The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
목적: 심전도(ECG) 특징점의 검출은 심장 질환에 대한 중요한 진단 정보를 제공할 수 있습니다. 우리는 심전도 특징점의 자동 검출을 위한 새로운 특징 추출 및 기계 학습 기법을 제안했습니다. 방법: 무작위로 선택된 웨이블릿 변환(RSWT) 기능이라는 새로운 기능이 ECG 특성 지점을 나타내기 위해 고안되었습니다. 높은 민감도와 정밀도로 특징적인 점 위치를 추론하기 위해 랜덤 포레스트 분류기가 적용되었습니다. 결과: QT 데이터베이스에 대한 다른 최신 알고리즘의 테스트 결과와 비교할 때 RSWT 체계의 탐지 결과는 비슷한 성능(각 특징점에 대한 유사한 민감도, 정밀도 및 탐지 오류)을 보여주었습니다. MIT-BIH 데이터베이스에 대한 RSWT 테스트에서도 유망한 데이터베이스 간 성능이 입증되었습니다. 결론: ECG 특성 지점에 대해 새로운 RSWT 기능과 새로운 감지 방식이 제작되었습니다. RSWT는 ECG 형태를 나타내는 강력하고 신뢰할 수 있는 기능을 보여주었습니다. 의의: 제안된 RSWT 기능의 효율성을 통해 우리는 모든 유형의 ECG 특성 지점을 한 번에 자동으로 감지하는 새로운 기계 학습 기반 체계를 제시했습니다. 또한, 우리의 알고리즘은 보고된 다른 기계 학습 기반 방법보다 더 나은 성능을 달성했음을 보여주었습니다.
Dapeng FU
Chinese Academy of Sciences Zhong Guan Cun Hospital
Zhourui XIA
Beijing University of Posts and Telecommunications
Pengfei GAO
Tsinghua University
Haiqing WANG
Beijing Zhong Guan Cun Hospital, Chinese Academy of Sciences Zhong Guan Cun Hospital
Jianping LIN
Beijing XinHeYiDian Technology Co. Ltd.
Li SUN
Beijing XinHeYiDian Technology Co. Ltd.
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Dapeng FU, Zhourui XIA, Pengfei GAO, Haiqing WANG, Jianping LIN, Li SUN, "ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 8, pp. 2082-2091, August 2018, doi: 10.1587/transinf.2017EDP7410.
Abstract: Objective: Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We proposed a novel feature extraction and machine learning scheme for automatic detection of ECG characteristic points. Methods: A new feature, termed as randomly selected wavelet transform (RSWT) feature, was devised to represent ECG characteristic points. A random forest classifier was adapted to infer the characteristic points position with high sensitivity and precision. Results: Compared with other state-of-the-art algorithms' testing results on QT database, our detection results of RSWT scheme showed comparable performance (similar sensitivity, precision, and detection error for each characteristic point). RSWT testing on MIT-BIH database also demonstrated promising cross-database performance. Conclusion: A novel RSWT feature and a new detection scheme was fabricated for ECG characteristic points. The RSWT demonstrated a robust and trustworthy feature for representing ECG morphologies. Significance: With the effectiveness of the proposed RSWT feature we presented a novel machine learning based scheme to automatically detect all types of ECG characteristic points at a time. Furthermore, it showed that our algorithm achieved better performance than other reported machine learning based methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7410/_p
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@ARTICLE{e101-d_8_2082,
author={Dapeng FU, Zhourui XIA, Pengfei GAO, Haiqing WANG, Jianping LIN, Li SUN, },
journal={IEICE TRANSACTIONS on Information},
title={ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier},
year={2018},
volume={E101-D},
number={8},
pages={2082-2091},
abstract={Objective: Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We proposed a novel feature extraction and machine learning scheme for automatic detection of ECG characteristic points. Methods: A new feature, termed as randomly selected wavelet transform (RSWT) feature, was devised to represent ECG characteristic points. A random forest classifier was adapted to infer the characteristic points position with high sensitivity and precision. Results: Compared with other state-of-the-art algorithms' testing results on QT database, our detection results of RSWT scheme showed comparable performance (similar sensitivity, precision, and detection error for each characteristic point). RSWT testing on MIT-BIH database also demonstrated promising cross-database performance. Conclusion: A novel RSWT feature and a new detection scheme was fabricated for ECG characteristic points. The RSWT demonstrated a robust and trustworthy feature for representing ECG morphologies. Significance: With the effectiveness of the proposed RSWT feature we presented a novel machine learning based scheme to automatically detect all types of ECG characteristic points at a time. Furthermore, it showed that our algorithm achieved better performance than other reported machine learning based methods.},
keywords={},
doi={10.1587/transinf.2017EDP7410},
ISSN={1745-1361},
month={August},}
부
TY - JOUR
TI - ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier
T2 - IEICE TRANSACTIONS on Information
SP - 2082
EP - 2091
AU - Dapeng FU
AU - Zhourui XIA
AU - Pengfei GAO
AU - Haiqing WANG
AU - Jianping LIN
AU - Li SUN
PY - 2018
DO - 10.1587/transinf.2017EDP7410
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2018
AB - Objective: Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We proposed a novel feature extraction and machine learning scheme for automatic detection of ECG characteristic points. Methods: A new feature, termed as randomly selected wavelet transform (RSWT) feature, was devised to represent ECG characteristic points. A random forest classifier was adapted to infer the characteristic points position with high sensitivity and precision. Results: Compared with other state-of-the-art algorithms' testing results on QT database, our detection results of RSWT scheme showed comparable performance (similar sensitivity, precision, and detection error for each characteristic point). RSWT testing on MIT-BIH database also demonstrated promising cross-database performance. Conclusion: A novel RSWT feature and a new detection scheme was fabricated for ECG characteristic points. The RSWT demonstrated a robust and trustworthy feature for representing ECG morphologies. Significance: With the effectiveness of the proposed RSWT feature we presented a novel machine learning based scheme to automatically detect all types of ECG characteristic points at a time. Furthermore, it showed that our algorithm achieved better performance than other reported machine learning based methods.
ER -