ECG features extraction : QRS detection and shape recognition

• Autorzy: Dubowik K. , Larsson J.E.
• Języki publikacji: EN

Abstrakty

EN

An algorithm for extraction of ECG features is presented. Digital filters and adaptive thresholds are used for the QRS detection and Chebyshev polynomial approximation is used for QRS shape recognition. The performance of the algorithm is evaluated discussed. The algorithm will be used for monitoring patients in intensive care unit, however future work is required.

Słowa kluczowe

EN

ECG signal; QRS; shape recognition; signal processing

PL

sygnał EKG; QRS; rozpoznawanie kształtu; przetwarzanie sygnału

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2001
• Tom: Vol. 9, nr 1
• Strony: 7--21
• Opis fizyczny: Bibliogr. 26 poz.

Twórcy

autor

Dubowik K.

• Institute of Computer Science, Technical University of Lodz, Sterlinga 16/18, 90-217 Lodz, Poland

autor

Larsson J.E.

• Lund Institute of Technology, Lund University P.O. Box 118, S-221 00 Lund, Sweden

Bibliografia

• [1] Albarran-Sotelo R. et. al., Textbook of Advanced Cardiac Life Support, American Heart Association, 1987, 1990.
• [2] Ruha A. et. al., A Real-Time Microprocessor QRS Detector System with a 1-ms Timing Accuracy for the Measurement of Ambulatory HRV, IEEE Trans, on Biomedical Engineering, 3(44)1997, pp. 159-167.
• [3] Akazawa K. et. al., Adaptive Threshold QRS Detection Algorithm for Ambulatory ECG, Computers in Cardiology 1991, Proceedings, pp. 445-448.
• [4] Kang-Ping Lin, Walter H. Chang, A Technique for Automated Arrythmia Detection of Holter ECG, Engineering in Medicine and Biology Society 1995, IEEE 17th Annual Conference, Volume: 1, 1997, pp. 183-184
• [5] Thakor N. V., Zhu Yi-Sheng, Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection, IEEE Transactions of Biomedical Engineering, 8(38)1991, pp. 785-794.
• [6] Anand R. S., Kumar V., Efficient and Reliable Detection of QRS-segment in ECG Signals, Engineering in Medicine and Biology Society, 1996 and 14,h Conference of the Biomedical Engineering Society of India. An International Meeting, Proceedings of the First Regional Conference, IEEE 1995, pp. 2/56-2/57.
• [7] J. Lee, et. al., A Simple Real-Time QRS Detection Algorithm, Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine, 18th Annual International Conference of the IEEE, Volume: 4, 1997, pp. 1396-1398.
• [8] Cohen K. P., et. al., QRS Detection Using a Fuzzy Neural Network, Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference, Volume: 1, 1997, pp. 189-190.
• [9] Pan J., Tompkins W. J., A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng., (32)1985, pp. 230-236.
• [10] Xue Q., et. al., Neural-Network-Based Adaptive Matched Filtering for QRS Detection, IEEE Transactions of Biomedical Engineering, 39(4)1992, pp. 317-329.
• [11] Poli R., et. al., A Genetic Algorithm Approach to the Design of Optimum QRS Detectors, IEEE Transactions of Biomedical Engineering, 11(42)1995, pp. 1137-1141.
• [12] MIT/BIH Arrhythmia Database, Harvard University and Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA.
• [13] Mitra S. K., Kaiser J. F., Handbook for Digital Signal Processing, John Wiley & Sons, Inc., New York 1993.
• [14] Proakis J. G., Manolakis D. G., Digital Signal Processing. Principles, Algorithms, and Applications, Third Edition, Prentice-Hall, Inc., New York 1996.
• [15] Outram N. J. et. al., Techniques for optimal enhancement and feature extraction of fetal electrocardiogram, Science, Measurement and Technology, IEE Proceedings, Vol. 142 6, Nov. 1995, pp. 482-489.
• [16] Zhu K. et. al., ECG Monitoring with Artifical Neural Networks.
• [17] Rasiah A. I. et. al., QRS Detection using Morphological and Rhythm information, IEEE International Conference on Neural Networks 1995. Proceedings, Volume: 5, 1995, pp. 2287-2292.
• [18] Krajewski Z., Ustrzycka R., SAS-EKG: ECG signal analysing system. ECG signal analysis algorithm description in a new ECG analysis system, International Symposium — System Modeling Control 98, Proceedings, available from: http://ics.p.lodz.pl/smc98/Krajewsk/krajew2.htm.
• [19] Strumiłło P., Detection of ECG Diagnostic Features Using Wavelet Transform, International Symposium — System Modeling Control 98, Proceedings, available from: http://ics.p.lodz.pl/smc98/Strumill/strumill2.htm.
• [20] Millet J. et. al., Previous Identification of QRS Onset and Offset is not Essential for Classifying QRS Complexes in a Single Lead, Computers in Cardiology 1997, pp. 299-302.
• [21] Elghazzawi Z., Geheb F., A Knowledge-Based System for Arrhytmia Detection, Computers in Cardiology, 1996, pp. 541-544.
• [22] Voukydis P. C., A Neural Network System for Detection of Life-Threatening Arrhythmias, Based on Kohonen Networks, Computers in Cardiology 1995, pp. 165-167.
• [23] Moody G. B. R. G., QRS morphology representation and noise estimation using the Karhunen-Loeve transform, Computers in Cardiology 1989, Proceedings, 1990, pp. 269-272.
• [24] Friesen G. M. et. al., A Comparison of the Noise Sensitivity of Nine QRS Detection Algorithms, IEEE Transactions of Biomedical Engineering, 1(37)1990, pp. 85-98.
• [25] Morales R. O. et. al., Evaluation of QRS Morphological Classifiers in the presence of Noise, Computers and Biomedical Research, (30)1997, pp. 200-210.
• [26] Dubowik K., Larsson J. E., Modeling ECG Waveforms.