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Signal Processing Techniques for Coherence Analysis Between ECG and EEG Signals with a Case Study

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Congress on Intelligent Systems (CIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1335))

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Abstract

Each organ of the human body has some synchronism, affiliation and connection to one another. Typically, the biomedical signal electrocardiography (ECG) and electroencephalography (EEG) are considered as the main signals for analysis and handling of circulatory and central nervous system-related anomalies. In this scenario, non-invasive, cost-effective, minimal effort and precise continuous monitoring of aforementioned systems is needed. Stand-alone ECG and EEG signals utilized in the investigation of corresponding abnormalities may lead to insufficient analysis and inadequate results in many situations where the abnormalities are interrelated to both the systems. Both ECG and EEG signals are utilized simultaneously in neurocardiology for study of heart- and brain-related problems. Thus, signal processing of such signals is of utmost importance. Coherence analysis is an important signal processing technique to correlate ECG and EEG signals. In this paper, magnitude squared coherence (MSC) and phase coherence (PC) are used to determine the coherence between the ECG and EEG signals. Also, various mathematical and experimental techniques used to determine the coherence between circulatory and nervous system are discussed concurrently to signify the necessity of adaptive and intelligent signal processing techniques. A case study is presented with statistical results and graphical illustrations to validate the use of signal processing techniques. The results of proposed techniques can be used to construct an intelligent decision-making system for early prediction and detection of various abnormalities of neuro-cardiac systems.

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References

  1. McCraty, R.: Exploring the role of the heart in human performance. Sci. Heart 2, 70 (2016)

    Google Scholar 

  2. Saini, S.K., Gupta, R.: A review on ECG signal analysis for mental stress assessment. In: 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 915–918 (2019)

    Google Scholar 

  3. Billones, R.K.C., Bedruz, R.A.R., Caguicla, S.M.D.: Cardiac and brain activity correlation analysis using electrocardiogram and electroencephalogram signals. In: IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, pp. 1–6 (2018)

    Google Scholar 

  4. Schomer, D.L., Da Silva, F.L.: Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins (2012)

    Google Scholar 

  5. Zou, R., Shi, W., Tao, J., Li, H., Lin, X., Yang, S., Hua, P.: Neurocardiology: cardiovascular changes and specific brain region infarcts. Bio-Med. Res. Int. (2017)

    Google Scholar 

  6. Asahina, K., Matsui, R.: Relationship between sleep EEG and circulatory activities. Jpn. J. Physiol. 12(2), 124–128 (1961)

    Article  Google Scholar 

  7. Ako, M., Kawara, T., Uchida, S., Miyazaki, S., Nishihara, K., Mukai, J., Hirao, K., Ako, J., Okubo, Y.: Correlation between electroencephalography and heart rate variability during sleep. Psychiatry Clin. Neurosci. 57(1), 59–65 (2003)

    Article  Google Scholar 

  8. Coccagna, G., Scaglione, C.: Cardiocirculatory disorders and sleep. In: Sleep, pp. 589–597. Springer, Boston (2003)

    Google Scholar 

  9. Khandoker, A.H., Karmakar, C.K., Palaniswami, M.: Interaction between sleep EEG and ECG signals during and after obstructive sleep apnea events with or without arousals. In: Computers in Cardiology, pp. 685–688 (2008)

    Google Scholar 

  10. Kokonozi, A.K., Michail, E.M., Chouvarda, I.C., Maglaveras, N.M.: A study of heart rate and brain system complexity and their interaction in sleep-deprived subjects. In: Computers in Cardiology, pp. 969–971 (2008)

    Google Scholar 

  11. Lin, Y.D., Liu, W.T., Tsai, C.C., Chen, W.H.: Coherence analysis between respiration and PPG signal by bivariate AR model. World Acad. Sci. Eng. Technol. 53, 847–852 (2009)

    Google Scholar 

  12. Yang, Q., Siemionow, V., Yao, W., Sahgal, V., Yue, G.H.: Single-trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling. IEEE Trans. Neural Syst. Rehabil. Eng. 18(2), 97–106 (2010)

    Article  Google Scholar 

  13. Hu, S., Stead, M., Dai, Q., Worrell, G.A.: On the recording reference contribution to EEG correlation, phase synchorony, and coherence. IEEE Trans. Syst. Man Cybern Part B (Cybern.) 40(5), 1294–1304 (2010)

    Google Scholar 

  14. Lin, P.F., Lo, M.T., Tsao, J., Chang, Y.C., Lin, C., Ho, Y.L.: Correlations between the signal complexity of cerebral and cardiac electrical activity: a multiscale entropy analysis. PLoS ONE 9(2), e87798 (2014)

    Article  Google Scholar 

  15. Piper, D., Schiecke, K., Pester, B., Benninger, F., Feucht, M., Witte, H.: Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks. New J. Phys. 16(11), 115012 (2014)

    Article  Google Scholar 

  16. Chiu, H.C., Lin, Y.H., Lo, M.T., Tang, S.C., Wang, T.D., Lu, H.C., Ho, Y.L., Ma, H.P., Peng, C.K.: Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization. Sci. Reports 5, 13315 (2015)

    Google Scholar 

  17. Mensen, A., Zhang, Z., Qi, M., Khatami, R.: The occurrence of individual slow waves in sleep is predicted by heart rate. Sci. Reports 6(1), 1–8 (2016)

    Google Scholar 

  18. Ramasamy, M., Varadan, V.K.: Study of heart-brain interactions through EEG, ECG, and emotions. In: Nanosensors, Biosensors, Info-Tech Sensors and 3D Systems, International Society for Optics and Photonics, vol. 10167, 101670I (2017)

    Google Scholar 

  19. Nakahata, Y., Hagiwara, H.: Relationship between EEG and ECG findings at rest and during brain activity. In: International Conference on Applied Human Factors and Ergonomics, pp. 285–294. Springer, Cham (2017)

    Google Scholar 

  20. Khandoker, A.H., Karmakar, C.K., Palaniswami, M.: Interaction between sleep EEG and ECG signals during and after obstructive sleep apnea events with or without arousals. In: Computers in Cardiology, The University of Melbourne, Victoria, Australia, pp. 685–688 (2008)

    Google Scholar 

  21. Singh, G., Gupta, V., Singh, D.: Coherence Analysis between ECG signal and EEG signal. Int. J. Electronic. Commun. Technol. 1, 25–28 (2010)

    Google Scholar 

  22. Abdullah, H., Maddage, N.C., Cosic, I., Cvetkovic, D.: Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification. Med. Biol. Eng. Comput. 48(12), 1261–1269 (2010)

    Google Scholar 

  23. Golińska, A.K.: Coherence function in biomedical signal processing: a short review of applications in neurology, cardiology and gynecology. Stud. Logic, Grammar Rhetoric 25(38), 73–82 (2011)

    Google Scholar 

  24. Singh, G., Singh, C.: Estimation of coherence between ecg signal and eeg signal at different heart rates and respiratory rates. Int. J. Eng. Innovative Technol. 1(5), 159–163 (2012)

    Google Scholar 

  25. Chaudhary, A., Chauhan, B., Singh, G., Jain, M.: Coherence among electrocardiogram and electroencephalogram signals as non-invasive tool of diagnosis. Int. J. Eng. Res. Gen. Sci. 1(2), 3 (2014)

    Google Scholar 

  26. Capon, J.: High-resolution frequency-wave number spectrum analysis. Proc. IEEE 57(8), 1408–1418 (1969)

    Article  Google Scholar 

  27. Benesty, J., Chen, J., Huang, Y.: A generalized MVDR spectrum. IEEE Signal Process. Lett. 12(12), 827–830 (2005)

    Article  Google Scholar 

  28. Benesty, J., Chen, J., Huang, Y.: Estimation of the coherence function with the MVDR approach. In: IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 3, pp. III-III (2006)

    Google Scholar 

  29. Zheng, C., Zhou, M., Li, X.: On the relationship of non-parametric methods for coherence function estimation. Sig. Process. 88(11), 2863–2867 (2008)

    Article  Google Scholar 

  30. Yapanel, U.H., Hansen, J.H.: A new perceptually motivated MVDR-based acoustic front-end (PMVDR) for robust automatic speech recognition. Speech Commun. 50(2), 142–152 (2008)

    Article  Google Scholar 

  31. Physionet: the research resource for complex physiologic signals. www.physionet.org

  32. MATLAB R2018b. www.mathworks.com

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Acknowledgements

The authors are profoundly thankful to Vellore Institute of Technology, Vellore, India, and V. R. Siddhartha Engineering College, Vijayawada, India.

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Authors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

    Rajesh Polepogu & Naveen Kumar Vaegae

Authors
  1. Rajesh Polepogu
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  2. Naveen Kumar Vaegae
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Corresponding author

Correspondence to Naveen Kumar Vaegae .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  3. National Institute of Technology, Jalandhar, Punjab, India

    Anupam Yadav

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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Polepogu, R., Vaegae, N.K. (2021). Signal Processing Techniques for Coherence Analysis Between ECG and EEG Signals with a Case Study. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_48

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