A continuous approach to the ECG noiseprint estimation

• Autorzy: Augustyniak, P.
• Języki publikacji: EN

Abstrakty

EN

Signal quality is a common problem in biomedical applications, as it impacts the reliability of electrocardiogram interpretation. The demand for a need for a dependable signal-to-noise measure is reinforced by widespread telemedical recordings in the home care conditions being interpreted automatically. Present techniques are based on baseline noise measurement and assumption about temporal noise stability. This paper presents an alternative approach to ECG noiseprint estimation technique based on a noise model calculated from seamless time-frequency representation. The key principle is recognition of area for possible cardiac components with use of temporarily adapted local bandwidth variability function. The part free of cardiac influence, above the local bandwidth of the ECG, represents background activities of any origin (muscle, mains interference etc.). Next, we consider the non-uniformly sampled time series in each particular scale as piecewise noise estimate and apply interpolation techniques to estimate the noiseprint in regions containing the ECG components. The algorithm was implemented and tested with use of the CSE Database records with the addition of the MIT-BIH Database noise patterns. The differences between the added and estimated noise show similar performance of baseline-based and noise model-based methods (accuracy of 0.69 dB and 0.64 dB respectively) as long as the noise level is stable.

Słowa kluczowe

EN

signal-to-noise ratio; time-frequency modeling; electrocardiogram

• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2011
• Tom: Vol. 7, no. 4
• Strony: 53--58
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wykr.

Twórcy

autor

Augustyniak, P.

• Institute of Automatics, AGH University of Science and Technology, Kraków,

Bibliografia

• 1. Moss A., Stern S.: Noninvasive Electrocardiology - clinical aspects of Holter monitoring. London: Saunders, 1996.
• 2. Akay M.: Biomedical signal processing. San Diego: Academic Press, 1994.
• 3. Akay M. (ed.): Wavelets in Biomedical Signal Processing. New York: IEEE Press, 1998.
• 4. Krishnan S., Rangayyan R.M.: Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations. Med. Biol. Eng Comput. 2000, 38: 2-8.
• 5. Nikolaev N., Gotchev A.: De-noising of ECG signals using wavelet shrinkage with time-frequency dependant threshold. Proc. European Signal Processing Conf. EUSIPCO-98, Island of Rhodes, Greece, 1998, pp. 2449-2453.
• 6. Nikolaev N., Gotchev A., Egiazarian K., Nikolov Z.: Suppression of electromyogram interference on the electrocardiogram by transform domain denoising. Med. Biol. Eng. Comput. 2001, 39: 649-655.
• 7. Paul J., Reedy M., Kumar V.: A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's. IEEE Trans. Biomed. Eng. 2000, 47: 645-662.
• 8. Augustyniak P.: Controlling the Distortions Distribution in a Wavelet Packet-Based ECG Compression. International Conference on Image and Signal Processing, Agadir Morroco, 2001, pp. 267-277.
• 9. Augustyniak P.: How a Human Perceives the Electrocardiogram. Computers in Cardiology 2003, 30: 601-604.
• 10. Augustyniak P.: Moving Window Signal Concatenation for Spectral Analysis of ECG Waves. Computing in Cardiology 2010, 37: 665-668.
• 11. Aldroubi A., Feichtinger H.: Exact iterative reconstruction algorithm for multivariate irregularly sampled functions in spline-like spaces: the Lp theory. Proc. Amer. Math. Soc. 1998, 126(9): 2677-2686.
• 12. Moody G.B. :The MIT-BIH arrhythmia database CD-ROM. (Third Ed.). Harvard-MIT Division of Health Sciences and Technology, 1997.
• 13. Willems J.L.: Common Standard for Quantitative Electrocardiography Multilead Atlas - Measurements results Data Set 3. Commission of the European Communities - Medical and Public Health Research, Leuven, 1988.