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Biomedical Science and Engineering. 2014, 2(1), 13-34
DOI: 10.12691/bse-2-1-3
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Rami J. Oweis1, and Basim O. Al-Tabbaa1

1Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid, Jordan

Pub. Date: February 17, 2014

Cite this paper:
Rami J. Oweis and Basim O. Al-Tabbaa. Withdraw this article (due to some internal problems related to the article). Biomedical Science and Engineering. 2014; 2(1):13-34. doi: 10.12691/bse-2-1-3



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[1]  Kovács, P. (2012, February). ECG signal generator based on geometrical features. In Annales Univ. Sci. Budapest., Sect. Comp (Vol. 37, pp. 247-260).
[2]  Mark, R. G. (2004). HST. 542J/2.792 J/BE. 371J/6.022 J Quantitative Physiology: Organ Transport Systems.
[3]  Reisner, A. T., Clifford, G. D., & Mark, R. G. (2007). The physiological basis of the electrocardiogram.
[4]  Aldersons, A., & Buikis, A. (2011, August). Mathematical algorithm for heart rate variability analysis. In Proceedings of the 11th WSEAS international conference on applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications (pp. 381-386).
[5]  Camm, A. J., Malik, M., Bigger, J. T., Breithardt, G., Cerutti, S., Cohen, R. J., & Singer, D. H. (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93(5), 1043-1065.
[6]  Kaplan, D. T., Furman, M. I., Pincus, S. M., Ryan, S. M., Lipsitz, L. A., & Goldberger, A. L. (1991). Aging and the complexity of cardiovascular dynamics.Biophysical Journal, 59(4), 945-949.
[7]  Pikkujämsä, S. M., Mäkikallio, T. H., Sourander, L. B., Räihä, I. J., Puukka, P., Skyttä, J., & Huikuri, H. V. (1999). Cardiac interbeat interval dynamics from childhood to senescence comparison of conventional and new measures based on fractals and chaos theory. Circulation, 100(4), 393-399.
[8]  Phyllis, K., Kleiger, M. D., Robert, E., Rottman, M. D., & Jeffrey, N. (1997). Differing effects of age on heart rate variability in men and women. The American journal of cardiology, 80(3), 302-305.
[9]  Sinnreich, R., Kark, J. D., Friedlander, Y., Sapoznikov, D., & Luria, M. H. (1998). Five minute recordings of heart rate variability for population studies: repeatability and age–sex characteristics. Heart, 80(2), 156-162.
[10]  Zhang, J. (2007). Effect of age and sex on heart rate variability in healthy subjects. Journal of manipulative and physiological therapeutics, 30(5), 374-379.
[11]  Carter, J. B., Banister, E. W., & Blaber, A. P. (2003). The effect of age and gender on heart rate variability after endurance training. Medicine and science in sports and exercise, 35(8), 1333-1340.
[12]  Huikuri, H. V., Pikkuja, S. M., Airaksinen, K. J., Ika, M. J., Rantala, A. O., Kauma, H., & Kesa, Y. A. (1996). Sex-related differences in autonomic modulation of heart rate in middle-aged subjects. Circulation, 94(2), 122-125.
[13]  Ramaekers, D., Ector, H., Aubert, A. E., Rubens, A., & Van de Werf, F. (1998). Heart rate variability and heart rate in healthy volunteers. Is the female autonomic nervous system cardioprotective?. European Heart Journal, 19(9), 1334-1341.
[14]  De La Cruz Torres, B., López, C. L., & Orellana, J. N. (2008). Analysis of heart rate variability at rest and during aerobic exercise: a study in healthy people and cardiac patients. British journal of sports medicine, 42(9), 715-720.
[15]  Bernardi, L., Ricordi, L., Lazzari, P., Solda, P., Calciati, A., Ferrari, M. R., & Fratino, P. (1992). Impaired circadian modulation of sympathovagal activity in diabetes. A possible explanation for altered temporal onset of cardiovascular disease. Circulation, 86(5), 1443-1452.
[16]  Guzzetti, S. T. E. F. A. N. O., Cogliati, C. H. I. A. R. A., Broggi, C. A. R. O. L. A., Carozzi, C. A. R. L. A., Caldiroli, D., Lombardi, F. E. D. E. R. I. C. O., & Malliani, A. L. B. E. R. T. (1994). Influences of neural mechanisms on heart period and arterial pressure variabilities in quadriplegic patients. American Journal of Physiology-Heart and Circulatory Physiology, 266(3), H1112-H1120.
[17]  Koh, J., Brown, T. E., Beightol, L. A., Ha, C. Y., & Eckberg, D. L. (1994). Human autonomic rhythms: vagal cardiac mechanisms in tetraplegic subjects.The Journal of physiology, 474(3), 483-495.
[18]  Garrido Esquivel, A., de la CruzTorres, B., Garrido Salazar, M. A., Medina Corrales, M., & Naranjo Orellana, J. (2009). Variabilidad de la frecuencia cardiaca en un deportista juvenil durante una competición de bádminton de máximo nivel. Revista Andaluza de Medicina del Deporte, 2(2), 70-74.
[19]  Saa, Y. D., Sarmiento, S., Martín González, J. M., Rodríguez Ruiz, D., Quiroga, M. E., & García Manso, J. M. (2009). Aplicación de la variabilidad de la frecuencia cardiaca en la caracterización de deportistas de élite de lucha canaria con diferente nivel de rendimiento. Archivos de medicina del deporte,2(4), 120-125.
[20]  Aubert, A. E., Seps, B., & Beckers, F. (2003). Heart rate variability in athletes.Sports Medicine, 33(12), 889-919.
[21]  Aubert, A. E., Beckers, F., & Ramaekers, D. (2000). Short-term heart rate variability in young athletes. Journal of cardiology, 37, 85-88.
[22]  Bonnemeier, H., Wiegand, U. K., Brandes, A., Kluge, N., Katus, H. A., Richardt, G., & Potratz, J. (2003). Circadian profile of cardiac Journal of cardiovascular electrophysiology, 14(8), 791-799.
[23]  Hall, M., Thayer, J. F., Germain, A., Moul, D., Vasko, R., Puhl, M., & Buysse, D. J. (2007). Psychological stress is associated with heightened physiological arousal during NREM sleep in primary insomnia. Behavioral sleep medicine, 5(3), 178-193.
[24]  Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K. R., & Iellamo, F. (2009). Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior. Neuroscience & Biobehavioral Reviews, 33(2), 71-80.
[25]  De Boer, R. W., Karemaker, J. M., & Strackee, J. (1985). Relationships between short-term blood-pressure fluctuations and heart-rate variability in resting subjects I: a spectral analysis approach. Medical and Biological Engineering and Computing, 23(4), 352-358.
[26]  Rothschild, M., Rothschild, A., & Pfeifer, M. (1988). Temporary decrease in cardiac parasympathetic tone after acute myocardial infarction. The American journal of cardiology, 62(9), 637-639.
[27]  Rossinen, M. D., Viitasalo, M. D., Partanen, M. D., Koskinen, M. D., Kupari, M. D., Nieminen, M. D., & Markku, S. (1997). Effects of acute alcohol ingestion on heart rate variability in patients with documented coronary artery disease and stable angina pectoris. The American journal of cardiology, 79(4), 487-491.
[28]  Carney, R. M., Blumenthal, J. A., Stein, P. K., Watkins, L., Catellier, D., Berkman, L. F., & Freedland, K. E. (2001). Depression, heart rate variability, and acute myocardial infarction. Circulation, 104(17), 2024-2028.
[29]  Carney, R. M., Blumenthal, J. A., Freedland, K. E., Stein, P. K., Howells, W. B., Berkman, L. F., & Jaffe, A. S. (2005). Low heart rate variability and the effect of depression on post-myocardial infarction mortality. Archives of internal medicine, 165(13), 1486.
[30]  Pfeifer, M. A., Cook, D., Brodsky, J., Tice, D., Reenan, A., Swedine, S., & Porte, D. (1982). Quantitative evaluation of cardiac parasympathetic activity in normal and diabetic man. Diabetes, 31(4), 339-345.
[31]  Singh, J. P., Larson, M. G., O’Donnell, C. J., Wilson, P. F., Tsuji, H., Lloyd-Jones, D. M., & Levy, D. (2000). Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study). The American journal of cardiology, 86(3), 309-312.
[32]  Wheeler, T., & Watkins, P. J. (1973). Cardiac denervation in diabetes. British Medical Journal, 4(5892), 584.
[33]  Forsström, J., Forsström, J., Heinonen, E., Välimäki, I., & Antila, K. (1986). Effects of haemodialysis on heart rate variability in chronic renal failure.Scandinavian journal of clinical & laboratory investigation, 46(7), 665-670.
[34]  Zoccali, C., Ciccarelli, M., & Maggiore, Q. (1982). Defective reflex control of heart rate in dialysis patients: evidence for an afferent autonomic lesion. Clinical Science, 63, 285-292.
[35]  Lerma, C., Minzoni, A., Infante, O., & José, M. V. (2004). A mathematical analysis for the cardiovascular control adaptations in chronic renal failure.Artificial organs, 28(4), 398-409.
[36]  Ewing, D. J., & Winney, R. (1975). Autonomic function in patients with chronic renal failure on intermittent haemodialysis. Nephron, 15(6), 424-429.
[37]  Nagy, E., Orvos, H., Bárdos, G., & Molnár, P. (2000). Gender-related heart rate differences in human neonates. Pediatric Research, 47(6), 778-780.
[38]  Spallone, V., Bernardi, L., Ricordi, L., Soldà, P., Maiello, M. R., Calciati, A., & Menzinger, G. (1993). Relationship between the circadian rhythms of blood pressure and sympathovagal balance in diabetic autonomic neuropathy.Diabetes, 42(12), 1745-1752.
[39]  van Ravenswaaij-Arts, C., Hopman, J. C., Kollée, L. A., van Amen, J. P., Stoelinga, G., & van Geijn, H. P. (1991). Influences on heart rate variability in spontaneously breathing preterm infants. Early human development, 27(3), 187-205.
[40]  Bekheit, S., Tangella, M., el-Sakr, A., Rasheed, Q., Craelius, W., & El-Sherif, N. (1990). Use of heart rate spectral analysis to study the effects of calcium channel blockers on sympathetic activity after myocardial infarction. American heart journal, 119(1), 79-85.
[41]  Coumel, P., Hermida, J. S., Wennerblöm, B., Leenhardt, A., Maison-Blanche, P., & Cauchemez, B. (1991). Heart rate variability in left ventricular hypertrophy and heart failure, and the effects of beta-blockade a non-spectral analysis of heart rate variability in the frequency domain and in the time domain. European heart journal, 12(3), 412-422.
[42]  Guzzetti, S., Piccaluga, E., Casati, R., Cerutti, S., Lombardi, F., Pagani, M., & Malliani, A. (1988). Sympathetic predominance an essential hypertension: a study employing spectral analysis of heart rate variability. Journal of hypertension, 6(9), 711-717.
[43]  Lucini, D., Bertocchi, F., Malliani, A., & Pagani, M. (1996). A controlled study of the autonomic changes produced by habitual cigarette smoking in healthy subjects. Cardiovascular research, 31(4), 633-639.
[44]  Hayano, J., Yamada, M., Sakakibara, Y., Fujinami, T., Yokoyama, K., Watanabe, Y., & Takata, K. (1990). Short-and long-term effects of cigarette smoking on heart rate variability. The American journal of cardiology, 65(1), 84-88.
[45]  Kamath, M. V., & Fallen, E. L. (1995). Correction of the heart rate variability signal for ectopics and missing beats. Heart rate variability. Armonk: Futura, 75-85.
[46]  Zeskind, P. S., & Gingras, J. L. (2006). Maternal cigarette-smoking during pregnancy disrupts rhythms in fetal heart rate. Journal of pediatric psychology,31(1), 5-14.
[47]  Webster, J. G. Medical instrumentation: application and design. 1998. John Wiley&Sons, NY.
[48]  Manolakis, D. G., Ingle, V. K., & Kogon, S. M. (2000). Statistical and adaptive signal processing: spectral estimation, signal modeling, adaptive filtering, and array processing (pp. 378-387). Boston: McGraw-Hill.
[49]  Li, Q., Mark, R. G., & Clifford, G. D. (2008). Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiological measurement, 29(1), 15.
[50]  Goutas, A., Ferdi, Y., Herbeuval, J. P., Boudraa, M., & Boucheham, B. (2005). Digital fractional order differentiation-based algorithm for P and T-waves detection and delineation. ITBM-RBM, 26(2), 127-132.
[51]  Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm.Biomedical Engineering, IEEE Transactions on, (3), 230-236.
[52]  Okada, M. (1979). A digital filter for the ors complex detection. Biomedical Engineering, IEEE Transactions on, (12), 700-703.
[53]  Menrad, A. (1981). Dual microprocessor system for cardiovascular data acquisition, processing and recording. Proc. 1981 IEEE Inr. Con5 Industrial Elect. Contr. Instrument, 64-69.
[54]  Holsinger, W. P., Kempner, K. M., & Miller, M. H. (1971). A QRS preprocessor based on digital differentiation. Biomedical Engineering, IEEE Transactions on, (3), 212-217.
[55]  Morizet-Mahoudeaux, P., Moreau, C., Moreau, D., & Quarante, J. J. (1981). Simple microprocessor-based system for on-line ECG arrhythmia analysis.Medical and Biological Engineering and Computing, 19(4), 497-500.
[56]  Fraden, J., & Neuman, M. R. (1980). QRS wave detection. Medical and Biological Engineering and computing, 18(2), 125-132.
[57]  Balda, R. A., Diller, G., Deardorff, E., Doue, J., & Hsieh, P. (1977). The HP ECG analysis program. Trends in Computer-Processed Electrocardiograms, 197-205.
[58]  Ahlstrom, M. L., & Tompkins, W. J. (1983). Automated high-speed analysis of Holter tapes with microcomputers. Biomedical Engineering, IEEE Transactions on, (10), 651-657.
[59]  Engelse, W. A. H., & Zeelenberg, C. (1979). A single scan algorithm for QRS detection and feature extraction. Computers in cardiology, 6(1979), 37-42.
[60]  Trahanias, P., & Skordalakis, E. (1989). Bottom-up approach to the ECG pattern-recognition problem. Medical and Biological Engineering and Computing, 27(3), 221-229.
[61]  Zhang, F., & Lian, Y. (2007, August). Electrocardiogram QRS detection using multiscale filtering based on mathematical morphology. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 3196-3199). IEEE.
[62]  Arzeno, N. M., Deng, Z. D., & Poon, C. S. (2008). Analysis of first-derivative based QRS detection algorithms. Biomedical Engineering, IEEE Transactions on, 55(2), 478-484.
[63]  Benitez, D. S., Gaydecki, P. A., Zaidi, A., & Fitzpatrick, A. P. (2000). A new QRS detection algorithm based on the Hilbert transform. In Computers in Cardiology 2000 (pp. 379-382). IEEE.
[64]  Sufi, F., Fang, Q., & Cosic, I. (2007, August). Ecg rr peak detection on mobile phones. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 3697-3700). IEEE.
[65]  Zhang, F., & Lian, Y. (2007, November). Novel QRS detection by CWT for ECG sensor. In Biomedical Circuits and Systems Conference, 2007. BIOCAS 2007. IEEE (pp. 211-214). IEEE.
[66]  Friesen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., & Nagle, H. T. (1990). A comparison of the noise sensitivity of nine QRS detection algorithms. Biomedical Engineering, IEEE Transactions on, 37(1), 85-98..
[67]  Christov, I. I. (2004). Real time electrocardiogram QRS detection using combined adaptive threshold. BioMedical Engineering OnLine, 3(1), 28.
[68]  Kohler, B. U., Hennig, C., & Orglmeister, R. (2002). The principles of software QRS detection. Engineering in Medicine and Biology Magazine, IEEE, 21(1), 42-57..
[69]  Vidal, C., Charnay, P., & Arce, P. (2009, January). Enhancement of a QRS detection algorithm based on the first derivative, using techniques of a QRS detector algorithm based on non-linear transformations. In 4th European Conference of the International Federation for Medical and Biological Engineering (pp. 393-396). Springer Berlin Heidelberg.
[70]  Ahlstrom, M. L., & Tompkins, W. J. (1983). Automated high-speed analysis of Holter tapes with microcomputers. Biomedical Engineering, IEEE Transactions on, (10), 651-657.
[71]  Macfarlane, P. W., Devine, B., & Clark, E. (2005, September). The university of Glasgow (Uni-G) ECG analysis program. In Computers in Cardiology, 2005 (pp. 451-454). IEEE.
[72]  Olvera, F. E. (2006). Electrocardiogram waveform feature extraction using the matched filter. ECE SIO Stat Proc.
[73]  Kay, S. M. (1998). Fundamentals of Statistical signal processing, Volume 2: Detection theory (pp. 345-349). Prentice Hall PTR.
[74]  Hamilton, P. S., & Tompkins, W. J. (1988, November). Adaptive matched filtering for QRS detection. In Engineering in Medicine and Biology Society, 1988. Proceedings of the Annual International Conference of the IEEE (pp. 147-148). IEEE.
[75]  Dobbs, S. E., Schmitt, N. M., & Ozemek, H. S. (1984). QRS detection by template matching using real-time correlation on a microcomputer. Journal of clinical engineering, 9(3), 197-212.
[76]  Ebenezer, D., & Krishnamurthy, V. (1993). Wave digital matched filter for electrocardiogram preprocessing. Journal of biomedical engineering, 15(2), 132-134.
[77]  Kaplan, D. T. (1990, September). Simultaneous QRS detection and feature extraction using simple matched filter basis functions. In Computers in Cardiology 1990, Proceedings. (pp. 503-506). IEEE.
[78]  Ruha, A., Sallinen, S., & Nissila, S. (1997). A real-time microprocessor QRS detector system with a 1-ms timing accuracy for the measurement of ambulatory HRV. Biomedical Engineering, IEEE Transactions on, 44(3), 159-167.
[79]  Rangayyan, R. M. (2002). Biomedical signal analysis (pp. 55-91). New York: IEEE press.
[80]  Xue, Q., Hu, Y. H., & Tompkins, W. J. (1992). Neural-network-based adaptive matched filtering for QRS detection. Biomedical Engineering, IEEE Transactions on, 39(4), 317-329.
[81]  Lu, Y., Xian, Y., Chen, J., & Zheng, Z. (2008, May). A comparative study to extract the diaphragmatic electromyogram signal. In BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on (Vol. 2, pp. 315-319). IEEE.
[82]  Gupta, R., Chatterjee, H. K., & Mitra, M. (2012). An online ECG QRS Detection Technique. Int. J. on Recent Trends in Engineering and Technology, 7(2).
[83]  Mehta, S. S., & Lingayat, N. S. (2007). Comparative study of QRS detection in single lead and 12-lead ecg based on entropy and combined entropy criteria using support vector machine. Journal of Theoretical and Applied Information Technology, 3(2), 8-18.
[84]  Mehta, S. S., & Lingayat, N. S. (2009). Identification of QRS complexes in 12-lead electrocardiogram. Expert Systems with Applications, 36(1), 820-828.
[85]  Nouira, I., Abdallah, A. B., Bedoui, M. H., & Dogui, M. A Robust R Peak Detection Algorithm Using Wavelet Transform for Heart Rate Variability Studies, International Journal on Electrical Engineering and Informatics - Volume 5,Number 3, September 2013.
[86]  Hamilton, P. S., & Tompkins, W. J. (1986). Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. Biomedical Engineering, IEEE Transactions on, (12), 1157-1165.
[87]  Hargittai, S. (2005, September). Savitzky-Golay least-squares polynomial filters in ECG signal processing. In Computers in Cardiology, 2005 (pp. 763-766). IEEE.
[88]  Luo, J., Ying, K., & Bai, J. (2005). Savitzky–Golay smoothing and differentiation filter for even number data. Signal Processing, 85(7), 1429-1434.
[89]  Das, S., & Chakraborty, M. (2012). QRS Detection Algorithm Using Savitzky-Golay Filter. Aceee International Journal on signal & Image processing, 3(1).
[90]  Fira, C. M., & Goras, L. (2008). An ECG signals compression method and its validation using NNs. Biomedical Engineering, IEEE Transactions on, 55(4), 1319-1326.
[91]  Suppappola, S. E. T. H., & Sun, Y. (1994). Nonlinear transforms of ECG signals for digital QRS detection: a quantitative analysis. Biomedical Engineering, IEEE Transactions on, 41(4), 397-400.
[92]  Lin, C. C., Hu, W. C., Chen, C. M., & Weng, C. H. (2008, September). Heart rate detection in highly noisy handgrip electrocardiogram. In Computers in Cardiology, 2008 (pp. 477-480). IEEE.
[93]  Ulusar, U. D., Govindan, R. B., Wilson, J. D., Lowery, C. L., Preissl, H., & Eswaran, H. (2009, September). Adaptive rule based fetal QRS complex detection using Hilbert transform. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (pp. 4666-4669). IEEE.
[94]  Clifford, G. D., & Azuaje, F. (2006). Advanced methods and tools for ECG data analysis (pp. 55-57). London: Artech house.
[95]  Cost, A. A., & Cano, G. G. (1989, November). QRS detection based on hidden Markov modeling. In Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in (pp. 34-35). IEEE.
[96]  Petrutiu, S., Ng, J., Nijm, G. M., Al-Angari, H., Swiryn, S., & Sahakian, A. V. (2006). Atrial fibrillation and waveform characterization. Engineering in Medicine and Biology Magazine, IEEE, 25(6), 24-30.
[97]  Coast, D. A., Stern, R. M., Cano, G. G., & Briller, S. A. (1990). An approach to cardiac arrhythmia analysis using hidden Markov models. Biomedical Engineering, IEEE Transactions on37(9), 826-836.
[98]  Krimi, S., Ouni, K., & Ellouze, N. (2008, April). An Approach Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation. InInformation and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on (pp. 1-6). IEEE.
[99]  Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-time signal processing (Vol. 5). Upper Saddle River: Prentice Hall.
[100]  Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transforms. Biomedical Engineering, IEEE Transactions on, 42(1), 21-28.
[101]  Taubin, G., Zhang, T., & Golub, G. (1996). Optimal surface smoothing as filter design (pp. 283-292). Springer Berlin Heidelberg.
[102]  Chu, C. H., & Delp, E. J. (1989). Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators.Biomedical Engineering, IEEE Transactions on, 36(2), 262-273.
[103]  Chen, Y., & Duan, H. (2006, January). A QRS complex detection algorithm based on mathematical morphology and envelope. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the (pp. 4654-4657). IEEE.
[104]  Zhang, F., & Lian, Y. (2007, August). Electrocardiogram QRS detection using multiscale filtering based on mathematical morphology. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE  (pp. 3196-3199). IEEE.
[105]  Zhang, F., & Lian, Y. (2011). QRS detection based on morphological filter and energy envelope for applications in body sensor networks. Journal of Signal Processing Systems, 64(2), 187-194.
[106]  Zhang, C. F., & Bae, T. W. (2012). VLSI friendly ECG QRS complex detector for body sensor networks. Emerging and Selected Topics in Circuits and Systems, IEEE Journal on, 2(1), 52-59.
[107]  Bracewell, R. N., & Bracewell, R. N. (1986). The Fourier transform and its applications (Vol. 31999). New York: McGraw-Hill.
[108]  Henning, C. (2002). The Principles of Software QRS Detection. IEEE Engineering in Medicine and Biology, 21, 42-57.
[109]  Pantelopoulos, A., & Bourbakis, N. G. (2010). Prognosis-a wearable health-monitoring system for people at risk: methodology and modeling. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society, 14(3), 613-621.
[110]  Addison, P. S. (2010). The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC Press.
[111]  Coifman, R. R., & Donoho, D. L. (1995). Translation-invariant de-noising (pp. 125-150). Springer New York.
[112]  Mukhopadhyay, S., Biswas, S., Roy, A. B., & Dey, N. (2012). Wavelet Based QRS Complex Detection of ECG Signal. arXiv preprint arXiv:1209.1563.
[113]  Martínez, J. P., Almeida, R., Olmos, S., Rocha, A. P., & Laguna, P. (2004). A wavelet-based ECG delineator: evaluation on standard databases. Biomedical Engineering, IEEE Transactions on, 51(4), 570-581.
[114]  Baas, T., Gravenhorst, F., Fischer, R., Khawaja, A., & Dossel, O. (2010, September). Comparison of three t-wave delineation algorithms based on wavelet filterbank, correlation and pca. In Computing in Cardiology, 2010 (pp. 361-364). IEEE.
[115]  Tamil, E. B. M., Kamarudin, N. H., Salleh, R., & Tamil, A. M. (2008, January). A Review on Feature Extraction & Classification Techniques for Biosignal Processing (Part I: Electrocardiogram). In 4th Kuala Lumpur International Conference on Biomedical Engineering 2008 (pp. 107-112). Springer Berlin Heidelberg.
[116]  Jaswal, G., Parmar, R., & Kaul, A. (2012). QRS Detection Using Wavelet Transform. International Journal of Engineering and Advanced Technology (IJEAT), 1.
[117]  Ghaffari, A., Golbayani, H., & Ghasemi, M. (2008). A new mathematical based QRS detector using continuous wavelet transform. Computers & Electrical Engineering, 34(2), 81-91.
[118]  Reddy, G. U., Muralidhar, M., & Varadarajan, S. (2009). ECG De-Noising using improved thresholding based on Wavelet transforms. IJCSNS, 9(9), 221.
[119]  Chapron, B., & Bliven, L. (1989, July). Wavelet analysis introduction and application to radar scattering from water waves. In Geoscience and Remote Sensing Symposium, 1989. IGARSS'89. 12th Canadian Symposium on Remote Sensing., 1989 International (Vol. 3, pp. 1474-1477). IEEE.
[120]  Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological measurement, 26(5), R155.
[121]  Clifford, G. D., & Azuaje, F. (2006). Advanced methods and tools for ECG data analysis (pp. 55-57). London: Artech house.
[122]  Mallat, S. (1999). A wavelet tour of signal processing. Access Online via Elsevier.
[123]  Hongyan, X., & Minsong, H. (2008, May). A new QRS detection algorithm based on empirical mode decomposition. In Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on (pp. 693-696). IEEE.
[124]  Tang, J. T., Yang, X. L., Xu, J. C., Tang, Y., Zou, Q., & Zhang, X. K. (2008, October). The Algorithm of R peak detection in ECG based on empirical Mode Decomposition. In Natural Computation, 2008. ICNC'08. Fourth International Conference on (Vol. 5, pp. 624-627). IEEE.
[125]  Arafat, A., & Hasan, K. (2009, April). Automatic detection of ECG wave boundaries using empirical mode decomposition. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on (pp. 461-464). IEEE.
[126]  Oweis, R. J., & Abdulhay, E. W. (2011). Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomedical engineering online, 10(1), 38.
[127]  Zhang, Q. (2010). Cuff-free blood pressure estimation using signal processing techniques (Doctoral dissertation, University of Saskatchewan).
[128]  Hadj Slimane, Z. E., & Naït-Ali, A. (2010). QRS complex detection using Empirical Mode Decomposition. Digital Signal Processing, 20(4), 1221-1228.
[129]  Kim, D. H., KIM, J., & Youn, C. H. (2009). Poincare Geometry-Characterized Arrhythmia Identification Scheme in Grid. International journal of engineering science and technology, 1(3).
[130]  Salahuddin, L., & Kim, D. (2006). Detection of acute stress by heart rate variability using a prototype mobile ECG sensor. Hybrid Information Technology, ICHIT, 6, 453-459.
[131]  Sovilj, S., Jeras, M., & Magjarevic, R. (2004, May). Real time P-wave detector based on wavelet analysis. In Electrotechnical Conference, 2004. MELECON 2004. Proceedings of the 12th IEEE Mediterranean (Vol. 1, pp. 403-406). IEEE.
[132]  Miranda, A. A., Le Borgne, Y. A., & Bontempi, G. (2008). New routes from minimal approximation error to principal components. Neural Processing Letters, 27(3), 197-207.
[133]  Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459.
[134]  Sörnmo, L., & Laguna, P. (2005). Bioelectrical signal processing in cardiac and neurological applications [electronic resource]. Academic Press.
[135]  Khawaja, A. (2006). Automatic ECG analysis using principal component analysis and wavelet transformation. Univ.-Verlag Karlsruhe.
[136]  Teodorescu, H. N., & Bonciu, C. (1996, August). Feedforward neural filter with learning in features space. Preliminary results. In Neuro-Fuzzy Systems, 1996. AT'96., International Symposium on (pp. 17-24). IEEE.
[137]  Costa, E. V., & Moraes, J. C. T. B. (2000). QRS feature discrimination capability: quantitative and qualitative analysis. In Computers in Cardiology 2000 (pp. 399-402). IEEE.
[138]  Stamkopoulos, T., Diamantaras, K., Maglaveras, N., & Strintzis, M. (1998). ECG analysis using nonlinear PCA neural networks for ischemia detection.Signal Processing, IEEE Transactions on, 46(11), 3058-3067.
[139]  Vargas, F., Lettnin, D., de Castro, M. C. F., & Macarthy, M. (2002). Electrocardiogram pattern recognition by means of MLP network and PCA: A case study on equal amount of input signal types. In Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on (pp. 200-205). IEEE.
[140]  Fatemian, S. Z., & Hatzinakos, D. (2009, July). A new ECG feature extractor for biometric recognition. In Digital Signal Processing, 2009 16th International Conference on (pp. 1-6). IEEE.
[141]  Martis, R. J., Chakraborty, C., & Ray, A. K. (2009). A two-stage mechanism for registration and classification of ECG using Gaussian mixture model. Pattern Recognition, 42(11), 2979-2988.
[142]  Upasani, D. E., & Kharadkar, R. D. Automated ECG Diagnosis. IOSR Journal of Engineering Vol.2(5), pp: 1265-1269, May. 2012.
[143]  Yeh, Y. C., Wang, W. J., & Chiou, C. W. (2009). Heartbeat case determination using fuzzy logic method on ECG signals. International Journal of Fuzzy Systems, 11(4), 250-261.
[144]  Qidwai, U., & Shakir, M. (2012). Filter Bank Approach to Critical Cardiac Abnormalities Detection using ECG data under Fuzzy Classification. in International Journal of Computer Information Systems & Industrial Management Applications (July 2012) ISSN, 2150-7988.
[145]  Hasnain, S. K. U., & Asim, S. M. (1999). Artificial Neural network in Cardiology-ECG Wave Analysis and Diagnosis Using Backpropogation Neural network.
[146]  Nocedal, J., & Wright, S. (2006). Numerical optimization, series in operations research and financial engineering. Springer, New York.
[147]  Jing-tian, T., Qing, Z., Yan, T., Bin, L., & Xiao-kai, Z. (2007, July). Hilbert-Huang transform for ECG de-noising. In Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on (pp. 664-667). IEEE.
[148]  Levenberg, K. (1944). A method for the solution of certain problems in least squares. Quarterly of applied mathematics, 2, 164-168.
[149]  Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial & Applied Mathematics, 11(2), 431-441.
[150]  Jadhav, S. M., Nalbalwar, S. L., & Ghatol, A. A. (2011). Modular neural network based arrhythmia classification system using ECG signal data. International Journal of Information Technology and Knowledge Management, 4(1), 205-209.
[151]  George Qi Gao. Computerized detection and classification of five cardiac conditions. Thesis submitted in partial fulfillment of the degree of master of engineering, Auckland university of technology, New Zealand, May, 2003.
[152]  Gupta, K. O., & Chatur, D. P. (2012). ECG Signal Analysis and Classification using Data Mining and Artificial Neural Networks. Int. J. Emerg. Technol. Adv. Eng, 2, 56-60.
[153]  Golpayegani, G. N., & Jafari, A. H. (2009). A novel approach in ECG beat recognition using adaptive neural fuzzy filter. Journal of Biomedical Science and Engineering, 2(2), 80-85.
[154]  Bogdanova Vandergheynst, I., Vallejos, R., Javier, F., & Atienza Alonso, D. A Multi-Lead ECG Classification Based on Random Projection Features. 37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), pp. 625-628 New York: IEEE Press.
[155]  Bailón, R., Laguna, P., Mainardi, L., & Sornmo, L. (2007, August). Analysis of heart rate variability using time-varying frequency bands based on respiratory frequency. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 6674-6677). IEEE.
[156]  Seyd, P. A., Ahamed, V. T., Jacob, J., & Joseph, P. (2008). Time and frequency domain analysis of heart rate variability and their correlations in diabetes mellitus. International Journal of Biological and Life Sciences, 1.
[157]  Camm, A. J., Malik, M., Bigger, J. T., Breithardt, G., Cerutti, S., Cohen, R. J., & Singer, D. H. (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93(5), 1043-1065.
[158]  Kleiger, R. E., Miller, J. P., Bigger Jr, J. T., & Moss, A. J. (1987). Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. The American journal of cardiology, 59(4), 256-262.
[159]  Kleiger, R. E., Stein, P. K., Bosner, M. S., & Rottman, J. N. (1992). Time domain measurements of heart rate variability. Cardiology clinics, 10(3), 487.
[160]  Nolan, J., Batin, P. D., Andrews, R., Lindsay, S. J., Brooksby, P., Mullen, M., & Fox, K. A. (1998). Prospective study of heart rate variability and mortality in chronic heart failure results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-Heart). Circulation, 98(15), 1510-1516.
[161]  Corrales, M. M., de la Cruz Torres, B., Esquivel, A. G., Salazar, M. A. G., & Orellana, J. N. (2012). Normal values of heart rate variability at rest in a young, healthy and active Mexican population. Health, 4(7), 377-385.
[162]  Uehara, A., Kurata, C., Sugi, T., Mikami, T., & Shouda, S. (1999). Diabetic cardiac autonomic dysfunction: parasympathetic versus sympathetic. Annals of nuclear medicine, 13(2), 95-100.
[163]  Cugini, P., Bernardini, F., Cammarota, C., Cipriani, D., Curione, M., De Laurentis, T., & Napoli, A. (2001). Is a Reduced Entropy in Heart Rate Variability an Early Finding of Silent Cardiac Neurovegetative Dysautonomia in Type 2 Diabetes Mellitus? Journal of Clinical and Basic Cardiology, 4(4), 289-294.
[164]  Jelinek, H., Flynn, A., & Warner, P. (2004). Automated assessment of cardiovascular disease associated with diabetes in rural and remote health care practice. In The national SARRAH conference (pp. 1-7).
[165]  Tian, L., & Tompkins, W. J. (1997, November). Time domain based algorithm for detection of ventricular fibrillation. In Engineering in Medicine and Biology society, 1997. Proceedings of the 19th Annual International Conference of the IEEE (Vol. 1, pp. 374-377). IEEE.
[166]  Najarian, K., & Splinter, R. (2012). Biomedical signal and image processing. CRC press.
[167]  Manikandan, M. S., & Soman, K. P. (2012). A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomedical Signal Processing and Control, 7(2), 118-128.
[168]  Farrell, T. G., Bashir, Y., Cripps, T., Malik, M., Poloniecki, J., Bennett, E. D., & Camm, A. J. (1991). Risk stratification for arrhythmic events in postinfarction patients based on heart rate variability, ambulatory electrocardiographic variables and the signal-averaged electrocardiogram.Journal of the American College of Cardiology, 18(3), 687-697.
[169]  Malik, M., Farrell, T., Cripps, T., & Camm, A. J. (1989). Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. European heart journal, 10(12), 1060-1074.
[170]  Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., & Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Physical Review E, 66(2), 026702.
[171]  Majercak, I. (2002). The use of heart rate variability in cardiology. Bratislavske lekarske listy, 103(10), 368-377.
[172]  EK, F. S., & jakovljević, m. (2002). Heart rate variability-a shape analysis of Lorenz plots. cell. mol. biol. lett, 7(1), 160.
[173]  Sevšek, F., & Gomiščcek, G. (2004). Shape determination of attached fluctuating phospholipid vesicles. Computer methods and programs in biomedicine, 73(3), 189-194.
[174]  Woo, M. A., Stevenson, W. G., Moser, D. K., Trelease, R. B., & Harper, R. M. (1992). Patterns of beat-to-beat heart rate variability in advanced heart failure.American heart journal, 123(3), 704-710.
[175]  Kamen, P. W., Krum, H., & Tonkin, A. M. (1996). Poincare plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clinical science, 91(Pt 2), 201-208.
[176]  Tulppo, M. P., Makikallio, T. H., Takala, T. E., Seppanen, T. H. H. V., & Huikuri, H. V. (1996). Quantitative beat-to-beat analysis of heart rate dynamics during exercise. American Journal of Physiology-Heart and Circulatory Physiology, 271(1), H244-H252.
[177]  Bogaert, C., Beckers, F., Ramaekers, D., & Aubert, A. E. (2001). Analysis of heart rate variability with correlation dimension method in a normal population and in heart transplant patients. Autonomic Neuroscience, 90(1), 142-147.
[178]  Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Doyne Farmer, J. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 58(1), 77-94.
[179]  Small, M., Yu, D., & Harrison, R. G. (2001). Surrogate test for pseudoperiodic time series data. Physical Review Letters, 87(18), 188101.
[180]  Cohen, M. E., Hudson, D. L., & Deedwania, P. C. (1996). Applying continuous chaotic modeling to cardiac signal analysis. Engineering in Medicine and Biology Magazine, IEEE, 15(5), 97-102.
[181]  Grassberger, P., & Procaccia, I. (1983). Measuring the strangeness of strange attractors. Physica D: Nonlinear Phenomena, 9(1), 189-208.
[182]  Judd, K. (1992). An improved estimator of dimension and some comments on providing confidence intervals. Physica D: Nonlinear Phenomena, 56(2), 216-228.
[183]  Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1), 117-134.
[184]  Uzun, I. S., Asyali, M. H., Celebi, G., & Pehlivan, M. (2001). Nonlinear analysis of heart rate variability. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (Vol. 2, pp. 1581-1584). IEEE.
[185]  Sun, Y., Chan, K. L., & Krishnan, S. M. (2000). Arrhythmia detection and recognition in ECG signals using nonlinear techniques. Ann. Biomed. Eng, 28, 32-37.
[186]  Eckmann, J. P., & World Scientific Series on Nonlinear Science Series A, 16, 365-404.
[187]  Gomes, M. E. D., Souza, A. V. P., Guimaraes, H. N., & Aguirre, L. A. (2000). Investigation of determinism in heart rate variability. Chaos: An Interdisciplinary Journal of Nonlinear Science, 10(2), 398-410.
[188]  Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors.Physical review letters, 50(5), 346-349.
[189]  Mandelbrot, B. B. (1983). The fractal geometry of nature/Revised and enlarged edition. New York, WH Freeman and Co., 1983, 495 p., 1.
[190]  Backes, A. R., & Bruno, O. M. (2008). Fractal and Multi-Scale Fractal Dimension analysis: a comparative study of Bouligand-Minkowski method.image, 11, 8.
[191]  Karim, N., Hasan, J. A., & Ali, S. S. (2011). Heart rate variability–a review. J. Basic Appl. Sci, 7, 71-77.
[192]  Schroeder, M. R. (2012). Fractals, chaos, power laws: Minutes from an infinite paradise. Courier Dover Publications.
[193]  C Tricot, C. (1995). Curves and fractal dimension. Springer.
[194]  Costa, L. D. F. D., & Cesar Jr, R. M. (2000). Shape analysis and classification: theory and practice. CRC Press, Inc..
[195]  Higuchi, T. (1988). Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena, 31(2), 277-283.
[196]  Katz, M. J. (1988). Fractals and the analysis of waveforms. Computers in biology and medicine, 18(3), 145-156.
[197]  Kraft, R. (1995). Fractals and dimensions. Munich University of Technology.
[198]  Krstacic, G., Krstacic, A., Martinis, M., Vargovic, E., Knezevic, A., Smalcelj, A., & Smuc, T. (2002, September). Non-linear analysis of heart rate variability in patients with coronary heart disease. In Computers in Cardiology, 2002 (pp. 673-675). IEEE.
[199]  Acharya, R., Kannathal, N., & Krishnan, S. M. (2004). Comprehensive analysis of cardiac health using heart rate signals. Physiological measurement, 25(5), 1139.
[200]  Acharya, U. R., Kannathal, N., Sing, O. W., Ping, L. Y., & Chua, T. (2004). Heart rate analysis in normal subjects of various age groups. Biomed Eng Online, 3(1), 24.
[201]  Pincus, S. M., & Viscarello, R. R. (1992). Approximate entropy: a regularity measure for fetal heart rate analysis. Obstetrics & Gynecology, 79(2), 249-255.
[202]  Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297-2301.
[203]  Goldberger, A. L., Mietus, J. E., Rigney, D. R., Wood, M. L., & Fortney, S. M. (1994). Effects of head-down bed rest on complex heart rate variability: response to LBNP testing. Journal of Applied Physiology, 77(6), 2863-2869.
[204]  Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049.
[205]  Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049.
[206]  Huikuri, H. V., Mäkikallio, T. H., Peng, C. K., Goldberger, A. L., Hintze, U., & Møller, M. (2000). Fractal correlation properties of RR interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation, 101(1), 47-53.
[207]  Peng, C. K., Havlin, S., Hausdorff, J. M., Mietus, J. E., Stanley, H. E., & Goldberger, A. L. (1995). Fractal mechanisms and heart rate dynamics: long-range correlations and their breakdown with disease. Journal of electrocardiology, 28, 59-65.
[208]  Mäkikallio, T. H., Høiber, S., Køber, L., Torp-Pedersen, C., Peng, C. K., Goldberger, A. L., & Huikuri, H. V. (1999). Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. The American journal of cardiology,83(6), 836-839.
[209]  Eckmann, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhys. Lett, 4(9), 973-977.
[210]  Chua, K. C., Chandran, V., Acharya, U. R., & Lim, C. M. (2008). Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry. Journal of medical engineering & technology, 32(4), 263-272.
[211]  Kobayashi, M., & Musha, T. (1982). 1/f fluctuation of heartbeat period.Biomedical Engineering, IEEE Transactions on, (6), 456-457.
[212]  Saul, J. P., Albrecht, P., Berger, R. D., & Cohen, R. J. (1987). Analysis of long term heart rate variability: methods, 1/f scaling and implications. Computers in cardiology, 14, 419-422.
[213]  Lombardi, F. (2000). Chaos theory, heart rate variability, and arrhythmic mortality. Circulation, 101(1), 8-10.
[214]  Bigger, J. T., Steinman, R. C., Rolnitzky, L. M., Fleiss, J. L., Albrecht, P., & Cohen, R. J. (1996). Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. Circulation, 93(12), 2142-2151.