2014, Number 1
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Rev Mex Ing Biomed 2014; 35 (1)
Amplitude Modulation Approach for Real-Time Algorithms of ECG-Derived Respiration
Vargas LJL, Mayr W, Cortés RJA
Language: English
References: 26
Page: 53-62
PDF size: 826.61 Kb.
ABSTRACT
This work presents the development of an ECG-Derived Respiration
(EDR) methodology based on the amplitude modulation approach. It
allows to redefine actual methodologies in order to obtain a continuous
EDR signals with high correlations and small delay between EDR
and respiration activity. Two algorithms are implemented: one of
them using the amplitude modulation of the R-peak (EDRAM) and
another one applying a band-pass filter in the bandwidth of respiration.
Unlike other techniques in literature, conventional low order filters are
applied without sacrifice of correlation factor (0.76 and 0.67) and a
minimum delay of 0.27s (with EDRAM) in a 6s cycle. A robustness
test was performed, and it shows a noise tolerance of up to 20% of the
maximum value before its correlation factor drops considerably. The
application into a wearable sensor was successfully implemented. The
two methodologies proposed show advantages like real-time processing
and robustness under certain noises. The proposed AM perspective
supports the use of both algorithms for typical applications with high
efficiency, low computational cost and ease of implementation. These
characteristics result on a technique that facilitates the development of
wearable systems, and to increase the information of actual databases.
REFERENCES
Travaglini A, Lamberti C, DeBie J, Ferri M. Respiratory signal derived from eight-lead ECG. Comput. Cardiol. 1998. Cleveland, USA: IEEE; 1998. p. 65-8.
Varanini M, Emdin M, Allegri F, Raciti M, Conforti F, Macerata A, et al. Adaptive filtering of ECG signal for deriving respiratory activity. Comput. Cardiol. 1990. Chicago, USA: IEEE Comput. Soc. Press; 1990. p. 621-4.
Ding S, Zhu X, Chen W,Wei D. Derivation of respiratory signal from single-channel ECGs based on Source Statistics. Int. J. Bioelectromagn. 2004;6.
Madhav KV, Ram MR, Krishna EH, Komalla NR, Reddy KA. Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition. Instrum. Meas. Technol. Conf. 2011. Binjiang, China: IEEE; 2011. p. 1-4.
Moody GB, Mark RG, Zoccola A, Mantero S. Derivation of Respiratory Signals from Multi-lead ECGs. Comput. Cardiol. 1985. Washington, DC: IEEE Computer Society Press; 1985. p. 113-6.
Arunachalam SP, Brown LF. Real-time estimation of the ECG-derived respiration (EDR) signal using a new algorithm for baseline wander noise removal. Eng. Med. Biol. Soc. 2009. EMBC 2009. Annu. Int. Conf. IEEE. Minneapolis, USA: IEEE; 2009. p. 5681-4.
O’Brien C, Heneghan C. A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogram. Comput. Biol. Med. 2007;37:305-14.
Lazaro J, Alcaine A, Gil E, Laguna P, Bailon R. Electrocardiogram derived respiration from QRS slopes. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013;2013:3913- 6.
Ramya K, Rajkumar K. Respiration Rate Diagnosis Using Single Lead ECG in Real Time. Glob. J. Med. Res. 2013;13:7-12.
Langley P, Bowers EJ, Murray A. Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features: application to ECG-derived respiration. IEEE Trans. Biomed. Eng. 2010;57:821-9.
Zhao L, Reisman S, Findley T. Respiration derived from the electrocardiogram during heart rate variability studies. Proc. 16th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Baltimore, USA: IEEE; 1994. p. 123- 4.
Widjaja D, Taelman J, Vandeput S. ECG-derived respiration: Comparison and new measures for respiratory variability. Comput. Cardiol. 2010. Belfast, Northern Ireland: IEEE; 2010. p. 149-52.
Mazzanti B, Lamberti C, de Bie J. Validation of an ECG-derived respiration monitoring method. Comput. Cardiol. 2003. Thessaloniki, Greece: IEEE; 2003. p. 613-6.
Pflugradt M, Mann S, Feller V, Orglmeister R. On-line Learning Algorithms for extracting respiratory activity from Single Lead ECGs based on Principal Component Analysis. Biomed. Tech. (Berl). 2012;57:352-4.
Boyle J, Bidargaddi N, Sarela A, Karunanithi M. Automatic detection of respiration rate from ambulatory singlelead ECG. IEEE Trans. Inf. Technol. Biomed. 2009;13:890-6.
Guyton AC, Hall JE. Tratado de Fisiología Médica. 12a ed. ELSEVIER, editor. 2011.
Goldberger A, Amaral L, Glass L. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation. 2000;
Babaeizadeh S, Zhou SH, Pittman SD, White DP. Electrocardiogram-derived respiration in screening of sleep-disordered breathing. J. Electrocardiol. 2011;44:700-6.
Moussavi ZK, Leopando MT, Pasterkamp H, Rempel G. Computerised acoustical respiratory phase detection without airflow measurement. Med. Biol. Eng. Compu.. 2000;38:198-203.
Prutchi D, Norris M. Design and Development of Medical Electronic Instrumentation. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2005.
Company-Bosch E, Hartmann E. ECG Front-End Design is Simplified with MicroConverter. Analog Dialogue. 2003;37:1-5.
Godse AP, Bakshi UA. Communication Engineering. Technical Publications; 2009.
Iyengar N, Peng CK, Morin R, Goldberger a L, Lipsitz L a. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am. J. Physiol. 1996;271:R1078-84.
Pereda E, Quiroga RQ, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 2005;77:1-37.
Anand S, Sanjay H, Suraj K, Krishnaswamy U. ECG-Derived respiration as a Screening Tool for OSA. Indian J. Sleep Med. 2012;7:94.
Dobrev D, Neycheva T, Mudrov N. Simple two-electrode biosignal amplifier. Med. Biol. Eng. Comput. 2005;43:725-30.