Abstract
The most characteristic wave set in ECG signals is the QRS complex. Automatic procedures to classify the QRS are very useful in the diagnosis of cardiac dysfunctions. Early detection and classification of QRS changes are important in realtime monitoring. ECG data compression is also important for storage and data transmission. An Adaptive Hermite Model Estimation System (AHMES) is presented for on-line beat-to-beat estimation of the features that describe the QRS complex with the Hermite model. The AHMES is based on the multiple-input adaptive linear combiner, using as inputs the succession of the QRS complexes and the Hermite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient real-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addition, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The application is shown, for subsequent classification, of the AHMES in extracting the QRS features of an ECG signal with the bigeminy phenomena. Another application is highlighted that helps wide ectopic beats detection using the width parameter b.
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References
Ahmed, N., andRao, K. R. (1875): ‘Orthogonal transforms for digital signal processing’ (Springer-Verlag, New York)
Bendat, J. S., andPiersol, A. G. (1986): ‘Random data. analysis and measurements procedures’ (John Wiley & Sons, New York)
Christov, I. I., Dotsinsky, I. A., andDaskalov, I. K. (1992): ‘High-pass filtering of ECG signals using QRS elimination’,Med. Biol. Eng. Comput.,302, (2), pp. 253–256
Ferrara, E. R., andWidrow, B. (1981): ‘The time-sequenced adaptive filter’,IEEE Trans.,CAS-28, pp. 519–523
Franks, L. E. (1975): ‘Signal theory’ (Prentice-Hall, Englewood Cliffs, New Jersey)
Jané, R., Laguna, P., andCaminal, P. (1991a): ‘Adaptive estimation of event-related bioelectric signals: effect of misalignment errors’. Proc. 13th Int. Conf. of the IEEE Engineering in Medicine and Biology Society, Orlando, pp. 365–366
Jané, R., Olmos, S., Laguna, P., andCaminal, P. (1993): ‘Adaptive Hermite models for ECG data compression: performance and evaluation with automatic wave detection’. Computers in Cardiology (IEEE Computer Society Press) pp. 389–392
Jané, R., Rix, H., Caminal, P., andLaguna, P. (1991b): ‘Alignment methods for signal averaging of high resolution cardiac signals: a comparative study of performance’,IEEE Trans.,BME-38, (6), pp. 571–579
Laguna, P. (1990): ‘New electrocardiographic signal processing techniques: application to long-term records’. PhD Thesis, Science Faculty, Zaragoza, Spain (in Spanish)
Laguna, P., Caminal, P., Thakor, N. V., andJané, R. (1989): ‘Adaptive QRS shape estimation using Hermite model’. Proc. 11th IEEE Ann. Conf. of Engineering in Medicine and Biology Society, pp. 683–684
Laguna, P., Jané, R., andCaminal, P. (1992a): ‘Adaptive feature extraction for QRS classification and ectopic beat detection’. Computers in Cardiology (IEEE Computer Society Press) pp. 613–616
Laguna, P., Jané, R., Meste, O., Poon, P. W., Caminal, P., Rix, H., andThakor, N. V. (1992b): ‘Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques’,IEEE Trans.,BME-39, (10), pp. 1032–1044
Meyer, C. R., andKeiser, H. N. (1977): ‘Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques’,Comput. biomed. Res.,10, pp. 459–470
Pan, J., andTompkins, W. J. (1985): ‘A real-time QRS detection algorithm’,IEEE Trans.,BME-32, (3), pp. 230–236
Rappaport, S. H., Gillick, L., Moody, G. B., andMark, R. G. (1982): ‘QRS morphology classification: Quantitative evaluation of different strategies’. Computers in Cardiology (IEEE Computer Society Press) pp. 33–38
Rompelman, P., andRos, H. H. (1986): ‘Coherent averaging technique: a tutorial review. part 1: noise reduction and the equivalent filter. part 2: trigger jitter, overlapping responses and non-periodic stimulation’,J. Biomed. Eng.,8, pp. 24–35
Sörnmo, L., Börjesson, P. O., Nygards, M. E. andPahlm, O. (1981): ‘A method for evaluation of QRS shape features using a methematical model for the ECG’,IEEE Trans.,BME-28, (19), pp. 713–717
Thakor, N. V., Webster, J. G., andTompkins, W. J. (1984): ‘Estimation of QRS complex power spectrum for design of a QRS filter’,IEEE Trans.,BME-31, (11), pp. 702–706
Thakor, N. V., andYi-Sheng, Z. (1991): ‘Applications of adaptive filtering to ECG analysis: noise cancelation and arrhythmia detection’,IEEE Trans.,BME-38, pp. 785–794
Trahanias, P., andSkordalakis, E. (1990): ‘Syntactic pattern recognition of the ECG’,IEEE Trans.,BME-12, pp. 648–657
Vaz, C. A., andThakor, N. V. (1989): ‘Adaptive Fourier estimation of time-varying evoked potentials’,IEEE Trans.,BME-36, (4), pp. 443–455
Widrow, B., andStearns, S. D. (1985): ‘Adaptive signal processing’ (Prentice-Hall, Englewood Cliffs, New Jersey)
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Laguna, P., Jané, R., Olmos, S. et al. Adaptive estimation of QRS complex wave features of ECG signal by the hermite model. Med. Biol. Eng. Comput. 34, 58–68 (1996). https://doi.org/10.1007/BF02637023
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DOI: https://doi.org/10.1007/BF02637023