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Electrocardiographic characteristics of significant factors of detected atrial fibrillation using WEMS
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 Title & Authors
Electrocardiographic characteristics of significant factors of detected atrial fibrillation using WEMS
Kim, Min Soo; Kim, Yoon Nyun; Cho, Young Chang;
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The wireless electrocardiographic monitoring system(WDMS) is designed to be long term monitoring for the early detection of cardiac disorders. The current version of the WDMS can identify two types of cardiac rhythms in real-time, such as atrial fibrillation(AF) and normal sinus rhythm(NSR), which are very important to track cardiac-rhythm disorders. In this study, we proposed the analysis method to discriminate the characteristics statistically evaluated in both time and frequency domains between AF and NSR using various parameters in the heart rate variability(HRV). And we applied various ECG detection methods (e.g., difference operation method) and compared the results with those of the discrete wavelet transform(DWT) method. From the statistically results, we found that the parameters such as STD RR, STD HR, RMSSD, NN50, pNN50, RR Trian, and TNN(p<0.05) are significantly different between the AF and NSR patients in time domain. On the other hand, the frequency domain analysis results showed a significant difference in VLF power(), LF power(), HF power(), VLF(%), LF(%), and HF(%). In particular, the parameters such as STD RR, RMSSD, NN50, pNN50, VLF power, LF power and HF power were considered as the most useful parameters in both AF and NSR patient groups. Our proposed method can be efficiently applied to early detection of abnormal conditions and prevent the such abnormals from becoming serious.
Wireless electrocardiographic monitoring system(WEMS);Wavelet transform (DWT);Arrhythmias;Atrial fibrillation(AF);Heart rate variability(HRV);
 Cited by
E.P Johan, M.B, "Atrial fibrillation," Circulation, Vol 106, pp. 14-16, 2002. crossref(new window)

E. Guirguis, "Holter monitoring. Can. Fam.," Physician., Vol 33, pp. 985-992. 1987.

G. Senatore, G. Stabile, E. Bertaglia et al., "Role of transtelephonic electrocardiographic monitoring in detecting short-term arrhythmia recurrences after radio frequency ablation in patients with atrial fibrillation," J. Am. Coll. Cardiol., Vol 45, pp. 873-876, 2005. crossref(new window)

S. S. Lobodzinski, M.M. Laks, "New devices for very long-term ECG monitoring," Cardiol. J., Vol 19, pp. 210-214, 2012. crossref(new window)

M.S. Kim, Y.C. Cho, S.T. Seo, C.S. Son, Y.N. Kim, "Auto-detection of R wave in ECG for patch type ECG remote monitoring system," Biomed. Eng. Letter, Vol 1, pp. 180-187, 2011. crossref(new window)

D.A. Coast, R.M. Stern, G.G. Cano, S.A. Briller, "An approach to cardiac arrhythmia analysis using hidden Markov models," IEEE Trans. Biomed. Eng., Vol 37, pp. 826-836, 1990. crossref(new window)

Y. Ozbay, R. Ceylan, B. Karlik, "A fuzzy clustering neural network architecture for classification of ECG arrhythmias," Comput. Biol. Med., Vol 36, pp. 376-388, 2003.

L. Khadra, A.S. Al-Fahoum, S. Binajjaj, "A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques," IEEE Trans. Biomed. Eng., Vol 522, pp. 1840-1845, 2005.

R. Ceylan, Y. Ozbay, "Comparison of FCM, PCA and WT techniques for classification of ECG arrhythmias using artificial neural network, "Expert Syst. Appl., Vol 33, pp. 286-295, 2007. crossref(new window)

Y.C. Yeh, W. J. Wang, "QRS complexes detection for ECG signal: the difference operation method," Comput. Methods Programs Biomed., Vol 91, pp. 245-254, 2008. crossref(new window)

M.S. Kim, Y.C. Cho, S.T. Seo, C.S. Son, Y.N. Kim, "A new method of ECG feature detection based on combined wavelet transform for u-health service," Biomed. Eng. Letter, Vol 1, pp. 67-76, 2011.

Y.C. Cho, M.S. Kim, J.O. Yoon," Comparison of Characteristics of P-Wave Detection in ECG with Wireless Patch Electrodes," Journal of the Korea Industrial Information System Society, Vol 19, pp.43-52, 2014.

Y.S. Heo, J.C. Lee and Y.N. Kim," Analysis and Processing of Driver's Biological Signal of Workload," Journal of the Korea Industrial Information System Society, Vol 20, pp. 87-93, 2015. crossref(new window)

S. Mallet, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Trans. Pattern Anal. Mach. Intelligence, Vol 11, pp.674-693. 1989. crossref(new window)

A. Grap, "An introduction to wavelets," IEEE Comput. Sci. Eng., Vol 2, 1995.

S.Z. Mahamoodibad, A. Ahmadian, M.D. Abolhasani, "ECG feature extraction using Daubechies wavelets, Proceedings of Fifth IASTED International Conference., pp. 343-348, 2005.

I. Daubechies, "The wavelet transform, time-frequency localization and signal analysis," IEEE Trans. Inform. Theory, Vol 36, pp. 961-1005, 1990. crossref(new window)

M. Malik, A.J. Camm, "(Eds.),Heart rate variability," Futura Pub. Co. Inc., Armonk, New York. 1995.

R.E. Kleiger, P.K. Stein, M.S. Bosner, J.N. Rottman, "Time domain measurements of heart rate variability," in: M. Malik., A.J. Camm (Eds.), Heart rate variability, Futura Pub. Co. Inc., Armonk, New York, pp. 33-45, 1995.

S. Akselrod, D. Gordon, F.A. Ubel, D.C. Shannon, A.C. Barger, R.I. Cohen, "Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control," Science, Vol 213, pp. 220-222, 1981. crossref(new window)

N.S. Cai, M. Dohnal, S.B. Olsson, "Methodological aspects of the use of heart rate stratified R-R interval histograms in the analysis of atrioventricular conduction during atrial fibrillation," Cardiovasc. Research., Vol 21, pp. 455-462, 1987. crossref(new window)

M. Brennan, M. Palaniswami, P. Kamen, "Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?," IEEE Trans. BME., Vol 48, pp. 1342-1347, 2001. crossref(new window)

D. Singh, K. Vinod, "Effect of R-R segment duration on short-term HRV assessment using Poincare plot," Proceeding of ICISIP, 2005.

T. Thong, "Geometric measures of Poincare plots for the detection of small sympathovagal shifts," Proceedings of the 29th Annual International Conference of the IEEE EMBS, France., pp. 23-26, 2007.