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An event detection algorithm in ECG with 60Hz interference and baseline wandering

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Woong HuhAuthors Info & Claims

ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

Pages 713 - 716

https://doi.org/10.1145/1655925.1656054

Published: 24 November 2009 Publication History

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Abstract

In this paper, a morphology based, time domain algorithm to detect QRS complexes in unfiltered ECG contaminated by 60Hz interference and baseline wandering is introduced. The method uses two slope trace waves which are the delayed version of an original signal with averaged slopes within a certain time interval. After the two slope trace waves are determined and used to remove 60Hz interference, another slope trace wave is followed to determine the epoch of QRS complexes by the help of amplitude and interval thresholds. The algorithm has been applied to ECG before preprocessing to show its practical applicability and efficiency. It is found that the method can detect the QRS complex appropriately in presence of 60Hz interference and baseline wandering.

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J. Kim, "A 60Hz interference reduction using slope trace waves," Proceedings of 2009 International Conference on Convergence and Hybrid Information Technology (ICHIT 2009), Daejeon, Korea, 2009.

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J. Kim, M. Kim, I. Won, S. Yang, K. Lee, and W. Huh, "An ECG signal processing algorithm based on removal of wave deflections in time domain," IEEE EMBS 31^st annual international conference, Minneapolis, USA, September 2009.

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Cited By

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Farrokhi AFarahbakhsh RRezazadeh JMinerva R(2021)Application of Internet of Things and artificial intelligence for smart fitness: A surveyComputer Networks10.1016/j.comnet.2021.107859189(107859)Online publication date: Apr-2021
https://doi.org/10.1016/j.comnet.2021.107859
Lee C(2017)Movement detection and analysis of resistance exercises for smart fitness platform2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN.2017.7993818(410-415)Online publication date: Jul-2017
https://doi.org/10.1109/ICUFN.2017.7993818

Index Terms

An event detection algorithm in ECG with 60Hz interference and baseline wandering
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

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ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

November 2009

1479 pages

ISBN:9781605587103

DOI:10.1145/1655925

Conference Chairs:
Sungwon Sohn
ETRI, Korea
,
Kyhyun Um
Dongguk University, Korea
,
Franz I. S. Ko
Dongguk University, Korea/IBC, UK
,
Kae Dal Kwack
Hanyang University, Korea
,
Jong Hyung Lee
Gumi Electronics & Information Technology Research Institute, Korea
,
Gang Kou
University of Electronic Science and Technology of China, China
,
Kiyoshi Nakamura
Waseda University, Japan
,
ACM Fong
Nanyang Technological University, Singapore
,
Patrick Ma
The Hong Kong Polytechnic University, Hongkong
,
Ling Chen
Zhejiang University, China
,
Soonwook Hwang
KISTI, Korea
,
Kyungeun Cho
Dongguk University, Korea
,
Shigeo Kawata
Utsunomiya University, Japan

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Published: 24 November 2009

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ICIS '09: The 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

November 24 - 26, 2009

Seoul, Korea

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Farrokhi AFarahbakhsh RRezazadeh JMinerva R(2021)Application of Internet of Things and artificial intelligence for smart fitness: A surveyComputer Networks10.1016/j.comnet.2021.107859189(107859)Online publication date: Apr-2021
https://doi.org/10.1016/j.comnet.2021.107859
Lee C(2017)Movement detection and analysis of resistance exercises for smart fitness platform2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN.2017.7993818(410-415)Online publication date: Jul-2017
https://doi.org/10.1109/ICUFN.2017.7993818

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Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram

Design of a Low-Complexity Real-Time Arrhythmia Detection System

Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

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