Wavelet Sub-bands Features-based ECG Signal Quality Assessment Scheme for Computer-aided Monitoring System

References

• S. Kaplan Berkaya, A. K. Uysal, E. Sora Gunal, S. Ergin, S. Gunal, and M. B. Gulmezoglu, “A survey on ECG analysis,” Biomed. Signal. Process. Control., Vol. 43, pp. 216–35, 2018. DOI:10.1016/j.bspc.2018.03.003
   Web of Science ®Google Scholar
• M. Rakshit, and S. Das, “Hybrid approach for ECG signal enhancement using dictionary learning-based sparse representation,” IET Sci. Meas. Technol., Vol. 13, no. 3, pp. 381–91, 2019. DOI:10.1049/iet-smt.2018.5060
   Web of Science ®Google Scholar
• U. Satija, B. Ramkumar, and M. Sabarimalai Manikandan, “A Review of signal processing techniques for Electrocardiogram signal quality assessment,” IEEE Rev. Biomed. Eng., Vol. 11, pp. 36–52, 2018. DOI:10.1109/RBME.2018.2810957
   PubMedGoogle Scholar
• Q. Li, C. Rajagopalan, and G. D. Clifford, “A machine learning approach to multi-level ECG signal quality classification,” Comput. Methods Programs Biomed., Vol. 117, no. 3, pp. 435–47, 2014. DOI:10.1016/j.cmpb.2014.09.002
   PubMed Web of Science ®Google Scholar
• C. Orphanidou, T. Bonnici, P. Charlton, D. Clifton, D. Vallance, and L. Tarassenko, “Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring,” IEEE J. Biomed. Heal. Informatics, Vol. 19, no. 3, pp. 832–8, 2015. DOI:10.1109/JBHI.2014.2338351
   PubMed Web of Science ®Google Scholar
• D. Hayn, B. Jammerbund, and G. Schreier, “QRS detection based ECG quality assessment,” Physiol. Meas., Vol. 33, no. 9, pp. 1449–61, 2012. DOI:10.1088/0967-3334/33/9/1449
   PubMed Web of Science ®Google Scholar
• P. X. Quesnel, A. D. C. Chan, and H. Yang. “Real-time biosignal quality analysis of ambulatory ECG for detection of myocardial ischemia,” in MeMeA 2013 – IEEE International Symposium on Medical Measurements and Applications, Proceedings, Gatineau, QC, Canada, 2013, pp. 1–5.
   Google Scholar
• J. Lee, D. D. McManus, S. Merchant, and K. H. Chon, “Automatic motion and noise artifact detection in holter ECG data using empirical mode decomposition and statistical approaches,” IEEE Trans. Biomed. Eng., Vol. 59, no. 6, pp. 1499–506, 2012. DOI:10.1109/TBME.2011.2175729
   PubMed Web of Science ®Google Scholar
• U. Satija, B. Ramkumar, and S. M. Manikandan, “Real-Time signal quality-aware ECG telemetry system for IoT-based health care monitoring,” IEEE Internet Things J., Vol. 4, no. 3, pp. 815–23, 2017. DOI:10.1109/JIOT.2017.2670022
   Web of Science ®Google Scholar
• C. Liu, P. Li, L. Zhao, F. Liu, and R. Wang, “Real-time signal quality assessment for ECGs collected using mobile phones,” Comput. Cardiol., Vol. 38, pp. 357–60, 2011.
   Google Scholar
• U. Satija, B. Ramkumar, and M. S. Manikandan, “An automated ECG signal quality assessment method for unsupervised diagnostic systems,” Biocybern. Biomed. Eng., Vol. 38, no. 1, pp. 54–70, 2018. DOI:10.1016/j.bbe.2017.10.002
   Web of Science ®Google Scholar
• U. Satija, B. Ramkumar, and M. S. Manikandan, “Automated ECG noise detection and classification system for unsupervised healthcare monitoring,” IEEE J. Biomed. Heal. Informatics, Vol. 22, no. 3, pp. 722–32, 2018. DOI:10.1109/JBHI.2017.2686436
   PubMed Web of Science ®Google Scholar
• 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,” Comput. Cardiol., Vol. 38, pp. 285–8, 2011. https://www.cinc.org/archives/2011/pdf/0285.pdf
   Google Scholar
• C. Orphanidou, and I. Drobnjak, “Quality assessment of ambulatory ECG using wavelet entropy of the HRV signal,” IEEE J. Biomed. Heal. Informatics, Vol. 21, no. 5, pp. 1216–23, 2017. DOI:10.1109/JBHI.2016.2615316
   PubMed Web of Science ®Google Scholar
• Y. Zhang, S. Wei, L. Zhang, and C. Liu, “Comparing the performance of random forest, SVM and their variants for ECG quality assessment combined with nonlinear features,” J. Med. Biol. Eng., Vol. 39, no. 3, pp. 381–92, 2019. DOI:10.1007/s40846-018-0411-0
   Web of Science ®Google Scholar
• Q. Li, R. G. Mark, and G. D. Clifford, “Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter,” Physiol. Meas., Vol. 29, no. 1, pp. 15–32, 2008. DOI:10.1088/0967-3334/29/1/002
   PubMed Web of Science ®Google Scholar
• I. Moody, and G. B. and Silva. “Physionet/computing in cardiology challenge 2011, July 2011,” http://physionet.org/challenge, 2011.
   Google Scholar
• G. B. Moody, and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Eng. Med. Biol. Mag, Vol. 20, no. 3, pp. 45–50, 2001. DOI:10.1109/51.932724
   PubMed Web of Science ®Google Scholar
• S. Mallat, A Wavelet Tour of Signal Processing. Ukraine: Elsevier Science, 1999, 2nd edition. https://doi.org/10.1016/B978-0-12-466606-1.X5000-4
   Google Scholar
• P. S. Addison, J. Walker, and R. C. Guido, “Time–frequency analysis of biosignals,” IEEE Eng. Med. Biol. Mag, Vol. 28, no. 5, pp. 14–29, 2009. DOI:10.1109/MEMB.2009.934244
   PubMedGoogle Scholar
• M. Rakshit, and S. Das, “An efficient wavelet-based automated R-peaks detection method using hilbert transform,” Biocybern. Biomed. Eng., Vol. 37, no. 3, pp. 566–77, 2017. DOI:10.1016/j.bbe.2017.02.002
   Web of Science ®Google Scholar
• S. Ari, M. K. Das, and A. Chacko, “ECG signal enhancement using S-transform,” Comput. Biol. Med., Vol. 43, no. 6, pp. 649–60, 2013. DOI:10.1016/j.compbiomed.2013.02.015
   PubMed Web of Science ®Google Scholar
• Y. Zhang, S. Wei, H. Liu, L. Zhao, and C. Liu, “A novel encoding Lempel-Ziv complexity algorithm for quantifying the irregularity of physiological time series,” Comput. Methods Programs Biomed., Vol. 133, pp. 7–15, 2016. DOI:10.1016/j.cmpb.2016.05.010
   PubMed Web of Science ®Google Scholar
• M. Riedl, A. Müller, and N. Wessel, “Practical considerations of permutation entropy: A tutorial review,” Eur. Phys. J.: Spec. Top., Vol. 222, no. 2, pp. 249–62, 2013. DOI:10.1140/epjst/e2013-01862-7
   Web of Science ®Google Scholar
• G. Manis, “Fast computation of approximate entropy,” Comput. Methods Programs Biomed., Vol. 91, no. 1, pp. 48–54, 2008. DOI:10.1016/j.cmpb.2008.02.008
   PubMed Web of Science ®Google Scholar
• G. Pan, Z. Xin, S. Shi, and D. Jin, “Arrhythmia classification based on wavelet transformation and random forests,” Multimed. Tools Appl., Vol. 77, no. 17, pp. 21905–22, 2018. DOI:10.1007/s11042-017-5225-5
   Web of Science ®Google Scholar
• J. Xia, P. Ghamisi, N. Yokoya, and A. Iwasaki, “Random forest ensembles and extended multiextinction profiles for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens., Vol. 56, no. 1, pp. 202–16, 2018. DOI:10.1109/TGRS.2017.2744662
   Web of Science ®Google Scholar
• T. Shaikhina, D. Lowe, S. Daga, D. Briggs, R. Higgins, and N. Khovanova, “Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation,” Biomed. Signal Process. Control, Vol. 52, pp. 456–62, 2019. DOI:10.1016/j.bspc.2017.01.012
   Web of Science ®Google Scholar
• G. B. Moody, W. K. Muldrow, and R. G. Mark, “Noise stress test for arrhythmia detectors,” Comput. Cardiol., Vol. 11, 381–4, 1984. DOI:10.13026/C2HS3T
   Google Scholar
• M. Merah, T. A. Abdelmalik, and B. H. Larbi, “R-peaks detection based on stationary wavelet transform,” Comput. Methods Programs Biomed., Vol. 121, no. 3, pp. 149–60, 2015. DOI:10.1016/j.cmpb.2015.06.003
   PubMed Web of Science ®Google Scholar
• M. A. Kabir, and C. Shahnaz, “Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains,” Biomed. Signal Process. Control, Vol. 7, no. 5, pp. 481–9, 2012. DOI:10.1016/j.bspc.2011.11.003
   Web of Science ®Google Scholar