An enhanced method for real-time modelling of cardiac related biosignals using Gaussian mixtures

References

• Pflugradt M, Hofmeyr N, Orglmeister R. Multi-channel biosignal processing on an OMAP 3530 system. 2012 5th European DSP Education and Research Conference (EDER); 2012.
   Google Scholar
• Jung D, Kim K, Kim G, et al. Biosignal monitoring system for mobile telemedicine. Proceedings of 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, 2005. HEALTHCOM; 2005.
   Google Scholar
• Zhu S, Tan K, Zhang X, et al. MICROSFT: a mixed approach for heart rate monitoring during intensive physical exercise using wrist-type PPG Signals. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015.
   Google Scholar
• Lin W, Ma H. A physiological information extraction method based on wearable PPG sensors with motion artifact removal. 2016 IEEE International Conference on Communications (ICC); 2016.
   Google Scholar
• Kim H, Van Hoof C, Yazicioglu R. A mixed signal ECG processing platform with an adaptive sampling ADC for portable monitoring applications. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011.
   Google Scholar
• Yaghoobi M, Nam S, Gribonval R, et al. Constrained overcomplete analysis operator learning for cosparse signal modelling. IEEE Trans Signal Process. 2013;61:2341–2355.
   Web of Science ®Google Scholar
• Justice HDH. Modeling of blood flow velocity and pressure signals using the multipulse method. 3rd IEEE Signal Processing Education Workshop. 2004 IEEE 11th Digital Signal Processing Workshop; 2015.
   Google Scholar
• Abdoli M, Ahmadian A, Karimifard S, et al. An efficient piecewise modeling of ECG signals based on critical samples using Hermitian basis functions. IFMBE Proceedings; 2009. p. 1188–1191.
   Google Scholar
• Roonizi E, Sameni R. Morphological modeling of cardiac signals based on signal decomposition. Comput Biol Med. 2013;43:1453–1461.
   PubMed Web of Science ®Google Scholar
• Kheirati Roonizi E, Sassi R. A signal decomposition model-based Bayesian framework for ECG components separation. IEEE Trans Signal Process. 2016;64:665–674.
   Web of Science ®Google Scholar
• Spainhour J, Janech M, Schwacke J, et al. The application of Gaussian mixture models for signal quantification in MALDI-ToF Mass Spectrometry of peptides. PLoS ONE. 2014;9:e111016.
   PubMed Web of Science ®Google Scholar
• Sameni R, Shamsollahi MB, Jutten C, et al. Filtering noisy ECG signals using the extended Kalman filter based on a modified dynamic ECG model. Proceedings of the 32nd Annual International Conference of Computers in Cardiology; Sep; 2005. p. 1017–1020.
   Google Scholar
• Ahmadian A, Karimifard S, Sadoughi H, et al. An efficient piecewise modeling of ECG signals based on hermitian basis functions. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007; Lyon. p. 3180–3183.
   Google Scholar
• McSharry PE, Clifford G, Tarassenko DL. A dynamic model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng. 2003;50:289–294.
   PubMed Web of Science ®Google Scholar
• Billah MS, Mahmud TB, Snigdha FS, et al. A novel method to model ECG beats using Gaussian functions. 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI); 2011.
   Google Scholar
• Goldberger A, Amaral L, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:e215–e220.
   PubMed Web of Science ®Google Scholar
• MIMIC-III Waveform Database; [Internet]; [cited 2017 Mar 24]. Available from: http://www.physionet.org/cgi-bin/atm/ATM
   Google Scholar
• Mit-bih arrhythmia database; [Internet]; [cited 2017 Sep 01]. Available from: http://www.physionet.org/cgi-bin/atm/ATM
   Google Scholar
• Blanco-Velasco M, Cruz-Roldán F, Godino-Llorente J, et al. On the use of PRD and CR parameters for ECG compression. Med Eng Phys. 2005;27:798–802.
   PubMed Web of Science ®Google Scholar
• Alshamali A. Wavelet based ECG compression with adaptive thresholding and efficient coding. J Med Eng Technol. 2010;34:335–339.
   PubMedGoogle Scholar
• Lu Z, Kim DY, Pearlman WA. Wavelet compression of ECG signals by the SET partitioning in hierarchal trees algorithm. IEEE Trans Biomed Eng. 2000;47:849–856.
   PubMed Web of Science ®Google Scholar
• Alam S, Gupta R. Zonal complexity based measure for lossy compression of photoplethysmogram using delta encoding. 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES); 2015.
   Google Scholar
• Al-Smadi A. Combined coding and wavelet transform for ECG compression. J Med Eng Technol. 2001;25:212–216.
   PubMed Web of Science ®Google Scholar
• Mitra M, Bera J, Gupta R. Electrocardiogram compression technique for global system of mobile-based offline telecardiology application for rural clinics in India. IET Sci Meas Technol. 2012;6:412.
   Web of Science ®Google Scholar
• Blanco-Velasco M, Cruz-Roldán F, Moreno-Martínez E, et al. Embedded filter bank-based algorithm for ECG compression. Signal Process. 2008;88:1402–1412.
   Web of Science ®Google Scholar
• Mukhopadhyay S, Mitra M, Mitra S. An ECG data compression method via standard deviation and ASCII character encoding. 2011 Second International Conference on IEEE Emerging Applications of Information Technology (EAIT); 2011. p. 67–70.
   Google Scholar
• Khalaj A, Naimi HM. New efficient fractal based compression method for electrocardiogram signals. Electrical and Computer Engineering, 2009. CCECE’09. Canadian Conference on IEEE; 2009. p. 983–986.
   Google Scholar