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Abstract

In this paper, an asynchronous demodulation method for a four-single sideband (SSB) signal arranged on the frequency axis is developed to support burst mode transmission in a mobile radio path and to achieve greater data throughputs. When a reduced pilot carrier is placed at the center of the 4-SSB signal, it is guarded by lower and upper sidebands, that is, this scheme is classified into a tone-in-band (TIB) system. Digital signal processing (DSP) processors are useful for implementing a Hilbert transform. However, we have for a long time neglected introducing it into the demodulation process of SSB signals.
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Bibliography

[1] W. C. Jakes, Ed., Microwave Mobile Communications, IEEE Press, 1993, ISBN 0-7803-1069-1
[2] K. Daikoku and K. Suwa, “RZ SSB transceiver with equal-gain combiner for speech and data transmission,” Proc. IEEE GLOBECOM’88, Hollywood, FL, USA, pp.26.4.1-26.4.5, 1988, DOI: 10.1109/GLOCOM.1988.25953
[3] K. Daikoku, “Field test results on JPEG/text file transmission employing RZ SSB transceivers through HF radio channels,” IEE Proc. Communi., vol. 151, no. 1, pp.50-58, 2004, DOI: 10.1049/ip-com:20040347
[4] K. Daikoku, “Hilbert transform applications in asynchronous demodulation for real zero single sideband signals in mobile radio path,” J. Signal Process., vol. 25, no. 1, pp. 11–24, Jan. 2021. DOI: 10.2299/jsp.25.11.
[5] S. L. Hahn, Hilbert Transforms in Signal Processing, Artech House, 1996, ISBN 0-9006-886-0
[6] W. T. Webb and L. Hanzo, Modern Quadrature Amplitude Modulation, Pentech Press, 1994, ISBN 0-7273-1701-6
[7] N. C. Davies, “Digital radio and its application in HF (2-30 MHz) band,” Doctor Thesis to the University of Leeds, May 2004.
[8] R. C. Daniels and S. W. Peters, “A new MIMO HF data link: Designing for high data rates and backwards compatibility,” 2013 IEEE MILCOM, San Diego, CA, USA, pp.1-6, 2013, DOI: 1 10.1109/MILCOM.2013.214
[9] M. Kuzlu, H. Dinçer and S. Öztürk, “DSP implementation of underwater communication using SSB modulation with random carrier frequencies,” Sci. Res. Essays, vol. 5, no. 10, pp.1084-1099, 2010, ISSN 1992-2248
[10] T. S. Rappaport, Wireless Communications, Prentice Hall, 1996, ISBN 0-13-461088
[11] S. Sampei, S. Komaki and N. Morinaga, “Adaptive modulation/TDMA scheme for large capacity personal multi-media communication systems,” IEICE Trans. Communi., vol. E77-B, no. 9 pp.1096-1103, 1994.
[12] G. Ohta, M. Nanri, M. Uesugi, T. Sato, H. Tominaga, “A study of new modulation method consisted of orthogonal four SSB elements having a common carrier frequency,” The 11th International Symposium on Wireless Personal Multimedia Communications (WPMC 2008), Lapland, Finland, 8–11 Sep. 2008.
[13] M. Nanri, “Transmitter and SSB signal generation method,” US Patent Application Publication, Pub. No.: US 2010/0246710 A1, Pub. Date: Sep. 30, 2010.
[14] A. M. Mustafa, Q. N. Nguyen, T. Sato and G. Ohta, “Four single-sideband M-QAM modulation using soft input soft output equalizer over OFDM,” 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), Sydney, NSW, Australia, 21-23 Nov. 2018, DOI: 10.1109/ATNAC.2018.8615451
[15] M. M. Alhasani, Q. N. Nguyen, G. Ohta, and T. Sato, “A novel four singlesideband M-QAM modulation scheme using a shadow equalizer for MIMO system toward 5G communications,” Sensors, 2019, 9, 1944; DOI: 10.3390/s19081944
[16] B. Pitakdumrongkija, H. Suzuki, S. Suyama and K. Fukawa, "Single sideband QPSK with turbo equalization for mobile communications," 2005 IEEE 61st VTC’05, Stockholm, Sweden, pp. 538-542, 30 May-1 June 2005, DOI: 10.1109/VETECS.2005.1543349
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Authors and Affiliations

Kazuhiro Daikoku
1

  1. Tokyo, Japan
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Abstract

Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.
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Bibliography

[1] Chylinski, M., & M., Szmajda, M. (2018). Statistical methods for analysing deceleration and acceleration capacity of the heart rate. In Hunek, W., & Paszkiel, S. (Eds.). Advances in Intelligent Systems and Computing: Vol. 720. Biomedical Engineering and Neuroscience. (pp. 85–97). Springer. https://doi.org/10.1007/978-3-319-75025-5_9
[2] Siecinski, S.,Kostka, P. S.,&Tkacz, E. J. (2020). Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms on healthy volunteers. Sensors, 20(16), 4522. https://doi.org/ 10.3390/s20164522
[3] Acharya, R. U., Joseph, P. K., Kannathal, N., Choo, M. L., & Suri, J. S. (2006). Heart rate variability: A review. Medical and Biological Engineering and Computing, 44(12), 1031–1051. https://doi.org/10.1007/s11517-006-0119-0
[4] Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258
[5] Goldoozian L. S., Zahedi, E., & Zarzoso, V. (2017). Time-varying assessment of heart rate variability parameters using respiratory information. Computers in Biology and Medicine, 89, 355–367. https://doi.org/10.1016/j.compbiomed.2017.07.022
[6] Boardman, A., Schlindwein, F. S., Rocha, A. P., & Leite, A. (2002). A study on the optimum order of autoregressive models for heart rate variability. Physiological Measurement, 23(2), 325–336. https://doi.org/10.1088/0967-3334/23/2/308
[7] Karim, N., Hasan, J. A., & Ali, S. S. (2011). Heart rate variability – A review. Australian Journal of Basic and Applied Sciences, 7(1), 71–77.
[8] Stein, P. K., & Pu, Y. (2012). Heart rate variability, sleep and sleep disorders. Sleep Medicine Reviews, 16(1), 47–66. https://doi.org/10.1016/j.smrv.2011.02.005
[9] Bernardi, L., Valle, F., Coca, M., Calciati, A., & Sleight, P. (1996). Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. Cardiovascular Research, 32(2), 234–237. https://doi.org/10.1016/0008-6363(96)00081-8
[10] Aoki, K., Stephens, D. P., & Johnson, J. M. (2001). Diurnal variation in cutaneous vasodilator and vasoconstrictor systems during heat stress. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 281(2), 591–595. https://doi.org/10.1152/ajpregu.2001.281.2.R591
[11] Fleisher, L. A., Frank, S. M., Sessler, D. I., Cheng, C., Matsukawa, T., & Vannier, C. A. (1996). Thermoregulation and heart rate variability. Clinical Science, 90(2), 97–103. https://doi.org/10.1042/cs0900097
[12] Akselrod. S., Gordon, D., Ubel. F. A., Shannon, D. C., Barger, A. C., & Cohen, R. J. (1981). Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science, 213(4504), 220–222. https://doi.org/10.1126/science.6166045
[13] Porter, G. A., Jr., & Rivkees, S. A. (2001). Ontogeny of humoral heart rate regulation in the embryonic mouse. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 281(2), 401–407. https://doi.org/10.1152/ajpregu.2001.281.2.r401
[14] Stampfer, H. G., & Dimmitt, S. B. (2013). Variations in circadian heart rate in psychiatric disorders: Theoretical and practical implications. ChronoPhysiology and Therapy, 3, 41–50. https://doi.org/10.2147/CPT.S43623
[15] Jelinek, H. F., Huang, Z. Q., Khandoker, A. H., Chang, D., & Kiat, H. (2013). Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG patients. Frontiers in Physiology, 4, 302. https://doi.org/10.3389/fphys.2013.00302
[16] Li, H., Kwong, S., Yang, L., Huang, D., & Xiao, D. (2011). Hilbert-Huang transform for analysis of heart rate variability in cardiac health. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6), 1557–1567. https://doi.org/10.1109/TCBB.2011.43
[17] Task Force of the European Society of Cardiology and the North American Society of Pacing Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
[18] Wen, F., & He, F.-T. (2011). An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability analysis. Journal of Zhejiang University Science B, 12, 976–982. https://doi.org/10.1631/jzus.b1000392
[19] Mendez, M. O., Bianchi, A. M., Matteucci, M., Cerutti, S., & Penzel, T. (2009). Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Transactions on Biomedical Engineering, 56(12), 2838–2850. https://doi.org/10.1109/tbme.2009.2029563
[20] Penzel, T., Kantelhardt, J. W., Grote, L., Peter, J. H., & Bunde, A. (2003). Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Transactions on Biomedical Engineering, 50(10), 1143–1151. https://doi.org/10.1109/TBME.2003.817636
[21] Chan, H. L., Chou, W. S., Chen, S. W., Fang, S. C., Liou, C. S., & Hwang, Y. S. (2005). Continuous and online analysis of heart rate variability. Journal of Medical Engineering and Technology, 29(5), 227–234. https://doi.org/10.1080/03091900512331332587
[22] Kudrynski, K., & Strumillo, P. (2015). Real-time estimation of the spectral parameters of heart rate variability. Biocybernetics and Biomedical Engineering, 35(4), 304–316. https://doi.org/10.1016/ j.bbe.2015.05.002
[23] Echeverria, J. C., Crowe, J. A., Woolfson, M. S., & Hayes-Gill, B. R. (2001). Application of empirical mode decomposition to heart rate variability analysis. Medical and Biological Engineering and Computing, 39(4), 471–479. https://doi.org/10.1007/bf02345370
[24] Billman, G. E. (2011). Heart rate variability – A historical perspective. Frontiers in Physiology, 2, 86. https://doi.org/10.3389/fphys.2011.00086
[25] Romano, M., Faiella, G., Clemente, F., Iuppariello, L., Bifulco, P., & Cesarelli, M. (2016). Analysis of foetal heart rate variability components by means of empirical mode decomposition. IFMBE Proceedings, 57, 71–74. https://doi.org/10.1007/978-3-319-32703-7_15
[26] 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 and Biobehavioral Reviews, 33(2), 71–80. https://doi.org/10.1016/j.neubiorev.2008.07.006
[27] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society of London A, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
[28] Chen, M., He, A., Feng, K., Liu, G., & Wang, Q. (2019). Empirical mode decomposition as a novel approach to study heart rate variability in congestive heart failure assessment. Entropy, 21(12), 1169. https://doi.org/10.3390/e21121169
[29] Balocchi, R., Menicucci, D., Santarcangelo, E., Sebastiani, L., Gemignani, A., Ghelarducci, B., & Varanini, M. (2004). Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition. Chaos, Solitons & Fractals, 20(1), 171–177. https://doi.org/10.1016/S0960-0779(03)00441-7
[30] Ortiz, M. R., Bojorges, E. R., Aguilar, S. D., Echeverria, J. C., Gonzalez-Camarena, R., Carrasco, S., Gaitan, M. J., & Martinez, A. (2005). Analysis of high frequency fetal heart rate variability using empirical mode decomposition. Computers in Cardiology, France, 675–678. https://doi.org/10.1109/ CIC.2005.1588192
[31] Helong, L., Yang, L., & Daren, H. (2008). Application of Hilbert-Huang transform to heart rate variability analysis. 2nd International Conference on Bioinformatics and Biomedical Engineering, China, 648–651. https://doi.org/10.1109/ICBBE.2008.158
[32] Neto, E. P. S., Custaud, M. A., Cejka, J. C., Abry, P., Frutoso, J., Gharib, C., & Flandrin, P. (2004). Assessment of cardiovascular autonomic control by the empirical mode decomposition. Methods of Information in Medicine, 43(1), 60–65. https://doi.org/10.1055/s-0038-1633836
[33] Ihlen, E. A. F. (2009). A comparison of two Hilbert spectral analyses of heart rate variability. Medical & Biological Engineering & Computing, 47(10), 1035–1044. https://doi.org/10.1007/ s11517-009-0500-x
[34] Eleuteri, A., Fisher, A. C., Groves, D., & Dewhurst, C. J. (2012). An efficient time-varying filter for detrending and bandwidth limiting the heart rate variability tachogram without resampling: MATLAB open-source code and internet web-based implementation. Computational and Mathematical Methods in Medicine, 2012, Article 578785. https://doi.org/10.1155/2012/578785
[35] Fisher, A. C., Eleuteri, A., Groves, D., & Dewhurst, C. J. (2012). The Ornstein–Uhlenbeck third-order Gaussian process (OUGP) applied directly to the un-resampled heart rate variability (HRV) tachogram for detrending and low-pass filtering. Medical and Biological Engineering and Computing, 50(7), 737–742. https://doi.org/10.1007/s11517-012-0928-2
[36] Varanini, M., Macerata, A., Emdin, M., & Marchesi, C. (1994). Non linear filtering for the estimation of the respiratory component in heart rate. Computers in Cardiology, USA, 565–568. https://doi.org/10.1109/CIC.1994.470129
[37] Estévez, M., Machado, C., Leisman, G., Estévez-Hernández, T., Arias-Morales, A., Machado, A., & Montes-Brown, J. (2016). Spectral analysis of heart rate variability. International Journal on Disability and Human Development, 15(1), 5–17. https://doi.org/10.1515/ijdhd-2014-0025
[38] McCraty, R., & Shaffer, F. (2015). Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine, 4(1), 46–61. https://doi.org/10.7453/gahmj.2014.073
[39] Nunan, D., Sandercock, G. R. H., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407–1417. https://doi.org/10.1111/j.1540-8159.2010.02841.x
[40] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J. E., Moody, G. B., Peng, C. K.,&Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220. https://doi.org/10.1161/01.cir.101.23.e215
[41] Terzano, M. G., Parrino, L., Sherieri, A., Chervin, R., Chokroverty, S., Guilleminault, C., Hirshkowitz, M., Mahowald, M., Moldofsky, H., Rosa, A., Thomas, R., & Walters, A. (2001). Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Medicine, 2(6), 537–553. https://doi.org/10.1016/s1389-9457(01)00149-6
[42] Jun, S., Szmajda, M., Khoma, V., Khoma, Y., Sabodashko, D., Kochan, O., & Wang, J. (2020). Comparison of methods for correcting outliers in ECG-based biometric identification. Metrology and Measurement Systems, 27(3), 387–398. https://doi.org/10.24425/mms.2020.132784
[43] Hsu, M.-K., Sheu, J.-C., & Hsue, C. (2011). Overcoming the negative frequencies: instantaneous frequency and amplitude estimation using osculating circle method. Journal of Marine Science and Technology, 19(5), 514–521. https://doi.org/10.6119/JMST.201110_19(5).0007
[44] Bayly E. J. (1968). Spectral analysis of pulse frequency modulation in the nervous systems. IEEE Transactions on Biomedical Engineering, 15(4), 257–265. https://doi.org/10.1109/TBME.1968.4502576
[45] Mateo, J., & Laguna, P. (1996). New heart rate variability time-domain signal construction from the beat occurrence time and the IPFM model. Computers in Cardiology, USA, 185–188. https://doi.org/10.1109/CIC.1996.542504
[46] de Boer, R. W., Karemaker, J. M., & Strackee, J. (1985). Spectrum of a series of point events, generated by the integral pulse frequency modulation model. Medical and Biological Engineering and Computing, 23(2), 138–142. https://doi.org/10.1007/BF02456750
[47] Nakao, M., Norimatsu, M., Mizutani, Y., & Yamamoto, M. (1997). Spectral distortion properties of the integral pulse frequency modulation model. IEEE Transactions on Biomedical Engineering, 44(5), 419–426. https://doi.org/10.1109/10.568918


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Authors and Affiliations

Krzysztof Adamczyk
1
Adam G. Polak
1

  1. Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, B. Prusa Str. 53/55, 50-317 Wrocław, Poland

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