Quality Assessment of 12 Lead ECG Signals based on Beat Detection Pattern
Abstract
This paper proposed a new method for assessing signal quality from 12 lead ECG signal. The proposed method can be applied to incoming ECG data stream in one pass, does not involve computatively expensive filtering and does not require large memory space, which makes it especially useful for use in mobile device based ECG signal acquisition. The proposed method is verified on PhysioNet/Computing in Cardiology Chal- lenge 2011 12 lead ECG signals database, achieving a result of 89.98 percent accuracy when tested against the training dataset, and 87.4 percent accuracy when tested against the test data set.
Keywords: ECG signal quality assessment
Full Text:
PDFReferences
P. Langley et al., ”An algorithm for assessment of quality of ECGs acquired via mobile telephones,” 2011 Computing in Cardiology, Hangzhou, 2011, pp. 281-284.
Chengyu Liu, Peng Li, Lina Zhao, Feifei Lio, Ruxiang Wang, ”Real-time Signal Quality Assessment for ECGs Collected Using Mobile Phones,” 2011 Computing in Cardiology, Hangzhou, 2011, pp. 357-360.
G. D. Clifford, D. Lopez, Q. Li and I. Rezek, ”Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments,” 2011 Computing in Cardiology, Hangzhou, 2011, pp. 285-288.
Arie C Maan, Erik W van Zwet, SumChe Man, Suzanne M M OlivieraMartens, Martin J Schalij, Cees A Swenne, ”Assessment of Signal Quality and Electrode Placement in ECGs using a Reconstruction Matrix,” 2011 Computing in Cardiology, Hangzhou, 2011, pp. 289-292.
Kai Noponen, Mari Karsikas, Suvi Tiinanen, Jukka Kortelainen, Heikki Huikuri, Tapop Seppanen, ”Electrocardiogram QUality Classification based on Robust Best Subsets Linear Prediction Error,” 2011 Computing in Cardiology, Hangzhou, 2011, pp. 353-356.
lvarez, Ral Alonso, Arturo J. Mndez Penn, and X. Antn Vila Sobrino. ”A comparison of three QRS detection algorithms over a public database.” Procedia Technology 9 (2013): 1159-1165.
Pan, Jiapu, and Willis J. Tompkins. ”A real-time QRS detection algorithm.” IEEE transactions on biomedical engineering 3 (1985): 230-236.
Hamilton, Patrick S., and Willis J. Tompkins. ”Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database.” IEEE transactions on biomedical engineering 12 (1986): 1157-1165.
Christov, Ivaylo I. ”Real time electrocardiogram QRS detection using combined adaptive threshold.” Biomedical engineering online 3.1 (2004): 28.
Bakardjian, H. ”Ventricular beat classifier using fractal number clustering.” Medical and Biological Engineering and Computing 30.5 (1992): 495-502.
Zaunseder, Sebastian, Robert Huhle, and Hagen Malberg. ”CinC challengeAssessing the usability of ECG by ensemble decision trees.” Computing in Cardiology, 2011. IEEE, 2011.
Moody, Benjamin E. ”Rule-based methods for ECG quality control.” Computing in Cardiology, 2011. IEEE, 2011.
Xia, Henian, et al. ”Computer algorithms for evaluating the quality of ECGs in real time.” Computing in Cardiology, 2011. IEEE, 2011.
Jekova, Irena, et al. ”Recognition of diagnostically useful ECG recordings: Alert for corrupted or interchanged leads.” Computing in Cardiology, 2011. IEEE, 2011.
Johannesen, Lars. ”Assessment of ECG quality on an Android platform.” Computing in Cardiology, 2011. IEEE, 2011.
Kalkstein, Nir, et al. ”Using machine learning to detect problems in ECG data collection.” Computing in Cardiology, 2011. IEEE, 2011.
Tat, Thomas Ho Chee, Chen Xiang, and Lim Eng Thiam. ”Physionet challenge 2011: improving the quality of electrocardiography data collected using real time QRS-complex and T-wave detection.” Computing in Cardiology, 2011. IEEE, 2011.
Starc, Vito. ”Could determination of equivalent dipoles from 12 lead ECG help in detection of misplaced electrodes.” Computing in Cardiology, 2011. IEEE, 2011.
Vclav Chudek, Luks Zach, Jakub Kuzilek, Jir Spilka, Lenka Lhotsk. ”Simple Scoring System for ECG Quality Assessment on Android
Platform.” Computing in Cardiology, 2011. IEEE, 2011.
Kulek, Jakub, et al. ”Data driven approach to ECG signal quality assessment using multistep SVM Classification.” Computing in Cardiology, 2011. IEEE, 2011.
Dieter Hayn, Bernhard Jammerbund, Gunter Schreier, ”ECG Quality Assessment for Patient Empowerment in mHealth Applications,” 2011 Computing in Cardiology, Hangzhou, 2011, pp. 353-356.
DOI: https://doi.org/10.12962/j25796216.v1.i2.17
Refbacks
- There are currently no refbacks.