based on Deep Learning Using ECG Signal

Urtnasan Erdenebayar1 , Jong-Uk Park1 , SooYong Lee2 , Eun-Yeon Joo3 , and Kyoung-Joung Lee1

1Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea
2Department of Liberal Education, Yonsei University, Wonju, Korea
3Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

Correspondence to :
Kyoung-Joung Lee (lkj5809@yonsei.ac.kr)

Received: December 31, 2019; Revised: April 8, 2020; Accepted: May 15, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

In this study, we demonstrated a novel method to predict a patient with periodic limb movements (PLMs) based on a deep learning model using an electrocardiogram (ECG) signal. A convolutional neural network (CNN) model was used to distinguish between the PLM and control subjects through morphological analysis of an ECG signal. The constructed CNN model consisted of convolutional, pooling, and fully connected layers. For this study, polysomnography (PSG) data that were measured from 14 subjects at the Samsung Medical Center were used. The subjects were divided into control group (4 males, 3 females) and PLM group (4 males, 3 females). To train and evaluate the CNN model, the ECG dataset was collected during the PSG study, and it was normalized and segmented at a duration of 10 s. The training and test sets consisted of 30,324 and 7,582 segments, respectively. The CNN model presented a prediction performance with an F1-score of 100.0% for the test sets. We obtained robust results that demonstrated the possibility of the automatic screening of PLM patients using the CNN model with an ECG signal.

Keywords: Periodic limb movement disorder, Convolutional neural network, Deep learning, Electrocardiogram

This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the National Innovation Cluster R&D Program (No. P0006697; Development of a Cardiopulmonary Monitoring System Using Wearable Device). This research was a result of a study on the “HPC Support” Project, supported by the ‘Ministry of Science and ICT’ and NIPA.

No potential conflict of interest relevant to this article was reported.

Urtnasan Erdenebayar received his B.S. in Computer science from Huree University, Ulaanbaatar, Mongolia, in 2007 and M.S. in Electronic engineering from Inha University, Incheon, Korea, in 2010, respectively. He also received his Ph.D. in Biomedical Engineering from Yonsei University, Seoul, Korea, in 2018. Since 2018, he is a Postdoc researcher at the Department of Biomedical Engineering, Yonsei University. His research interests are artificial intelligence, deep learning, machine learning, digital healthcare, digital medicine, data science, and biosignal processing.

E-mail: edenbyra@yonsei.ac.kr


Jong-Uk Park received his B.S. in Biomedical engineering from Konyang University, Daejeon, Korea, in 2008 and M.S. in Biomedical Engineering from Yonsei University, Wonju, Korea, in 2012, respectively. He is currently Ph.D. candidate at Department of Biomedical Engineering, Yonsei University. He has been working on research related to sleep signal analysis, algorithm development and signal processing.

E-mail: pjwwhite01@naver.com


SooYong Lee received his Ph.D. in Mathematics from the Kyunghee University, Seoul, Korea, in 1992. He also received his Ph.D in Computer Science from the Yonsei University, Seoul, Korea, in 2004. He is a faculty member at Yonsei University, Wonju, Korea. Since 2004, he has been working on research related to artificial intelligence, machine learning and data mining.

E-mail: 0691@yonsei.ac.kr


Eun-Yeon Joo received the M.D., M.S., and Ph.D. degrees in Neurology from the Ewha Womans University, Seoul, Korea, in 1997, 2001, and 2005, respectively. She served as an intern and resident in neurology at Ewha Womans University Hospital, Neurology Department, Seoul Korea from 1997 to 2001. She was a visiting scholar with the Northwestern University Hospital, Sleep Center, Chicago, IL, USA. Since 2007, she has been a faculty member of Neurology, Samsung Medical Center, the Sungkyunkwan University, School of Medicine, Seoul, Korea.

E-mail: eunyeon.joo@gmail.com


Kyoung-Joung Lee Lee received the B.S., M.S., and Ph.D. degrees in electric engineering from the Yonsei University, Seoul, Korea, in 1981, 1983, and 1988, respectively. He was an international fellow at Case Western Reserve University, USA, in 1993. Since 1989, He has been a faculty member with the Biomedical Engineering Department, Yonsei University, Wonju, Korea. He is the coauthor of five books, more than 130 articles, and more than 30 inventions. His research interests include medical instrument, biosignals processing, and bio-system modelling.

E-mail: lkj5809@yonsei.ac.kr


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(2): 138-144

Published online June 25, 2020 https://doi.org/10.5391/IJFIS.2020.20.2.138

Copyright © The Korean Institute of Intelligent Systems.

Prediction Method of Periodic Limb Movements Based on Deep Learning Using ECG Signal

Urtnasan Erdenebayar1 , Jong-Uk Park1 , SooYong Lee2 , Eun-Yeon Joo3 , and Kyoung-Joung Lee1

1Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea
2Department of Liberal Education, Yonsei University, Wonju, Korea
3Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

Correspondence to:Kyoung-Joung Lee (lkj5809@yonsei.ac.kr)

Received: December 31, 2019; Revised: April 8, 2020; Accepted: May 15, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this study, we demonstrated a novel method to predict a patient with periodic limb movements (PLMs) based on a deep learning model using an electrocardiogram (ECG) signal. A convolutional neural network (CNN) model was used to distinguish between the PLM and control subjects through morphological analysis of an ECG signal. The constructed CNN model consisted of convolutional, pooling, and fully connected layers. For this study, polysomnography (PSG) data that were measured from 14 subjects at the Samsung Medical Center were used. The subjects were divided into control group (4 males, 3 females) and PLM group (4 males, 3 females). To train and evaluate the CNN model, the ECG dataset was collected during the PSG study, and it was normalized and segmented at a duration of 10 s. The training and test sets consisted of 30,324 and 7,582 segments, respectively. The CNN model presented a prediction performance with an F1-score of 100.0% for the test sets. We obtained robust results that demonstrated the possibility of the automatic screening of PLM patients using the CNN model with an ECG signal.

Keywords: Periodic limb movement disorder, Convolutional neural network, Deep learning, Electrocardiogram

Fig 1.

Figure 1.

Architecture of the proposed deep learning model for PLM prediction.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 138-144https://doi.org/10.5391/IJFIS.2020.20.2.138

Fig 2.

Figure 2.

(a) Accuracy and (b) loss functions of the proposed deep learning model for PLM prediction.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 138-144https://doi.org/10.5391/IJFIS.2020.20.2.138

Table 1 . Characteristics of the study population.

MeasuresControl (n = 7)PLM (n = 7)p-value
Sex
 Male44
 Female33

Age (yr)49.4±5.949.6±5.9NS

Body mass index (kg/m2)22.2±1.822.4±1.9NS

PLM index (/hr)0.0±0.022.9±6.5<0.001

Total sleep time (hr)6.9±0.46.3±0.6NS

Sleep efficiency (%)88.1±4.988.7±5.6NS

Values are presented as mean±standard deviation..

NS, not significant..


Table 2 . Information of the training and test sets.

DatasetTraining setTest setTotal
Number of subjects10414
 Control group527
 PLM group527

Age (yr)48.5±6.752.0±3.849.5±6.1

Body mass index (kg/m2)22.5±2.121.9±1.622.3±2.0

PLM index (/hr)10.6±12.213.6±16.011.5±12.8

Total sleep time (hr)6.5±0.76.8±0.56.6±0.6

Total episodes30,3247,58237,906

Values are presented as mean±standard deviation..


Table 3 . Performance of the proposed deep learning model for PLM prediction.

DatasetsEpisodesPrecisionRecallF1-scoreAccuracy
Training setCTR1.001.001.000.998
PLM1.001.001.00

Test setCTR1.001.001.000.992
PLM1.001.001.00

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