R-Peak Detection in ECG Images using Matlab®
Pages 11 - 14
Abstract
Electrocardiography (ECG) has, for long been an important and vital measure of the heart's electrical activities. Using the information obtained from a person's ECG, doctors can assess the risk factors associated to the circulatory system of the body. Conventionally, this analysis is either done manually by doctors, or by running signal processing algorithms on a computer. In this paper, the possibility of performing such analysis using an ECG image rather than using a pre-recorded, raw ECG signal has been discussed. The discussion has been limited to the detection of R-peaks in the ECG waveform using both, Image and Signal Processing. The idea can be extended to evaluate several other parameters related to heart health, since most of them are based on ECG analysis report.
References
[1]
Felix Scholkmann *, Jens Boss and Martin Wolf, An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals, ISSN 1999-4893, Published: 21 November 2012
[2]
Ahmet Resit KAVSAOGLU, Kemal POLAT, Mehmet Recep BOZKURT, An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method, Accepted/Published Online: 30.06.2014
[3]
Dae-Geun Jang, Sangjun Park, and Minsoo Hahn, A Real-Time Pulse Peak Detection Algorithm for the Photoplethysmogram, International Journal of Electronics and Electrical Engineering Vol.2, No. 1, March, 2014
[4]
Jan Gierałtowski, Kamil Ciuchciński, Iga Grzegorczyk, Katarzyna Kośna, Mateusz Soliń;ski and Piotr Podziemski, RS slope detection algorithm for extraction of heart rate from noisy, multimodal recordings, Published 28 July 2015 © 2015 Institute of Physics and Engineering in Medicine, Physiological Measurement, Volume 36, Number 8
[5]
Generalized Feedforward Neural Network based cardiac arrhythmia classification from ECG signal data, Shivajirao M. Jadhav; Sanjay L. Nalbalwar; Ashok A. Ghatol, 2010 6th International Conference on Advanced Information Management and Service (IMS), Year: 2010, IEEE Conferences
[6]
Artificial Neural Network based cardiac arrhythmia classification using ECG signal data, S. M. Jadhav; S. L. Nalbalwar; Ashok Ghatol, 2010 International Conference on Electronics and Information Engineering, Year: 2010, Volume: 1, IEEE Conferences
[7]
A computationally efficient approach for ECG signal denoising and data compression, Shanti Chandra; Ambalika Sharma, 2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), Year:2017, IEEE Conferences
[8]
Reversible data hiding in ecg signals based on histogram shifting and thresholding, Wenchao Wu; Bin Liu; Weiming Zhang; Changwen Chen, 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech), Year: 2015, IEEE Conferences
[9]
R-peak detection using efficient technique for tachycardia detection, Mohan Debarchan Mohanty; Bibhuprasad Mohanty; Mihir N. Mohanty, 2017 2nd International Conference on Man and Machine Interfacing (MAMI), Year: 2017, IEEE Conferences
[10]
R - Peak detection using Bayesian regularization neural network, Nilesh Parihar, 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), Year: 2017, IEEE Conferences
Index Terms
- R-Peak Detection in ECG Images using Matlab®
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Published In
June 2018
261 pages
ISBN:9781450364607
DOI:10.1145/3239576
Copyright © 2018 ACM.
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In-Cooperation
- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
- Southwest Jiaotong University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 June 2018
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- Research-article
- Research
- Refereed limited
Conference
ICAIP '18
ICAIP '18: 2018 the 2nd International Conference on Advances in Image Processing
June 16 - 18, 2018
Chengdu, China
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