Analysis of Noise Level Effects on Signal-Averaged Electrocardiograms
Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.
[1] M. B. Simson, "Use of signals in the terminal QRS complex to identify
patients with ventricular tachycardia after myocardial infarction",
Circulation, vol. 64, pp. 235-242, 1981.
[2] M. E. Cain, H. D. Ambos, F. X. Witkowski, B.E. Sobel, "Fast-Fourier
transform danalysis of signal-averaged electrocardiogram for
identification of patients prone to sustained ventricular tachycardia",
Circulation, vol. 69, pp. 711-720, 1984.
[3] J. R. Jarrett, N. C. Flowers, "Signal-averaged electrocardiography:
history, techniques, and clinical applications", Clin. Cardiol., vol. 14, pp.
984-994, 1991.
[4] C. C. Lin, C. M. Chen, I. F. Yang, T. F. Yang, "Automatic optimal order
selection of parametric modeling for the evaluation of abnormal
intra-QRS signals in signal-averaged electrocardiograms", Med. Biol.
Eng. Comput., vol. 43, pp. 218-224, 2005.
[5] C. C. Lin, "Enhancement of accuracy and reproducibility of parametric
modeling for estimating abnormal intra-QRS potentials in
signal-averaged electrocardiograms", Med. Eng. Phys., vol. 30, pp.
834-842, 2008.
[6] C. C. Lin, "Analysis of Unpredictable Intra-QRS Potentials in
Signal-Averaged Electrocardiograms Using an Autoregressive Moving
Average Prediction Model", Med. Eng. Phys., vol. 32, pp. 136-144, 2010.
[7] O. Rompelman, H. H. Ros, "Coherent averaging technique: a tutorial
review. Part 1: Noise reduction and the equivalent filter", J. Biomed. Eng.,
vol. 8, pp. 24-29, 1986.
[8] O. Rompelman, H. H. Ros, "Coherent averaging technique: a tutorial
review. Part 2: trigger jitter, overlapping responses and non-periodic
stimulation", J. Biomed. Eng., vol. 8, pp. 30-35, 1986.
[9] G. Breithardt, M. E. Cain, N. El-Sherif, N. C. Flowers, V. Hombach, M.
Janse, M. B. Simson, G. Steinbeck, "Standards for analysis of ventricular
late potentials using high-resolution or signal-averaged
electrocardiography: a statement by a task force committee of the
European Society of Cardiology, the American Heart Association, and the
American College of Cardiology", J. Am. Coll. Cardiol., vol. 17, pp.
999-1006, 1991.
[10] M. E. Cain, J. L. Anderson, M. F. Arnsdorf, J. W. Mason, M. M.
Scheinman, A. L. Waldo, "Signal-Averaged Electrocardiography", J. Am.
Coll. Cardiol., vol. 27, pp. 238-249, 1996.
[11] H. Tatsumi, M. Takagi, E. Nakagawa, H. Yamashita, M. Yoshiyama,
"Risk stratification in patients with Brugada syndrome: analysis of daily
fluctuations in 12-lead electrocardiogram (ECG) and signal-averaged
electrocardiogram (SAECG)", J. Cardiovasc. Electrophysiol., vol. 17(7),
pp. 705-711, 2006.
[12] A. L. Ribeiro, P. S. Cavalvanti, F. Lombardi, C. Mdo Nunes, M. V.
Barros, M. O. Rocha, "Prognostic value of signal-averaged
electrocardiogram in Chagas disease", J. Cardiovasc. Electrophysiol.,
vol. 19(5), pp. 502-509, 2008.
[13] H. Isma'eel, W. Shamseddeen, A. Taher, W. Gharzuddine, A. Dimassi, S.
Alam, L. Masri, M. Khoury, "Ventricular late potentials among
thalassemia patients", Int. J. Cardiol., vol. 132(3), pp. 453-455, 2009.
[14] H. Ichikawa, Y. Nagake, H. Makino, "Signal averaged
electrocardiography (SAECG) in patients on hemodialysis", J. Med., vol.
28, pp. 229-243, 1997.
[15] M. A. Morales, C. Gremigni, P. Dattolo, et al., "Signal-averaged ECG
abnormalities in haemodialysis patients. Role of dialysis", Nephrol. Dial.
Transplant, vol. 13, pp. 668-673, 1988.
[16] I. Girgis, G. Contreras, S. Chakko, et al., "Effect of hemodialysis on the
signal-averaged electrocardiogram", Am. J. Kidney Dis., vol. 34, pp.
1105-1113, 1999.
[17] A. Yildiz, V. Akkaya, S. Sahin, T. Tukek, M. Besler, S. Bozfakioglu, "QT
dispersion and signal-averaged electrocardiogram in hemodialysis and
CAPD patients", Perit. Dial. Int., vol. 21, pp. 186-192, 2001.
[18] R. N. Foley, P. S. Pafrey, M. J. Sarnak, "Epidemiology of cardiovascular
disease in chronic renal disease", J. Am. Soc. Nephrol., vol. 9, pp.
S16-S23, 1998.
[19] J. S. Steinberg, J. T. Jr Bigger, "Importance of the endpoint of noise
reduction in analysis of the signal-averaged electrocardiogram", Am. J.
Cardiol., vol. 63, pp. 556-560, 1989.
[20] E. H. Christiansen, L. Frost, H. Molgaard, T. T. Nielsen, A. K. Pedersen,
"Noise in the signal-averaged electrocardiogram and accuracy for
identification of patients with sustained monomorphic ventricular
tachycardia after myocardial infarction", Eur. Heart J., vol. 17, pp.
911-916, 1996.
[21] E.H. Christiansen, L. Frost, H. Molgaard, T. T. Nielsen, A. K. Pedersen,
"Effect of residual noise level on reproducibility of the signal-averaged
ECG", J. Electrocardiol., vol. 29, pp. 235-241, 1996.
[22] T. N. Maounis, E. Kyrozi, I. Chiladakis, V. P. Vassilikos, A. S. Manolis,
D. V. Cokkinos, "Comparison of signal-averaged electrocardiograms
with different levels of noise: time-domain, frequency-domain, and
spectrotemporal analysis", Pacing Clin. Electrophysiol., vol. 20, pp.
671-682, 1997.
[23] P. Lander, E. J. Berbari, C. V. Rajagopalan, P. Vatterott, R. Lazzara,
"Critical analysis of the signal-averaged electrocardiogram, Improved
identification of late potentials", Circulation, vol. 87, pp. 105-117, 1993.
[24] P. Lander, E. J. Berbari, R. Lazzara, "Optimal filtering and quality control
of the signal-averaged ECG. High-fidelity 1-minute recordings",
Circulation, vol. 91, pp. 1495-1505, 1995.
[25] J. J. Goldberger, S. Challapalli, M. Waligora, A. H. Kadish, D. A.
Johnson, M. W. Ahmed, S. Inbar, "Uncertainty principle of
signal-averaged electrocardiography", Circulation, vol. 101, pp.
2909-2915, 2000.
[1] M. B. Simson, "Use of signals in the terminal QRS complex to identify
patients with ventricular tachycardia after myocardial infarction",
Circulation, vol. 64, pp. 235-242, 1981.
[2] M. E. Cain, H. D. Ambos, F. X. Witkowski, B.E. Sobel, "Fast-Fourier
transform danalysis of signal-averaged electrocardiogram for
identification of patients prone to sustained ventricular tachycardia",
Circulation, vol. 69, pp. 711-720, 1984.
[3] J. R. Jarrett, N. C. Flowers, "Signal-averaged electrocardiography:
history, techniques, and clinical applications", Clin. Cardiol., vol. 14, pp.
984-994, 1991.
[4] C. C. Lin, C. M. Chen, I. F. Yang, T. F. Yang, "Automatic optimal order
selection of parametric modeling for the evaluation of abnormal
intra-QRS signals in signal-averaged electrocardiograms", Med. Biol.
Eng. Comput., vol. 43, pp. 218-224, 2005.
[5] C. C. Lin, "Enhancement of accuracy and reproducibility of parametric
modeling for estimating abnormal intra-QRS potentials in
signal-averaged electrocardiograms", Med. Eng. Phys., vol. 30, pp.
834-842, 2008.
[6] C. C. Lin, "Analysis of Unpredictable Intra-QRS Potentials in
Signal-Averaged Electrocardiograms Using an Autoregressive Moving
Average Prediction Model", Med. Eng. Phys., vol. 32, pp. 136-144, 2010.
[7] O. Rompelman, H. H. Ros, "Coherent averaging technique: a tutorial
review. Part 1: Noise reduction and the equivalent filter", J. Biomed. Eng.,
vol. 8, pp. 24-29, 1986.
[8] O. Rompelman, H. H. Ros, "Coherent averaging technique: a tutorial
review. Part 2: trigger jitter, overlapping responses and non-periodic
stimulation", J. Biomed. Eng., vol. 8, pp. 30-35, 1986.
[9] G. Breithardt, M. E. Cain, N. El-Sherif, N. C. Flowers, V. Hombach, M.
Janse, M. B. Simson, G. Steinbeck, "Standards for analysis of ventricular
late potentials using high-resolution or signal-averaged
electrocardiography: a statement by a task force committee of the
European Society of Cardiology, the American Heart Association, and the
American College of Cardiology", J. Am. Coll. Cardiol., vol. 17, pp.
999-1006, 1991.
[10] M. E. Cain, J. L. Anderson, M. F. Arnsdorf, J. W. Mason, M. M.
Scheinman, A. L. Waldo, "Signal-Averaged Electrocardiography", J. Am.
Coll. Cardiol., vol. 27, pp. 238-249, 1996.
[11] H. Tatsumi, M. Takagi, E. Nakagawa, H. Yamashita, M. Yoshiyama,
"Risk stratification in patients with Brugada syndrome: analysis of daily
fluctuations in 12-lead electrocardiogram (ECG) and signal-averaged
electrocardiogram (SAECG)", J. Cardiovasc. Electrophysiol., vol. 17(7),
pp. 705-711, 2006.
[12] A. L. Ribeiro, P. S. Cavalvanti, F. Lombardi, C. Mdo Nunes, M. V.
Barros, M. O. Rocha, "Prognostic value of signal-averaged
electrocardiogram in Chagas disease", J. Cardiovasc. Electrophysiol.,
vol. 19(5), pp. 502-509, 2008.
[13] H. Isma'eel, W. Shamseddeen, A. Taher, W. Gharzuddine, A. Dimassi, S.
Alam, L. Masri, M. Khoury, "Ventricular late potentials among
thalassemia patients", Int. J. Cardiol., vol. 132(3), pp. 453-455, 2009.
[14] H. Ichikawa, Y. Nagake, H. Makino, "Signal averaged
electrocardiography (SAECG) in patients on hemodialysis", J. Med., vol.
28, pp. 229-243, 1997.
[15] M. A. Morales, C. Gremigni, P. Dattolo, et al., "Signal-averaged ECG
abnormalities in haemodialysis patients. Role of dialysis", Nephrol. Dial.
Transplant, vol. 13, pp. 668-673, 1988.
[16] I. Girgis, G. Contreras, S. Chakko, et al., "Effect of hemodialysis on the
signal-averaged electrocardiogram", Am. J. Kidney Dis., vol. 34, pp.
1105-1113, 1999.
[17] A. Yildiz, V. Akkaya, S. Sahin, T. Tukek, M. Besler, S. Bozfakioglu, "QT
dispersion and signal-averaged electrocardiogram in hemodialysis and
CAPD patients", Perit. Dial. Int., vol. 21, pp. 186-192, 2001.
[18] R. N. Foley, P. S. Pafrey, M. J. Sarnak, "Epidemiology of cardiovascular
disease in chronic renal disease", J. Am. Soc. Nephrol., vol. 9, pp.
S16-S23, 1998.
[19] J. S. Steinberg, J. T. Jr Bigger, "Importance of the endpoint of noise
reduction in analysis of the signal-averaged electrocardiogram", Am. J.
Cardiol., vol. 63, pp. 556-560, 1989.
[20] E. H. Christiansen, L. Frost, H. Molgaard, T. T. Nielsen, A. K. Pedersen,
"Noise in the signal-averaged electrocardiogram and accuracy for
identification of patients with sustained monomorphic ventricular
tachycardia after myocardial infarction", Eur. Heart J., vol. 17, pp.
911-916, 1996.
[21] E.H. Christiansen, L. Frost, H. Molgaard, T. T. Nielsen, A. K. Pedersen,
"Effect of residual noise level on reproducibility of the signal-averaged
ECG", J. Electrocardiol., vol. 29, pp. 235-241, 1996.
[22] T. N. Maounis, E. Kyrozi, I. Chiladakis, V. P. Vassilikos, A. S. Manolis,
D. V. Cokkinos, "Comparison of signal-averaged electrocardiograms
with different levels of noise: time-domain, frequency-domain, and
spectrotemporal analysis", Pacing Clin. Electrophysiol., vol. 20, pp.
671-682, 1997.
[23] P. Lander, E. J. Berbari, C. V. Rajagopalan, P. Vatterott, R. Lazzara,
"Critical analysis of the signal-averaged electrocardiogram, Improved
identification of late potentials", Circulation, vol. 87, pp. 105-117, 1993.
[24] P. Lander, E. J. Berbari, R. Lazzara, "Optimal filtering and quality control
of the signal-averaged ECG. High-fidelity 1-minute recordings",
Circulation, vol. 91, pp. 1495-1505, 1995.
[25] J. J. Goldberger, S. Challapalli, M. Waligora, A. H. Kadish, D. A.
Johnson, M. W. Ahmed, S. Inbar, "Uncertainty principle of
signal-averaged electrocardiography", Circulation, vol. 101, pp.
2909-2915, 2000.
@article{"International Journal of Medical, Medicine and Health Sciences:57082", author = "Chun-Cheng Lin", title = "Analysis of Noise Level Effects on Signal-Averaged Electrocardiograms", abstract = "Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.", keywords = "Signal-averaged electrocardiogram, Ventricular latepotentials, Chronic renal failure, Noise level effects.", volume = "4", number = "10", pages = "524-6", }