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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Objetivo: A detecção de pontos característicos do eletrocardiograma (ECG) pode fornecer informações diagnósticas críticas sobre doenças cardíacas. Propusemos um novo esquema de extração de recursos e aprendizado de máquina para detecção automática de pontos característicos de ECG. Métodos: Um novo recurso, denominado recurso de transformada wavelet selecionada aleatoriamente (RSWT), foi desenvolvido para representar pontos característicos do ECG. Um classificador florestal aleatório foi adaptado para inferir a posição dos pontos característicos com alta sensibilidade e precisão. Resultados: Comparados com os resultados de testes de outros algoritmos de última geração no banco de dados QT, nossos resultados de detecção do esquema RSWT mostraram desempenho comparável (sensibilidade, precisão e erro de detecção semelhantes para cada ponto característico). Os testes RSWT no banco de dados MIT-BIH também demonstraram um desempenho promissor entre bancos de dados. Conclusão: Um novo recurso de RSWT e um novo esquema de detecção foram fabricados para pontos característicos do ECG. O RSWT demonstrou um recurso robusto e confiável para representar morfologias de ECG. Significância: Com a eficácia do recurso RSWT proposto, apresentamos um novo esquema baseado em aprendizado de máquina para detectar automaticamente todos os tipos de pontos característicos de ECG de cada vez. Além disso, mostrou que nosso algoritmo alcançou melhor desempenho do que outros métodos baseados em aprendizado de máquina relatados.
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},}
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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 -