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
Objektif: Pengesanan titik ciri Elektrokardiogram (ECG) boleh memberikan maklumat diagnostik kritikal tentang penyakit jantung. Kami mencadangkan skim pengekstrakan ciri baru dan pembelajaran mesin untuk pengesanan automatik titik ciri ECG. Kaedah: Satu ciri baharu, yang disebut sebagai ciri transformasi wavelet (RSWT) yang dipilih secara rawak, telah direka untuk mewakili titik ciri ECG. Pengelas hutan rawak telah disesuaikan untuk membuat kesimpulan kedudukan titik ciri dengan kepekaan dan ketepatan yang tinggi. Keputusan: Berbanding dengan keputusan ujian algoritma terkini yang lain pada pangkalan data QT, hasil pengesanan skema RSWT kami menunjukkan prestasi yang setanding (kepekaan, ketepatan dan ralat pengesanan yang serupa untuk setiap titik ciri). Ujian RSWT pada pangkalan data MIT-BIH juga menunjukkan prestasi merentas pangkalan data yang menjanjikan. Kesimpulan: Ciri RSWT novel dan skema pengesanan baharu telah direka untuk titik ciri ECG. RSWT menunjukkan ciri yang teguh dan boleh dipercayai untuk mewakili morfologi ECG. Kepentingan: Dengan keberkesanan ciri RSWT yang dicadangkan, kami mempersembahkan skim berasaskan pembelajaran mesin baru untuk mengesan semua jenis titik ciri ECG secara automatik pada satu masa. Tambahan pula, ia menunjukkan bahawa algoritma kami mencapai prestasi yang lebih baik daripada kaedah berasaskan pembelajaran mesin lain yang dilaporkan.
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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Salinan
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
Salinan
@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},}
Salinan
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 -