Analysis of heart rate signals during meditation using visibility graph complexity

In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

[1]  Hojjat Adeli,et al.  New diagnostic EEG markers of the Alzheimer’s disease using visibility graph , 2010, Journal of Neural Transmission.

[2]  Effect of extreme data loss on heart rate signals quantified by entropy analysis , 2015 .

[3]  J. Pu,et al.  Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study , 2016, Cognitive Neurodynamics.

[4]  Jeffrey M. Hausdorff,et al.  Heart rate dynamics during three forms of meditation. , 2004, International journal of cardiology.

[5]  F. Travis,et al.  Effect of meditation on psychological distress and brain functioning: A randomized controlled study , 2018, Brain and Cognition.

[6]  Michael Small,et al.  Recurrence-based time series analysis by means of complex network methods , 2010, Int. J. Bifurc. Chaos.

[7]  A. Sanz-Lobera,et al.  Feasibility Study for Visibility Algorithms Implementation in Surface Texture Characterization , 2015 .

[8]  Atefeh Goshvarpour,et al.  Bispectrum Estimation of Electroencephalogram Signals During Meditation , 2012, Iranian journal of psychiatry and behavioral sciences.

[9]  J. C. Nuño,et al.  The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion , 2009, 0901.0888.

[10]  C. Peng,et al.  Exaggerated heart rate oscillations during two meditation techniques. , 1999, International journal of cardiology.

[11]  Pham Lam Vuong,et al.  An EEG-based machine learning method to screen alcohol use disorder , 2017, Cognitive Neurodynamics.

[12]  Xiaoying Tang,et al.  New Approach to Epileptic Diagnosis Using Visibility Graph of High-Frequency Signal , 2013, Clinical EEG and neuroscience.

[13]  Atefeh Goshvarpour,et al.  Comparison of higher order spectra in heart rate signals during two techniques of meditation: Chi and Kundalini meditation , 2012, Cognitive Neurodynamics.

[14]  Jongwha Kim,et al.  Dynamic correlations between heart and brain rhythm during Autogenic meditation , 2013, Front. Hum. Neurosci..

[15]  Xinbao Ning,et al.  Visibility graph analysis on heartbeat dynamics of meditation training , 2013 .

[16]  Alexander A. Fingelkurts,et al.  EEG-guided meditation: A personalized approach , 2015, Journal of Physiology-Paris.

[17]  Z. Shao.  Network analysis of human heartbeat dynamics , 2010 .

[18]  Anirban Bhaduri,et al.  Quantitative Assessment of Heart Rate Dynamics during Meditation: An ECG Based Study with Multi-Fractality and Visibility Graph , 2016, Front. Physiol..

[19]  Vicente Moret-Bonillo,et al.  Spectral Heart Rate Variability analysis using the heart timing signal for the screening of the Sleep Apnea-Hypopnea Syndrome , 2016, Comput. Biol. Medicine.

[20]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[21]  Ranjan Sengupta,et al.  Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli , 2015 .

[22]  C. Costa-Neto,et al.  Participation of kallikrein-kinin system in different pathologies. , 2008, International immunopharmacology.

[23]  J. González-Mora,et al.  Gray Matter and Functional Connectivity in Anterior Cingulate Cortex are Associated with the State of Mental Silence During Sahaja Yoga Meditation , 2018, Neuroscience.

[24]  Thomas Wilhelm,et al.  What is a complex graph , 2008 .

[25]  P. Acar,et al.  Cardiac imaging of congenital heart diseases during interventional procedures continues to evolve: Pros and cons of the main techniques. , 2016, Archives of cardiovascular diseases.

[26]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[27]  Lucas Lacasa,et al.  Visibility Algorithms: A Short Review , 2012 .

[28]  Xiang Li,et al.  Detection and prediction of the onset of human ventricular fibrillation: an approach based on complex network theory. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Y. Park,et al.  The association between abnormal heart rate variability and new onset of chronic kidney disease in patients with type 2 diabetes: a ten-year follow-up study. , 2015, Diabetes research and clinical practice.

[30]  Javad Haddadnia,et al.  Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy , 2017, Seizure.

[31]  Zhao Dong,et al.  Comment on “Network analysis of human heartbeat dynamics” [Appl. Phys. Lett. 96, 073703 (2010)] , 2010 .

[32]  Jonathan F. Donges,et al.  Visibility graph analysis of geophysical time series: Potentials and possible pitfalls , 2012, Acta Geophysica.

[33]  Susmita Bhaduri,et al.  Electroencephalographic Data Analysis With Visibility Graph Technique for Quantitative Assessment of Brain Dysfunction , 2015, Clinical EEG and neuroscience.

[34]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[35]  Solange Akselrod,et al.  The use of a wavelet transform for the analysis of nonstationary heart rate variability signal during thrombolytic therapy as a marker of reperfusion , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[36]  Yasar Dasdemir,et al.  Analysis of functional brain connections for positive–negative emotions using phase locking value , 2017, Cognitive Neurodynamics.

[37]  Atefeh Goshvarpour,et al.  Chaotic Behavior of Heart Rate Signals during Chi and Kundalini Meditation , 2012 .

[38]  P. Lehrer,et al.  Dynamic Processes in Regulation and Some Implications for Biofeedback and Biobehavioral Interventions , 2013, Applied psychophysiology and biofeedback.

[39]  P. Barat,et al.  EFFECT OF MEDITATION ON SCALING BEHAVIOR AND COMPLEXITY OF HUMAN HEART RATE VARIABILITY , 2006, physics/0612088.

[40]  Anirban Bhaduri,et al.  Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure , 2017 .