KSC 2020
Current Result Document :
| 한글제목(Korean Title) |
데이터 증강 기술을 활용한 ECG 분류기 성능 개선 |
| 영문제목(English Title) |
Improving the performance of an ECG classifier based on data augmentation technology |
| 저자(Author) |
주우
박주영
안진성
강경태
Yu Zhou
Juyoung Park
Jisung An
Kyungtae Kang
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| 원문수록처(Citation) |
VOL 47 NO. 02 PP. 0690 ~ 0692 (2020. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Arrhythmia is most commonly diagnosed using an electrocardiogram (ECG) with classification techniques based on various heart arrhythmia characteristics, The most commonly used heartbeat data set is the MIT-BIH arrhythmia database. A heartbeat is classified into normal or abnormal types, of which abnormal types can be classified into 15 types. However, despite many studies on prior classification techniques, these 15 types of data are still not uniformly classified and distributed. In this paper, we propose the application of a generative adversarial network, a data augmentation technique, in the arrhythmia diagnosis process and enhance the classification of the 15 abnormal heartbeat types. We expect to achieve accurate classification of normal and abnormal types of heartbeats through our study and the further classification of abnormal heartbeats into specific types.
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| 키워드(Keyword) |
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| 파일첨부 |
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