A Novel Method to Estimate Heart Rate from ECG
1
조회
0
다운로드
0
인용
0
참고문헌
초록
Abstract
Heart rate variability (HRV) in electrocardiogram (ECG) is an important index for understanding the health status of heart and the autonomic nervous system. Most HRV analysis approaches are based on the proper heart rate (HR) data. Estimation of heart rate is thus a key process in the HRV study. In this paper, we report an innovative method to estimate the heart rate. This method is mainly based on the concept of periodicity transform (PT) and instantaneous period (IP) estimate. The method presented is accordingly called the “PT-IP method.” It does not require ECG R-wave detection and thus possesses robust noise-immune capability. While the noise contamination, ECG time-varying morphology, and subjects' physiological variations make the R-wave detection a difficult task, this method can help us effectively estimate HR for medical research and clinical diagnosis. The results of estimating HR from empirical ECG data verify the efficacy and reliability of the proposed method.
참고문헌 (34)
- R.E. Kleiger, "Heart rate variability: Measurement and clinical utility", 10(1), pp.88~101 (2005)
- N. Kannathal, "Cardiac health diagnosis using heart rate variability signals - A comparative study", 10(1), pp.23~36 (2004)
- Y.R. Zhong, "Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes", 51(2), pp.255~262 (2004)
- P. Sleight, "The importance of the autonomic nervous system in health and disease", 27(4), pp.467~473 (1997)
- D. Gordon, "Power spectrum analysis of heart rate fluctuation a quantitative probe of beat-to-beat cardiovascular control", 213, pp.220~222 (1981)
- Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, "Heart rate variability: Standards of measurement, physiological interpre- tation, and clinical use", 93, pp.1043~1065 (1996)
- W.P. Holsinger, "A QRS preprocessor based on digital differentiation IEEE Transac- tions on Biomedical Engineering", 18, pp.212~217 (1971)
- O. Pahlm, "Software QRS detection in ambulatory monitoring-a review", 22, pp.289~297 (1984)
- G.M. Friesen, "A comparison of the noise sensitivity of nine QRS detection algorithm IEEE Transactions on Biomedical Engineering", 37, pp.85~98 (1990)
- F.rtet, "Evaluation of real-time QRS detection algorithms in variable contexts", 43(3), pp.379~385 (2005)
- no. 2, "An approach to QRS complex detection using mathematical morphology IEEE Transactions on Biomedical Engineering", 40, pp.201~205 (1993)
- K.I. Panoulas, "Enha- ncement of R-wave detection in ECG data analysis using higher-order statistics", 1, pp.344~347 (2001)
- S.W. Chen, "real-time QRS- detection method based on moving-averaging incorporating with wavelet denoising", 82(3), pp.187~195 (2006)
- G. Vijaya, "ANN- based QRS- complex analysis of ECG", 22(4), pp.160~167 (1998)
- G. Vijaya, "Artificial neural network based wave complex detection in electrocardiograms", 28(2), pp.125~132 (1997)
- Z.S. Wang, "Robust ECG R-R wave detection using evolutionary programming based fuzzy inference system (EPFIS), and application to assessing brain-gut interaction", 147, pp.351~356 (2000)
- R. Murray, "Wavelet transform-based QRS complex detector", 46(7), pp.838~848 (1999)
- I.R. Legarreta, "R-wave detection using continuous wavelet modulus maxima", 30, pp.565~568 (2003)
- J.W. Lee, "A real time QRS detection using delay-coordinate mapping for the microcontroller implementation", 30(9), pp.1140~1151 (2002)
- R. Poli, "Genetic design of optimum linear and nonlinear QRS detectors", 42(11), pp.1137~1141 (1995)
- J. Lee, "A new QRS detection algorithm using index function based on resonance theory", 24(2), pp.107~112 (2003)
- C. Meyer, "Combining algorithms in automatic detection of QRS complexes in ECG signals", 10(3), pp.468~475 (2006)
- A.K. Barros, "Heart instantaneous frequency (HIF): An alternative approach to extract heart rate variability", 48(8), pp.850~855 (2001)
- T. Ravichandran, "Estimation and power spectral analysis of heart instantaneous frequency (HIF) - a wavelet approach", 1(1), pp.223~226 (2003)
- W.A. Sethares, "Periodicity transforms", 47, pp.2953~2964 (1999)
- P.C. Lo, "Quantification of pseudo- periodicity of alpha rhythm in meditation EEG Journal of Medical and Biological Engineering", 25, pp.7~13
- M. Arif, "Application of threshold- based acceleration change index (TACI) in heart rate variability analysis", 26, pp.653~665 (2005)
- H. Kawahara, "Speech representation and transformation using adaptive interpolation of weighted spectrum: vocoder revisited", 2, pp.1303~1306 (1997)
- A.L. Goldberger, "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals", 101(23), pp.215~22 (2000)
- X. Hu, "A single-lead ECG enhancement algorithm using a regularized data- driven filter", 53(2), pp.347~351 (2006)
- Y.C. Pu, "Comparison of R-wave detection errors of four wireless heart rate belts in the presence of noise", 24(4), pp.913~924 (2003)
- L. Hejjel, "Suppression of power-line interference by analog notch filtering in the ECG signal for heart rate variability analysis: to do or not to do?", 10(1), pp.6~13 (2004)
- J. Young, "Comparison of fast Fourier transform and autoregressive spectral analysis for the study of heart rate variability in diabetic patients", 104(3), pp.307~313 (2005)
- M. Karrakchou, "Analysis of heart rate variability comparison between spectra obtained from ECG and finger blood pressure", 2, pp.559~560
