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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.7, No.9, Aug. 2015

Analysis of the Functional Relationship between Electrocardiographic Signal and Simultaneously Acquired Respiratory Signals

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Author(s)

Shikha Thakur, Rakesh Kumar Saxena

Index Terms

ECG;cross spectrum;mean square coherence;power spectrum;correlation

Abstract

Clinically, ECG is a potential tool used in the applications evaluating electrical activities of heart, functioning and providing solution to problems associated with it. Among such, the relationship (correlation) between respiration and electrocardiographic signal has attracted attention in past decades. In this research, a Welch spectrum estimation approach was utilized for normalizing cross spectrum analysis between these two signals. This approach can be useful while diagnosing diseases like pulmonary embolism, coronary lung diseases, Deep vein thrombosis and other diseases related to heart, from the knowledge of existing coherence bonding between these signals. This research applies the above approach to human subjects, whose ECG and respiratory signal annotations has been evaluated and were sampled at 100 samples/ second (sampling rate). The different respiratory signals are taken from Chest (CRSP), abdominal (ARSP) and oronasal regions (NRSP). The annotated signals for all the four subjects, discussed in this paper were obtained through a non-invasive test, which is medically well known as impedance phlebography, or impedance plethysmography. The numbers of samples, under the analysis were 6000 for each signal. The data was acquired from recording database of physionet. For this examination the mean square coherence (MSC) was chosen as an excellent candidate. The results imply that the mean of MSCs is found continuously decreasing in chest respiration. Secondly, the results showed maximum coherence between ECG and corresponding respiratory signal in three subjects is in Abdominal (ARSP) region (i.e. having maximum value greater than 0.5). Lastly, above analysis was analyzed over the fourth subject's data and under observation it was found, exceptionally that, the value of coherence for all respiratory patterns showed a poor functional association or simply coherence between the signal i.e.Coh2 below 0.5 in the abdominal region (ref.Fig.5) and the reason suggested could be chronic lung disease while the results show higher values, that is between (0.5 < coherence <1) in other two. Further, we show that the coherence peak reflects that the one physiological signal is synchronized with another signal of same nature at a particular frequency, here it is 0 to 35 Hz frequency band and combined analysis is shown through a Boxplot, from three regions showing maximum value of coherence upper quartile in abdominal region for three healthy subjects with maximum value of peak in the same region. This paper also presents a platform to dissolve the problem pertaining in an individual related to deep vein thrombosis, hypoxemia (blood level <90%) [16] and related diseases by estimating the coupling associated between saturated oxygen content (SO2) with respiratory patterns, in order to detect dysfunctioning clinically, also for efficient heart working. Thus, the research shows successful attempt to investigate the interaction of the PS of ECG signal and respiratory signals. The work presented in this paper can further be extended by adopting different method and either by defining a vector array element for maximum number of coherence value that could be beneficial for detecting diseases like sleep apnea on basis of minimum or maximum occurrence of peaks.

Cite This Paper

Shikha Thakur, Rakesh Kumar Saxena,"Analysis of the Functional Relationship between Electrocardiographic Signal and Simultaneously Acquired Respiratory Signals", IJIGSP, vol.7, no.9, pp.34-40, 2015.DOI: 10.5815/ijigsp.2015.09.05

Reference

[1]George B. Moody, Roger G. Mark, Andrea Zoccola, and Sara Mantero, "Derivation of Respiratory Signals Multi-lead ECGs", Massachusetts Institute of Technology, Computers in Cardiology, Washington, IEEE Computer Society Press, 1985.

[2]Douglas A. Newandee, M.S., Reisman," Measurement of EEG coherence in Group Meditation, IEEE Computer society, pp.7803-3204, 1996.

[3]N. Saiwaki, H. Tsujimoto, S. Nishida and S. Inokuchi, "Directed Coherence Analysis of EEG recorded during music listening", 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1996.

[4]Yan Xu, Simon Haykin, Fellow, IEEE, and Ronald J. Racine," Multiple Window Time-Frequency Distribution and Coherence of EEG Using Slepian Sequences and Hermite Functions", IEEE Transactions on biomedical engineering, vol. 46, no. 7, July 1999.

[5]JE Mietus, C-K Peng, PCh Ivanov, AL Goldberger ,"Detection of Obstructive Sleep Apnea from Cardiac Interbeat Interval Time Series", Harvard Medical School, Boston, USA, Computers in Cardiology, vol. 27, pp. 753-756, 2000.

[6]Ta-Hsin Li, Ta-Hsin Li," Detection of Cognitive Binding During Ambiguous Figure Tasks by Wavelet Coherence Analysis of EEG Signals, IEEE 2000.

[7]Dr. David Rapoport, Robert Norman, Michael Nielson, "Nasal Pressure Airflow Measurement", Pro-Tech Services, Inc., 2001.

[8]B. Schack', G. Seide12, Heinrich3, U. Krause',"Coherence analysis of the ongoing EEG by means of microstates of synchronous oscillations", Proceedings of the 23rd Annual EMBS International Conference, pp. 25-28, Istanbul Turkey, October 2001.

[9]Minfen Shen, Xianhui Li and Xinjun Zhang, "The Time-Varying Coherent Analysis of Medical Signals", proceedings in International Conference, 2002.

[10]Hye-Sue Songand Paul M. Lehrer," The Effects of Specific Respiratory Rates on Heart Rate and Heart Rate Variability", Applied Psychophysiology and Biofeedback, Vol. 28, No. 1, March 2003.

[11]Zhenhua Zhang, Tong Wang, Jinshan Ding, Zheng Bao, "A New Approach to Improve Coherence in SAR/GMT Processing of Distributed Microsatellites Systems", IEEE 2006.

[12]A Sobron, I Romero, T Lopetegi," Evaluation of Methods for Estimation of Respiratory Frequency from the ECG", 2008

[13]Alexander Wong," An Iterative Approach to Improved Local Phase Coherence Estimation", Canadian Conference on Computer and Robot Vision, IEEE 2008.

[14]Tetsuya Asakawa, Takuto Hayashi, Yuko Mizuno-Matsumoto," Visualization of the correlation and propagation of information between EEG electrodes", IEEE 2008.

[15]Yer Der Lin, Wei Ting Liu, Ching Che Tsai, and W. Chen, " Coherence Analysis between Respiration and PPG signal by Bivariate AR Model", WASET, vol. 3, No. 5, 2009.

[16]Gavendra Singh, Charanjeet Singh, "Estimation of Coherence Between ECG and EEG signal at different Heart rate and Respiratory Rate", International journal of Engineering and Innovative Technology, vol. 1, Issue 5, 2012.

[17]Physionet: the research resource for complex physiologic signals http://www.physionet.org/. 

[18]N. Sumitra, Rakesh Saxena,"Brain Tumor Classification Using Back Propagation Neural Network", International Journal of Image, Graphics and Signal Processing, DOI: 10.5815, 2, 45-50, February 2013.