Tytuł artykułu
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Warianty tytułu
Języki publikacji
Abstrakty
Machine-aided detection of R-peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R-peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R-peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R-peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R-peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R-peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F-score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal.
Wydawca
Czasopismo
Rocznik
Tom
Strony
566--577
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor
- Signal Processing & Communication Lab, Department of Electrical Engineering, National Institute of Technology Rourkela, Odisha 769008, India
autor
- Department of Electrical Engineering, National Institute of Technology Rourkela, Odisha, India
Bibliografia
- [1] McSharry PE, Clifford GD, Tarassenko L, Smith LA. A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng 2003;50:289–94. http://dx.doi.org/10.1109/TBME.2003.808805.
- [2] Lin C, Kail G, Giremus A, Mailhes C, Tourneret J-Y, Hlawatsch F. Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter. Signal Process 2014;104:174–87. http://dx.doi.org/10.1016/j.sigpro.2014.03.011.
- [3] Panigrahy D, Rakshit M, Sahu PK. FPGA implementation of heart rate monitoring system. J Med Syst 2016;40:1–12. http://dx.doi.org/10.1007/s10916-015-0410-4.
- [4] Ari S, Das MK, Chacko A. ECG signal enhancement using S-transform. Comput Biol Med 2013;43:649–60. http://dx.doi.org/10.1016/j.compbiomed.2013.02.015.
- [5] Pan J, Tompkins W. A real-time QRS detection algorithm. Biomed Eng IEEE 1985;BME-32:230–6. http://dx.doi.org/10.1109/TBME.1985.325532.
- [6] Hamilton PS, Tompkins WJ. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans Biomed Eng 1986;33:1157–65.
- [7] Arzeno NM, Deng Z De, Poon CS. Analysis of first-derivative based QRS detection algorithms. IEEE Trans Biomed Eng 2008;55:478–84. http://dx.doi.org/10.1109/TBME.2007.912658.
- [8] Manikandan MS, Soman KP. A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed Signal Process Control 2012;7:118–28. http://dx.doi.org/10.1016/j.bspc.2011.03.004.
- [9] Benitez DS, Gaydecki PA, Zaidi A, Fitzpatrick AP. A new QRS detection algorithm based on the Hilbert transform. Comput Cardiol 2000 2000;27:379–82. http://dx.doi.org/10.1109/CIC.2000.898536.
- [10] Silipo R, Marchesi C. Artificial neural networks for automatic ECG analysis. IEEE Trans Signal Process 1998;46:1417–25. http://dx.doi.org/10.1109/78.668803.
- [11] Pal S, Mitra M. Empirical mode decomposition based ECG enhancement and QRS detection. Comput Biol Med 2012;42:83–92. http://dx.doi.org/10.1016/j.compbiomed.2011.10.012.
- [12] Zidelmal Z, Amirou A, Ould-Abdeslam D, Moukadem A, Dieterlen A. QRS detection using S-transform and Shannon energy. Comput Methods Programs Biomed 2014;116:1–9. http://dx.doi.org/10.1016/j.cmpb.2014.04.008.
- [13] Ghaffarl A, Golbayani H, Ghasemi M. A new mathematical based QRS detector using continuous wavelet transform. Comput Electr Eng 2008;34:81–91. http://dx.doi.org/10.1016/j.compeleceng.2007.10.005.
- [14] Martinez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng 2004;51:570–81. http://dx.doi.org/10.1109/TBME.2003.821031.
- [15] Zidelmal Z, Amirou A, Adnane M, Belouchrani A. QRS detection based on wavelet coefficients. Comput Methods Programs Biomed 2012;107:490–6. http://dx.doi.org/10.1016/j.cmpb.2011.12.004.
- [16] Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 2001;20:45–50. http://dx.doi.org/10.1109/51.932724.
- [17] Banerjee S, Gupta R, Mitra M. Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement 2012;45:474–87. http://dx.doi.org/10.1016/j.measurement.2011.10.025.
- [18] Yan R, Gao RX. Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis. Tribol Int 2009;42:293–302. http://dx.doi.org/10.1016/j.triboint.2008.06.013.
- [19] Addison PS, Walker J, Guido RC. Time–frequency analysis of biosignals. IEEE Eng Med Biol Mag 2009;28(5):14–29. http://dx.doi.org/10.1109/MEMB.2009.934244.
- [20] Chouakri SA, Bereksi-Reguig F, Taleb-Ahmed A. QRS complex detection based on multi wavelet packet decomposition. Appl Math Comput 2011;217:9508–25. http://dx.doi.org/10.1016/j.amc.2011.03.001.
- [21] Rai HM, Trivedi A, Shukla S. ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier. Measurement 2013;46:3238–46. http://dx.doi.org/10.1016/j.measurement.2013.05.021.
- [22] Rein S, Reisslein M. Low-memory wavelet transforms for wireless sensor networks: a tutorial. IEEE Commun Surv Tutorials 2011;13:291–307. http://dx.doi.org/10.1109/SURV.2011.100110.00059.
- [23] Mallat S. A wavelet tour of signal processing. A wavelet tour signal process; 1999;20–41. http://dx.doi.org/10.1016/B978-012466606-1/50004-0.
- [24] Gutiérrez-Rivas R, Garcia JJ, Marnane WP, Hernández Á. Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens J 2015;15: 6036–43.
- [25] Benitez D, Gaydecki PA, Zaidi A, Fitzpatrick AP. The use of the Hilbert transform in ECG signal analysis. Comput Biol Med 2001;31:399–406. http://dx.doi.org/10.1016/S0010-4825(01)00009-9.
- [26] Hennig C, Orglmeister R, Group BE. QRS detection using zero crossing counts. Prog Biomed Res 2003;8:138–45.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fc366354-e545-4e9f-8fb8-df9847cd6945