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논문 기본 정보

자료유형
학술저널
저자정보
Urtnasan Erdenebayar (Yonsei University) Jong-Uk Park (Yonsei University) SooYong Lee (Yonsei University) Eun-Yeon Joo (Sungkyunkwan University School of Medicine) Kyoung-Joung Lee (Yonsei University)
저널정보
한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.2
발행연도
2020.6
수록면
138 - 144 (7page)
DOI
10.5391/IJFIS.2020.20.2.138

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초록· 키워드

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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.

목차

Abstract
1. Introduction
2. Materials
3. Methods
4. Result
5. Discussion
References

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