||The Analysis of HRV and Approximate Entropy for Quantitative Application in Stroop Experiments
||Department of BioMedical Engineering
Heart rate variability
Stroop experiments are commonly used to test attention in psychology. With this type of analysis, differences in reaction time are used as indicators to compare physiological parameters, such as brain activity. Unfortunately, this involves expensive and time-consuming methods, such as EEG and fMRI. The aim of the study was to employ low-cost, convenient instruments with simple parameters to investigate variations in the physiological parameters relevant to Stroop experiments. Specifically, photoplethysmography (PPG) was used to measure changes in peripheral blood flow and PP intervals (PPIs) were calculated for complexity analysis. A total of 15 healthy subjects participated in these experiments. PPG and EEG signals were measured simultaneously in the Stroop experiment. Approximate entropy (ApEn) and heart rate variability (HRV) were applied as parameters in PPG, and power spectrum were applied to analyze EEG signals.
Our results show that higher ApEn values are associated with lower Stroop interference (the difference in reaction time between inconsistent and consistent color-words). However, we did not observe this result for HRV. In the EEG results, significant differences were observed between the two tasks for the theta and alpha bands at the Fz electrode, and for the beta band at the Cz electrode.
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.1.1 The Attention 1
1.1.2 The Networks of Attention System 3
1.1.3 The Physiology of Attention 5
1.1.4 The Reviews of Attention Measurement 6
1.2 Motivations and Purposes 9
Chapter 2 Materials and Methods 10
2.1 Research Framework 10
2.2 Physiological Signals Acquisition 12
2.2.1 Photoplethysmography 12
2.2.2 Electroencephalography 15
2.2.3 Overall Acquisition System 18
2.3 Experimental Design 19
2.3.1 Stroop Task 19
2.3.2 Vocabulary Learning Task 21
2.3.3 Subject Selection 21
2.3.4 Experimental Procedure 22
2.4 Signals Analysis 26
2.4.1 Digital Filter 26
2.4.2 Peak Detection 29
2.4.3 Approximate Entropy 32
2.4.4 Spectrum Analysis 36
2.5 Statistics 39
Chapter 3 Experimental Result 40
3.1 Previous Test 40
3.1.1 ApEn Value in Simulated Signals 40
3.1.2 ApEn Values and High-pass Filter Settings 41
3.1.3 ApEn values in Speaking and Non-speaking cases 42
3.1.4 EEG signals for Blinking and Open Eyes 43
3.2 Results of Stroop Tasks 45
3.3 Results of Vocabulary Learning 51
Chapter 4 Discussions 54
Chapter 5 Conclusions and Prospects 58
5.1 Conclusions 58
5.2 Prospects 59
 J. R. Anderson, Cognitive Psychology and its Implications, 6th ed., New York: Worth Publishers, 2005.
 W. James, The Principles of Psychology, vol.1, 1st ed., New York: Cosimo Classics, 2007.
 W. Wu, “What is conscious attention?” Philosophy and Phenomenological Research, vol.82, no.1, pp.93-120, 2011.
 M. M. Sohlberg and C. A. Meateer, “Effectiveness of an attention-training program,” Journal of Clinical and Experimental Neuropsychology, vol.9, no.2, pp.117-130, 1987.
 S. E. Petersen and M. I. Posner, “The attention system of the human brain: 20 years after,” Annual Review of Neuroscience, vol.35, pp.73-89, 2012.
 M. I. Posner and M. K. Rothbart, “Research on attention networks as a model for the integration of psychological science,” Annual Review of Psychology, vol.58, pp.1-23, 2007.
 M. I. Posner, Cognitive Neuroscience of Attention, 2nd ed., New York: The Guilford Press, 2012.
 D. M. Davydov and D. Shapiro, “Single and combined effects of sympathetic and parasympathetic activity on perceptual sensitivity and attention,” Journal of Russian and East European Psychology, vol.37, pp.68-90, 1999.
 A. L. Hansen, B. H. Johnsen, and J. F. Thayer, “Vagal influence on working memory and attention,” International Journal of Psychophysiology, vol.48, no.3, pp.263-274, 2003.
 I. Tonhajzerova, I. Ondrejka, P. Adamik, R. Hruby, M. Javorka, Z. Trunkvalterova, D. Mokra, and K. Javorka, “Changes in the cardiac autonomic regulation in children with attention deficit hyperactivity disorder (ADHD),” Indian Journal of Medical Research, vol.130, no.1, pp.44-50, 2009.
 S. H. Fairclough and K. Houston, “A metabolic measure of mental effort,” Biological Psychology, vol.66, no.2, pp.177-190, 2004.
 D. Kimhy, O. V. Crowley, P. S. McKinley, M. M. Burg, M. E. Lachman, P. A. Tun, C. D. Ryff, T. E. Seeman, and R. P. Sloan, “The association of cardiac vagal control and executive functioning - findings from the MIDUS study,” Journal of Psychiatric Research, vol.47, no.5, pp.628-635, 2013.
 M. Albares, M. Criaud, C. Wardak, S. C. T. Nguyen, S. B. Hamed, and P. Boulinguez, “Attention to baseline: does orienting visuospatial attention really facilitate target detection?” Journal of Neurophysiology, vol.106, no.2, pp.809-816, 2011.
 A. Gunduz, P. Brunner, A. Daitch, E. C. Leuthardt, A. L. Ritaccio, B. Pesaran, and G. Schalk, “Decoding covert spatial attention using electrocorticographic (ECoG) signals in humans,” NeuroImage,vol.60, no.4, pp.2285-2293, 2012.
 N. V. Lutsyuk, E. V. Éismont, and V. B. Pavlenko, “Modulation of Attention in Healthy Children Using a Course of EEG-Feedback Sessions,” Neurophysiology, vol. 38, no. 5/6, pp 389-395, 2006.
 Y. Fan, Y. Y. Tang, R. Tang, and M. I. Posner, “Short Term Integrative Meditation Improves Resting Alpha Activity and Stroop Performance,” Applied Psychophysiology and Biofeedback, vol.39, no.3-4, pp.213-217, 2014.
 C. Nombela., M. Nombela, P. Castell, T. García, J. López-Coronado, M. T. Herrero, “Alpha-Theta Effects Associated with Ageing during the Stroop Test,” PLoS ONE, vol. 9, no.5, 2014.
 A. Belle, R. H. Hargraves, and K. Najarian, “An automated optimal engagement and attention detection system using electrocardiogram,” Computational and Mathematical Methods in Medicine, vol.2012, pp.1-12, 2012.
 Y. C. Chang and S. L. Huang, “The influence of attention levels on psychophysiological responses,” International Journal of Psychophysiology, vol.86, no.1, pp.39-47, 2012.
 C. C. Hsu, H. C. Chen, Y. N. Su, K. K. Huang, and Y.M. Huang, “Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom,” Sensors, vol.12, no.10, pp.14158-14178, 2012.
 K. A. Garrison, D. Scheinost, P. D. Worhunsky, H. M. Elwafi, T. A. Thornhill IV, E. Thompson, C. Saron, G. Desbordes, H. Kober, M. Hampson, J. R. Gray, R. T. Constable, X. Papademetris, and J. A. Brewer, “Real-time fMRI links subjective experience with brain activity during focused attention,” NeuroImage, vol.81, pp.110-118, 2013.
 TNS Research International [Online]. Available: http:// http://www.tnsglobal.com/
 K. Chellappan, “Photoplethysmogram signal variability and repeatability assessment,” Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2010), pp.281-284, 2010.
 R. Gonzalez, A. Manzo, J. Delgado, J. M. Padilla, B. Trenor, and J. Saiz, “A computer based photoplethysmographic vascular analyzer through derivatives,” Computers in Cardiology, vol.35, pp.177-180, 2008.
 I. Jeong, S. Jun, D. Um, J. Oh, and H. Yoon, “Non-invasive estimation of systolic blood pressure and diastolic blood pressure using photoplethysmograph components,” Yonsei Medical Journal, vol.51, no.3, pp.345-353, 2010.
 G. Lu, F. Yang, J. A. Taylor, and J. F. Stein, “A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects,” Journal of Medical Engineering and Technology, vol.33, no.8, pp.634-641, 2009.
 W. H. Lin, D. Wu, C. Li, H. Zhang, and Y. T. Zhang, “Comparison of heart rate variability from PPG with that from ECG,” Proceedings of the IFMBE International Conference on Health Informatics, vol.42, pp.213-215, 2014.
 N. Selvaraj, A. Jaryal, J. Santhosh, K. K. Deepak, and S. Anand, “Assessment of heart rate variability derived from ﬁnger-tip photoplethysmography as compared to electrocardiography,” Journal of Medical Engineering & Technology, vol.32, no.6, pp.479-484, 2008.
 C. H. Chen, A Hand-Drawing and EEG Integrated System for Analysis of Alcoholic Effect, Master Thesis, Institute of Biomedical Engineering, National Cheng Kung University, 2007.
 Taiwan Power Company [Online]. Available: http://www.taipower.com.tw/
 J. R. Stroop, “Studies of interference in serial verbal reactions.” Journal of Experimental Psychology, vol.18, no.6, pp.643-662, 1935.
 C. M. MacLeod, “The stroop task: the“gold standard”of attentional measures,” Journal of Experimental Psychology: General, vol.121, no.1, pp.12-14, 1992.
 T. M. C. Lee and C. C. H. Chan, “Stroop interference in chinese and english,” Journal of Clinical and Experimental Neuropsychology, vol.22, no.4, pp.465-471, 2000.
 E. Yatsuka, M. Bassan, T. Hatae, M. Ishikawa, T. Shimada, G. Vayakis, M. Walsh, R. Scannell, R. Huxford, P. Bilkova, P. Bohm, M. Aftanas and K. Itami, “Progresses in development of the ITER edge Thomson scattering system,” Proceedings of the 16th International Conference on Laser Aided Plasma Diagnostics, vol.8, 2013.
 H. S. Shin, C. Lee, and M. Lee, “Adaptive threshold method for the peak detection of photoplethysmographic waveform,” Computers in Biology and Medicine, vol.39, no.12, pp.1145-1152, 2009.
 B. Hong, F. Yang, Q. Tang, and T. C. Chan, “Approximate entropy and it's preliminary application in the field of EEG and cognition,” Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol.20, no.4, 1998.
 O. Faustm and M. G. Bairy, “Nonlinear analysis of physiological signals: a review,” Journal of Mechanics in Medicine and Biology, vol.12, no.4, pp.1240015(1)-1240015(21), 2012.
 H. Sohn, I. Kim, W. Lee, B. S. Peterson, H. Hong, J. H. Chae, S. Hong, and J. Jeong, “Linear and non-linear EEG analysis of adolescents with attention-deficit/hyperactivity disorder during a cognitive task,” Clinical Neurophysiology, vol.121, no.11, pp.1863-1870, 2010.
 S. M. Pincus, “Approximate entropy as a measure of systemcomplexity,” Proceedings of the National Academy of Sciences of the United States of America, voI.88, no.6, pp.2297-2301, 1991.
 C. P. Dancey and J. Reidy, Statistics Without Maths for Psychology: Using SPSS for Windows, 3rd ed., London: Prentice Hall, 2004.
 G. S. E. van den Broek, A. Takashima, E. Segers, G. Fernández, and L.Verhoeven, “Neural correlates of testing effects in vocabulary learning,” NeuroImage, vol.78, pp.94-102, 2013.
 H. C. Middleton, A. Sharma, D. Agouzoul, B. J. Sahakian, and T. W. Robbins, “Contrast between the cardiovascular concomitants of tests of planning and attention,” Psychophysiology, vol.36, no.5, pp.610–618, 1999.
 R. W. Backs, and K. A. Seljos, “Metabolic and cardiorespiratory measures of mental effort: the effects of level of difficulty in working memory task,” International Journal of Psychophysiology, vol.16, no.1, pp.57–68, 1994.