Biomedical Science and Engineering

Current Issue» Volume 2, Number 3 (2014)

Article

Sample Entropy based HRV: Effect of ECG Sampling Frequency

1Department of Electronics and Communication Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, India

2Research Scholar Punjab Technical University Jalandhar and Department of Electronics and Communication Engineering, Guru Nanak Dev University Regional Campus, Jalandhar, India

3Amrirtsar College of Engineering and Technology, Amritsar, India


Biomedical Science and Engineering. 2014, 2(3), 68-72
DOI: 10.12691/bse-2-3-3
Copyright © 2014 Science and Education Publishing

Cite this paper:
Butta Singh, Manjit Singh, Vijay Kumar Banga. Sample Entropy based HRV: Effect of ECG Sampling Frequency. Biomedical Science and Engineering. 2014; 2(3):68-72. doi: 10.12691/bse-2-3-3.

Correspondence to: Butta  Singh, Department of Electronics and Communication Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, India. Email: bsl.khanna@gmail.com

Abstract

Biomedical signals carry important information about the behaviour of the living systems under study. A proper processing of these signals in principle enhances their physiological and clinical information. Analysis of variations in the instantaneous heart rate time series using the beat to-beat RR intervals (the RR tachogram) is known as heart rate variability (HRV) analysis. Sample entropy (SampEn), refined version of approximate entropy (ApEn), is a nonlinear complexity measure used to quantify the irregularity of a RR interval time series without biasing. An increase in SampEn is an indicator of increases in complexity. Linear HRV parameters are very sensitive to ECG sampling frequency and low sampling frequency may result in clinically misinterpretation of HRV. In this study consequences of errors in SampEn based HRV induced by ECG sampling frequency have been investigated. The error induced in SampEn based HRV was found to be a function of ECG sampling frequency and RR interval data length. The relative error in SampEn was approximately 3.5%.for medium and long term data (N=500, 1000 respectively) and less than 2% for short term data (N=200) at low ECG sampling frequency of 125 Hz with respect to reference values at 2000 Hz. Therefore the SampEn based HRV indices computed from RR interval time series with low ECG sampling should be regarded with caution. The finding of this study can be partly used as a reference for the optimal ECG sampling frequency for the SampEn-based HRV assessment.

Keywords

References

[[[[[[[[[[[[
[1]  Karim, N., Hasan, J.A. and Syed, S.A, “Heart rate variability-A review,” Journal of Basic and Applied Sciences, 7(1).71-77. 2011.
 
[2]  Acharya, U.R., Kannathal. N. and Krishnan, S.M., “Comprehensive analysis of cardiac health using heart rate signals,” Physiological Measurement, 25. 1130-1151. 2004.
 
[3]  Acharya, U.R., Joseph, K.P., Kannathal, N., Lim C.M. and Suri J.S, “Heart rate variability: a review,” Medical and Biological Engineering and Computing, 44. 1031-1051. 2006.
 
[4]  Kurths, J., Voss, A., Witt, A., Saparin, P., Kleiner, H.J. and Wessel N, “Quantitative analysis of heart rate variability,” Chaos, 5(1). 88-94. 1995.
 
[5]  Task Force of European society of Cardiology and the North American Society of Pacing and Electrophysiology, “Heart rate variability, standard of measurement, physiological interpretations and clinical use,” circulation 93(5). 1043-1065. 1996.
 
Show More References
[6]  Pincus S.M., “Approximate entropy as a measure of system complexity”, Proceeding of National Academy of Sciences, USA, 88(6). 2297-2301. 1991.
 
[7]  Goldberger A.L, “Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside,” The Lancet, 347.1312-1314. 1996.
 
[8]  Lee, B., Jang, J., Lee, J. and Lee M, “Relationship between autonomic nervous system activity and chaotic attractor on biological signal,” Proceedings of IEEE Engineering in Medicine and Biolog , 16th Conf, 1256-1257. 1994.
 
[9]  Loun, W.A., “Variability analysis of physiological signals using nonlinear time series analysis techniques,” Ph.D Thesis PIEAS, Pakistan. 2006.
 
[10]  Pincus S.M., Goldberger A.L., “Physiological time-series analysis: What does Regularity Quantify?” American Journal of Physiology (Heart Circ Physiol), 35.1643-656. 1994.
 
[11]  Singh, B., Singh, D., Jaryal, A.K. and Deepak K.K, “Ectopic beats in approximate entropy and sample entropy-based HRV assessment,” International Journal of Systems Science, 43(5). 884-893. 2012.
 
[12]  Chen, X., Solomon, I.C. and Chon K.H., “Comparison of the use of approximate entropy and sample entropy: application to neural respiratory signal,” Proceedings of IEEE Engineering in Medicine and Biology, 27th International Conf, sanghai, China, 4212-4215. 2005.
 
[13]  Lake, D.E., Richman, J.S., Grin, M.P. and Moorman, J.R, “Sample entropy analysis of neonatal heart rate variability,” American Journal of Physiology-Regulatory Integrative and Comparative Physiology, 283. 789-797. 2002.
 
[14]  Hejjel, L. and Rooth, E., “What is the adequate sampling interval of ECG signal for heart rate variability analysis in time domain,” Physiological Measurements, 25. 1405-1411. 2004.
 
[15]  Ziemsseen, T., Gascg, Z. and Ruediger, H, “Influence of ECG sampling frequency on spectral analysis of RR intervals and baroreflex sensitivity using eurobarvar data,” Journal of Clinical Monitoring and Computing, 22. 159-168. 2008.
 
[16]  Abboud, S. and Barnea O., “Errors due to sampling frequency of electrocardiogram in spectral analysis of heart rate signals with low variability,” Proceedings of IEEE Computers in Cardiology, 461-463. 1995.
 
[17]  Kolmogorov, A.N., “A new metric invariant of transient dynamical systems and automorphisms in Lebesgue spaces,” Dokl. Akad. Nauk SSSR (N.S.), 119.61-864. 1958.
 
Show Less References

Article

Review Article: Non-Invasive Fetal Heart Rate Monitoring Techniques

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


Biomedical Science and Engineering. 2014, 2(3), 53-67
DOI: 10.12691/bse-2-3-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Enas W. Abdulhay, Rami J. Oweis, Asal M. Alhaddad, Fadi N. Sublaban, Mahmoud A. Radwan, Hiyam M. Almasaeed. Review Article: Non-Invasive Fetal Heart Rate Monitoring Techniques. Biomedical Science and Engineering. 2014; 2(3):53-67. doi: 10.12691/bse-2-3-2.

Correspondence to: Rami  J. Oweis, Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid, Jordan. Email: oweis@just.edu.jo

Abstract

Fetal heart rate monitoring is a process carried out during pregnancy and/or labor to keep track of the fetal heart rate and in some devices the uterine contractions. A variety of techniques has been studied and is used on a daily basis in many hospitals. This review discusses and compares the operating principle, the key signal processing techniques, advantages and drawbacks of five of those techniques: fetal electrocardiography (FECG) using abdominal surface electrodes, photoplethysmography (PPG) using near infrared (NIR) light, Doppler ultrasound, ultrasound based cardiotocography (CTG) known as electronic fetal monitoring and fetal magnetocardiography (FMCG). The review leads to the conclusion that the PPG overcomes almost all of the drawbacks of the other methods and thus deserves the most attention in future biomedical research.

Keywords

References

[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[1]  Ungureanu, G. M., Gussi, I., Wolf, W., Taralunga, D., Pasca, S., & Strungaru, R. (2011). Prenatal Telemedicine-Advances in Fetal Monitoring. Advances in Telemedicine: Applications in Various Medical Disciplines and Geographical Regions, 97-120.
 
[2]  Hasan, M. A., Reaz, M. B. I., Ibrahimy, M. I., Hussain, M. S., & Uddin, J. (2009). Detection and processing techniques of FECG signal for fetal monitoring. Biological procedures online, 11 (1), 263-295.
 
[3]  Chez, B. F., & Baird, S. M. (2011). Electronic Fetal Heart Rate Monitoring: Where Are We Now?. The Journal of Perinatal & Neonatal Nursing, 25 (2), 180-192. [10] Pildner von Steinburg, S., Boulesteix, A., Lederer, C., Grunow, S., Schiermeier, S., Hatzmann, W., .. & Daumer, M. (2012). What is the “normal” fetal heart rate? PeerJ, 1.
 
[4]  Neilson, J. P. (2006). Fetal electrocardiogram (ECG) for fetal monitoring during labour. Cochrane Database Syst Rev, 3.
 
[5]  Freeman, R. K., Garite, T. J., Nageotte, M. P., & Miller, L. A. (2012). Fetal heart rate monitoring. Lippincott Williams & Wilkins.
 
Show More References
[6]  Peters, C. H., ten Broeke, E. D., Andriessen, P., Vermeulen, B., Berendsen, R. C., Wijn, P. F., & Oei, S. G. (2004). Beat-to-beat detection of fetal heart rate: Doppler ultrasound cardiotocography compared to direct ECG cardiotocography in time and frequency domain. Physiological measurement, 25 (2), 585.
 
[7]  Vullings, R., Peters, C., Mischi, M., Oei, G., & Bergmans, J. (2006, August). Maternal ECG removal from non-invasive fetal ECG recordings. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE (pp. 1394-1397). IEEE.
 
[8]  Hon, E. H. (1960). Apparatus for continuous monitoring of the fetal heart rate. The Yale Journal of Biology and Medicine, 32 (5), 397.
 
[9]  Feinstein, N., Torgersen, K. L., & Atterbury, J. (1993). Fetal Heart Monitoring: Principles and Practices. Kendall Hunt.
 
[10]  Jezewski, J., Roj, D., Wrobel, J., & Horoba, K. (2011). A novel technique for fetal heart rate estimation from Doppler ultrasound signal. Biomedical engineering online, 10 (1), 1-17.
 
[11]  Black, R. S., & Campbell, S. (1997). Cardiotocography versus Doppler. Ultrasound in Obstetrics & Gynecology, 9 (3), 148-151.
 
[12]  Peters, M., Crowe, J., Piéri, J. F., Quartero, H., Hayes-Gill, B., James, D. & Shakespeare, S. (2001). Monitoring the fetal heart non-invasively: a review of methods. Journal of perinatal medicine, 29 (5), 408-416.
 
[13]  Strasburger, J. F., Cheulkar, B., & Wakai, R. T. (2008). Magnetocardiography for fetal arrhythmias. Heart rhythm: the official journal of the Heart Rhythm Society, 5 (7), 1073.
 
[14]  Sameni, R., & Clifford, G. D. (2010). A review of fetal ECG signal processing; issues and promising directions. The open pacing, electrophysiology & therapy journal, 3, 4.
 
[15]  Pildner von Steinburg, S., Boulesteix, A., Lederer, C., Grunow, S., Schiermeier, S., Hatzmann, W. & Daumer, M. (2012). What is the “normal” fetal heart rate?. PeerJ, 1.
 
[16]  Adam, J. (2012). The Future of Fetal Monitoring. Reviews in Obstetrics and Gynecology, 5 (3-4), e132.
 
[17]  Clifford, G., Sameni, R., Ward, J., Robinson, J., & Wolfberg, A. J. (2011). Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors. American journal of obstetrics and gynecology, 205 (1), 47-e1.
 
[18]  Graatsma, E. M., Jacod, B. C., Van Egmond, L. A. J., Mulder, E. J. H., & Visser, G. H. A. (2009). Fetal electrocardiography: feasibility of long‐term fetal heart rate recordings. BJOG: An International Journal of Obstetrics & Gynaecology, 116 (2), 334-338.
 
[19]  Graatsma, E. M. (2010). Monitoring of fetal heart rate and uterine activity.
 
[20]  Algunaidi, M. S. M., Ali, M. M., Gan, K. B., & Zahedi, E. (2009). Fetal heart rate monitoring based on adaptive noise cancellation and maternal QRS removal window. European Journal of Scientific Research, ISSN, 565-575.
 
[21]  Goell, P., Rai, S., Chandra, M., & Gupta, V. K. (2013). Analysis of LMS Algorithm in Wavelet Domain.
 
[22]  Unser, M., & Aldroubi, A. (1996). A review of wavelets in biomedical applications. Proceedings of the IEEE, 84 (4), 626-638.
 
[23]  Karvounis, E. C., Papaloukas, C., Fotiadis, D. I., & Michalis, L. K. (2004, September). Fetal heart rate extraction from composite maternal ECG using complex continuous wavelet transform. In Computers in Cardiology, 2004 (pp. 737-740). IEEE.
 
[24]  Ghodsi, M., Hassani, H., & Sanei, S. (2010). Extracting fetal heart signal from noisy maternal ECG by singular spectrum analysis. Journal of Statistics and its Interface, Special Issue on the Application of SSA, 3 (3), 399-411.
 
[25]  Zhongliang, L. U. O. (2012). Fetal Electrocardiogram Extraction using Blind Source Separation and Empirical Mode Decomposition⋆. Journal of Computational Information Systems, 8 (12), 4825-4833.
 
[26]  Taylor, M. J., Smith, M. J., Thomas, M., Green, A. R., Cheng, F., Oseku‐Afful, S.,. .. & Gardiner, H. M. (2003). Non‐invasive fetal electrocardiography in singleton and multiple pregnancies. BJOG: An International Journal of Obstetrics & Gynaecology, 110 (7), 668-678.
 
[27]  Zheng, W., Liu, H., He, A., Ning, X., & Cheng, J. (2010). Single-lead fetal electrocardiogram estimation by means of combining R-peak detection, resampling and comb filter. Medical engineering & physics, 32 (7), 708-719.
 
[28]  Sun, Y., Hu, S., Azorin-Peris, V., Greenwald, S., Chambers, J., & Zhu, Y. (2011). Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise. Journal of Biomedical Optics, 16 (7), 077010-077010.
 
[29]  Sree, T. H., Garimella, M., Bandari, A., & Patel, I. Microcontroller Based Fetal Heart Rate Monitoring using Intelligent Biosystem.
 
[30]  Nitzan, M., Khanokh, B., & Slovik, Y. (2002). The difference in pulse transit time to the toe and finger measured by photoplethysmography. Physiological measurement, 23 (1), 85.
 
[31]  Gan, K. B., Zahedi, E., & Ali, M. M. (2011). Investigation of optical detection strategies for transabdominal fetal heart rate detection using three-layered tissue model and Monte Carlo simulation. Optica Applicata, 41 (4), 885-896.
 
[32]  Gan, K. B., Zahedi, E., & Ali, M. A. M. (2009). Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results. Biomedical Engineering, IEEE Transactions on, 56 (8), 2075-2082.
 
[33]  Gan, K. B., Zahedi, E., & Ali, M. A. M. (2011). Application of Adaptive Noise Cancellation in Transabdominal Fetal Heart Rate Detection Using Photoplethysmography.
 
[34]  Apolinário Jr, J. A., & Netto, S. L. (2009). Introduction to Adaptive Filters. InQRD-RLS Adaptive Filtering (pp. 1-27). Springer US.
 
[35]  Gan, K. B., Zahedi, E., & Ali, M. M. (2007, January). Feasibility of Fetal Photoplethysmography Signal Extraction using Adaptive Noise Cancelling. In3rd Kuala Lumpur International Conference on Biomedical Engineering 2006 (pp. 387-390). Springer Berlin Heidelberg.
 
[36]  Zourabian, A., Siegel, A., Chance, B., Ramanujam, N., Rode, M., & Boas, D. A. (2000). Trans-abdominal monitoring of fetal arterial blood oxygenation using pulse oximetry. Journal of biomedical optics, 5 (4), 391-405.
 
[37]  Ramanujam, N., Vishnoi, G., Hielscher, A. H., Rode, M., Forouzan, I., & Chance, B. (2000). Photon migration through fetal head in utero using continuous wave, near infrared spectroscopy: clinical and experimental model studies. Journal of Biomedical Optics, 5 (2), 173-184.
 
[38]  Gan, K. B., Ali, M. M., & Zahedi, E. (2008, January). Two Channel Abdominal PPG Instrumentation. In 4th Kuala Lumpur International Conference on Biomedical Engineering 2008 (pp. 691-693). Springer Berlin Heidelberg.
 
[39]  Jumadi, N. A., Gan, K. B., Ali, M. M., & Zahedi, E. (2011, January). Determination of reflectance optical sensor array configuration using 3-layer tissue model and monte carlo simulation. In 5th Kuala Lumpur International Conference on Biomedical Engineering 2011 (pp. 424-427). Springer Berlin Heidelberg.
 
[40]  Oweis, R. J., As' ad, H., Aldarawsheh, A., Al-Khdeirat, R., & Lwissy, K. (2013). A PC-aided optical foetal heart rate detection system. Journal of medical engineering & technology, (0), 1-9.
 
[41]  Cesarelli, M., Romano, M., Bifulco, P., Fedele, F., & Bracale, M. (2007). An algorithm for the recovery of fetal heart rate series from CTG data. Computers in biology and medicine, 37 (5), 663-669.
 
[42]  Williams, B., & Arulkumaran, S. (2004). Cardiotocography and medicolegal issues. Best Practice & Research Clinical Obstetrics & Gynaecology, 18 (3), 457-466.
 
[43]  Taylor, J., Hayes-Gill, B. R., Crowe, J. A., & Paull, C. J. (1999). Towards multi-patient leadless and wireless cardiotocography via RF telemetry. Medical engineering & physics, 20 (10), 764-772.
 
[44]  Ugwumadu, A. (2013). Understanding cardiotocographic patterns associated with intrapartum fetal hypoxia and neurologic injury. Best Practice & Research Clinical Obstetrics & Gynaecology.
 
[45]  Alfirevic, Z., Devane, D., & Gyte, G. M. (2006). Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst Rev, 3.
 
[46]  External and Internal Heart Rate Monitoring of the Fetus hopkinsmedicine.org/healthlibrary/test_procedures/gynecology/external_and_internal_heart_rate_monitoring_of_the_fetus_92,P07776/
 
[47]  Liston, R., Sawchuck, D., & Young, D. (2007). Fetal health surveillance: antepartum and intrapartum consensus guideline. Journal of obstetrics and gynaecology Canada: JOGC= Journal d'obstétrique et gynécologie du Canada: JOGC, 29 (9 Suppl 4), S3.
 
[48]  Daly, N., Brennan, D., Foley, M., & O’Herlihy, C. (2011). Cardiotocography as a predictor of fetal outcome in women presenting with reduced fetal movement.European Journal of Obstetrics & Gynecology and Reproductive Biology,159 (1), 57-61.
 
[49]  Salamalekis, E., Siristatidis, C., Vasios, G., Saloum, J., Giannaris, D., Chrelias, C.,. .. & Koutsouris, D. (2006). Fetal pulse oximetry and wavelet analysis of the fetal heart rate in the evaluation of abnormal cardiotocography tracings. Journal of Obstetrics and Gynaecology Research, 32 (2), 135-139.
 
[50]  Tekin, A., Özkan, S., Çalışkan, E., Özeren, S., Çorakçı, A., & Yücesoy, İ. (2008). Fetal pulse oximetry: correlation with intrapartum fetal heart rate patterns and neonatal outcome. Journal of Obstetrics and Gynaecology Research, 34 (5), 824-831.
 
[51]  Arulkumaran, S., & Chua, S. (1996). Cardiotocograph in labour. Current Obstetrics & Gynaecology, 6 (4), 182-188.
 
[52]  Schiermeier, S., Pildner von Steinburg, S., Thieme, A., Reinhard, J., Daumer, M., Scholz, M., & Schneider, K. T. M. (2008). Sensitivity and specificity of intrapartum computerised FIGO criteria for cardiotocography and fetal scalp pH during labour: multicentre, observational study. BJOG: An International Journal of Obstetrics & Gynaecology, 115 (12), 1557-1563.
 
[53]  Nurani, R., Chandraharan, E., Lowe, V., Ugwumadu, A., & Arulkumaran, S. (2012). Misidentification of maternal heart rate as fetal on cardiotocography during the second stage of labor: the role of the fetal electrocardiograph. Acta Obstetricia et Gynecologica Scandinavica, 91 (12), 1428-1432.
 
[54]  Dixon, J. C., Penman, D. M., & Soothill, P. W. (2000). The influence of bowel atresia in gastroschisis on fetal growth, cardiotocograph abnormalities and amniotic fluid staining. BJOG: An International Journal of Obstetrics & Gynaecology, 107 (4), 472-475.
 
[55]  Gebeh, A., Yulia, A., & Ayuk, P. (2010). Intrapartum cardiotocograph interpretation by midwives and trainee obstetricians using a modified definition of a fetal heart rate deceleration. Journal of Obstetrics & Gynaecology, 30 (7), 671-674.
 
[56]  Devane, D., & Lalor, J. (2005). Midwives’ visual interpretation of intrapartum cardiotocographs: intra‐and inter‐observer agreement. Journal of advanced nursing, 52 (2), 133-141.
 
[57]  Sinclair, M. (2001). Midwives’ attitudes to the use of the cardiotocograph machine. Journal of Advanced Nursing, 35 (4), 599-606.
 
[58]  McKevitt, S., Gillen, P., & Sinclair, M. (2011). Midwives’ and doctors’ attitudes towards the use of the cardiotocograph machine. Midwifery, 27 (6), e279-e285.
 
[59]  Jeżewski, J., Wróbel, J., Horoba, K., Cholewa, D., Gacek, A., Kupka, T., & Matonia, A. (2002). Monitoring of mechanical and electrical activity of fetal heart: The nature of signals. Archives of Perinatal Medicine, 8 (1), 40-46.
 
[60]  Karlsson, B., Foulquière, K., Kaluzynski, K., Tranquart, F., Fignon, A., Pourcelot, D.,. .. & Berson, M. (2000). The DopFet system: a new ultrasonic Doppler system for monitoring and characterization of fetal movement.Ultrasound in medicine & biology, 26 (7), 1117-1124.
 
[61]  JF Guerrero-Martínez, M Martínez-Sober, M Bataller-Mompean, JR Magdalena-Benedito. New Algorithm for Fetal QRS Detectionin Surface Abdominal Records.
 
[62]  Divon, M. Y., Torres, F. P., Yeh, S. Y., & Paul, R. H. (1985). Autocorrelation techniques in fetal monitoring. American journal of obstetrics and gynecology, 151 (1), 2.
 
[63]  Muhammad I.I., Firoz A., Mohd Ali M.A., Zahedi E.: Real-Time Signal Processing for Fetal HeartRate Monitoring IEEE Trans. on Biomed. Eng., 2003, 50, 2, 258-252.
 
[64]  Misiti M., Misiti Y., Oppenheim G., Poggi J.M.: Wavelet toolbox for use with MatlabR Mathworks 2000.
 
[65]  JEŻEWSKI, J., WRÓBEL, J., KUPKA, T., MATONIA, A., HOROBA, K., & WIDERA, M. Reliability and quality of ultrasound measurements of fetal heart rate variability. Journal of Medical Informatics.
 
[66]  Chris H L Peters, Edith D M ten Broeke, Peter Andriessen, Barbara Vermeulen, Ralph C M Berendsen1, Pieter F FWijn and S Guid Oei. Beat-to-beat detection of fetal heart rate: Dopplerultrasound cardiotocography compared to direct ECG cardiotocography in time and frequency domain. Physiol. Meas. 25 (2004) 585-593.
 
[67]  Strasburger, J. F., Cheulkar, B., & Wakai, R. T. (2008). Magnetocardiography for fetal arrhythmias. Heart rhythm: the official journal of the Heart Rhythm Society, 5 (7), 1073.
 
[68]  Van Leeuwen, P. (2000). Future topics in fetal magnetocardiography. In Biomag (pp. 587-590).
 
[69]  Van Leeuwen, P., Bettermann, H., Schüßler, M., & Lange, S. (1996). Magnetocardiography in the determination of fetal heart rate complexity. InProceedings of the Tenth International Conference on Biomagnetism.v.
 
[70]  Lewis, M. J. (2003). Review of electromagnetic source investigations of the fetal heart. Medical engineering & physics, 25 (10), 801-810.
 
[71]  Ruffo, M., Cesarelli, M., Jin, C., Gargiulo, G., McEwan, A., Sullivan, C.,. .. & van Schaik, A. (2011). Non-Invasive Foetal Monitoring with Combined ECG-PCG System. Biomedical Engineering, Trends, Researches and Technologies", To be published by INTECH.
 
[72]  Gutiérrez, D., Nehorai, A., McKenzie, D., Eswaran, H., Lowery, C. L., & Preissl, H. (2004). On-line fetal heart rate monitoring using SQUID sensor arrays. In Proc. 14th Biennial BIOMAG Conf (pp. 315-316).
 
[73]  Brazdeikis, A., & Padhye, N. S. Biomagnetic Measurements for Assessment of Fetal Neuromaturation and Well 丁 Being.
 
[74]  ZHANG ShuLin, ZHANG GuoFeng, WANG YongLiang, ZENG Jia, QIU Yang, LIU Ming, KONG XiangYan & XIE XiaoMing. A novel superconducting quantum interference device for bio magnetic measurements, (August 2013).
 
[75]  Wilson, J. D., Govindan, R. B., Hatton, J. O., Lowery, C. L., & Preissl, H. (2008). Integrated approach for fetal QRS detection. Biomedical Engineering, IEEE Transactions on, 55 (9), 2190-2197.
 
[76]  Shu-Lin, Z., Guo-Feng, Z., Yong-Liang, W., Ming, L., Hua, L., Yang, Q., & Xiao-Ming, X. (2013). Multichannel fetal magnetocardiography using SQUID bootstrap circuit.
 
[77]  Stinstra, J. G. (2001). The reliability of the fetal magnetocardiogram. Universiteit Twente.
 
[78]  Stinstra, J. G., Peters, M. J., & Quartero, H. W. P. (2000). Extracting reliable data from the fetal MCG. In Proc. Int. Conf. on Biomagnetism (pp. 591-4).
 
[79]  Comani, S., Mantini, D., Alleva, G., Gabriele, E., Liberati, M., & Romani, G. L. (2005). Simultaneous monitoring of separate fetal magnetocardiographic signals in twin pregnancy. Physiological measurement, 26 (3), 193.
 
[80]  D. Geue, H. Zavala-Fernandez, S. Lange, M. Burghoff, R. Orglmeister, D. Gronemeyer, P. van Leeumen. processing the magnetocardiographic signal in the identification of fetal and maternal heart beats in triplet pregnancy, (2007).
 
[81]  Arvinti, B., Isar, A., Stolz, R., & Costache, M. (2011, May). Performance of Fourier versus Wavelet analysis for magnetocardiograms using a SQUID-acquisition system. In Applied Computational Intelligence and Informatics (SACI), 2011 6th IEEE International Symposium on (pp. 69-74). IEEE.
 
[82]  Adamopoulos, A. V., Boutsinas, B., Vrahatis, M. N., & Anninos, P. (2001, December). Analysis of Normal and Pathological Fetal Magnetocardiograms Using Clustering Algorithms. In Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems (eunite 2001), Special Session-Adaptive Systems and Hybrid CI in Medicine, Tenerife, Spain.
 
Show Less References

Article

Relation between Planck Length and Origin of Consciousness in Life Sciences-A Mathematical Proof

1Independent Researcher, Bheemunipatnam, India


Biomedical Science and Engineering. 2014, 2(3), 48-52
DOI: 10.12691/bse-2-3-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Siva Prasad Kodukula. Relation between Planck Length and Origin of Consciousness in Life Sciences-A Mathematical Proof. Biomedical Science and Engineering. 2014; 2(3):48-52. doi: 10.12691/bse-2-3-1.

Correspondence to: Siva  Prasad Kodukula, Independent Researcher, Bheemunipatnam, India. Email: sivkod@gmail.com

Abstract

A novel attempt has been made to define the difference between living and non living things in terms of physics. This is the relation between the bio energy in the form of consciousness associated to any living thing and Planck length of quantum physics. Consciousness, a parameter which differentiates living and non living thing has been explained by physics with the use of ‘Siva’s equation of consciousnes’. ‘Planck length’ of Quantum physics has been derived by substituting the value of ‘d’ mass of ‘K-Suryon’ in ‘Siva’s equation of consciousness’. The final result is a substantial mathematical proof says that the consciousness wave originates from a point in our four dimensional space time continuum whose diameter is 1.6 times higher than ‘Planck length’ of physics. This consciousness wave obeys all the definitions of electromagnetic wave without collapsing in to Planck hole. This will be useful in making substantial theories of consciousness and ‘Neuro Quantology’.

Keywords

References

[[[[[[[[
[1]  Kodukula, S.P. Double Relativity Effect & Film Theory of the Universe. ISBN9780557077120). Raleigh, NorthCorolina: Lulu.com, 2009, 13-32, 50.
 
[2]  Kodukula S.P, Heart of the God with Grand proof Equation - A classical approach to quantum theory (ISBN9780557089956). Raleigh, NorthCorolina: Lulu.com, 2009,3-9.
 
[3]  Kodukula S P, “Space Time Equivalence -A New Concept”. International journal of Scientific Research and Publications, 2(10).Oct.2012.Weblink: http://www.ijsrp.org/researchpaper1012.php?rp=P10232.
 
[4]  Kodukula S P, “Siva's Theory of Quantum Gravity”. American Journal of Modern Physics, 3(1). 16-19. Jan. 2014.
 
[5]  Kodukula, S.P, “New Discovery about Prediction of a Particle ‘K-Suryon’ as Basic Building Block of Mass”. International Journal of physics, 2(1). 12-14. Jan. 2014.
 
Show More References
[6]  C.King, “Cosmological Foundations of Consciousness”. Journal of Cosmology, (volume14). 3706-3725, 2011.
 
[7]  Kodukula,S.P, “Equation for Consciousness in terms of Physics”, International Journal of Advancements in Research & Technology’, 1(6). Nov.212.
 
[8]  Kodukula,S.P, “Siva’s Classical Equation for Space Time and Matter”, International Journal of Advancements in Research & Technology, 2(8). Aug. 2013.
 
[9]  Malik, M. & Hipolito, M., “Time and its Relationship to Consciousness”, Journal of Consciousness Exploration & Research, 1(5). 578. 2010.
 
[10]  Oktar, C.H, “On Quantum Consciousness Mechanics”, Journal of Consciousness Exploration & Research, 3(9).1052-1063.2012.
 
[11]  Max Tegmark, “The Importance of Quantum Decoherence in Brain Processes, Phys Rev E 1999. Available: Xiv:quant-ph/9907009v2 10 Nov 1999 [ Accessed on July16,2014]
 
[12]  S. Hameroff., “Consciousness, the Brain, and Space-time Geometry”, ‘Annals of Newyork Academy of Sciences’ 1996, www.quantumconsciousness.org/penrose-hameroff/cajal.pdf [ Accessed on July16,2014]
 
[13]  Wikipedia website; en.wikipedia.org/wiki/Planck_constant.
 
Show Less References