Characterization of HRV by Poincare Plot Analysis among the Female Tea Garden Workers of Northern Hilly Regions of West Bengal

Recent research on Heart Rate Variability HRV has proven that Poincare Plot is a powerful tool to mark Short Term and Long Term Heart Rate Variability. This study focuses a comprehensive characterization of HRV among the Tea Garden Workers of the Northern Hilly Regions of West Bengal. The characterization, as available from the data sets, projects the average values of SD1 characteristics, that is, Short Term HRV in females as 58.265ms and SD2 as 149.474. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. ApEn Characterization showed mean value of 0.961 and standard deviation of 0.274.

[1]  Joseph Tan.  Healthcare Information Systems and Informatics: Research and Practices , 2008 .

[2]  E. Geisler,et al.  The Current State of Bionanotechnology , 2008 .

[3]  A. Patzak,et al.  Blunted arterial baroreflex causes "pathological" heart rate turbulence. , 2000, American journal of physiology. Regulatory, integrative and comparative physiology.

[4]  D. Eckberg.  Sympathovagal balance: a critical appraisal. , 1997, Circulation.

[5]  L. Glass,et al.  Phase locking, period-doubling bifurcations, and irregular dynamics in periodically stimulated cardiac cells. , 1981, Science.

[6]  P. Macklem,et al.  Complex systems and the technology of variability analysis , 2004, Critical Care.

[7]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[8]  Paolo Castiglioni,et al.  Assessing arterial baroreflex control of heart rate: new perspectives. , 2004, Journal of hypertension.

[9]  E. Ott.  Chaos in Dynamical Systems: Contents , 1993 .

[10]  Leonid Churilov,et al.  Towards Process-of-Care Aware Emergency Department Information Systems: A Clustering Approach to Activity Views Elicitation , 2008, Int. J. Heal. Inf. Syst. Informatics.

[11]  Mieso K. Denko,et al.  Envisioning a National e-Medicine Network Architecture in a Developing Country: A Case Study , 2008, Int. J. Heal. Inf. Syst. Informatics.

[12]  Thomas W. Miller,et al.  Tele-Practice Technology: A Model for Healthcare Delivery to Underserved Populations , 2009 .

[13]  Tobias Mettler,et al.  Transformation of the Hospital Supply Chain: How to Measure the Maturity of Supplier Relationship Management Systems in Hospitals? , 2011, Int. J. Heal. Inf. Syst. Informatics.

[14]  D. Eckberg.  The human respiratory gate , 2003 .

[15]  Marimuthu Palaniswami,et al.  Poincaré plot interpretation using a physiological model of HRV based on a network of oscillators. , 2002, American journal of physiology. Heart and circulatory physiology.

[16]  Joel J. P. C. Rodrigues,et al.  Health Information Systems: Concepts, Methodologies, Tools, and Applications , 2009 .

[17]  N. Wickramasinghe.  Encyclopedia of Healthcare Information Systems , 2008 .

[18]  Penny A. Jennett,et al.  Telehealth Organizational Implementation Guideline Issues: A Canadian Perspective , 2006, Int. J. Heal. Inf. Syst. Informatics.

[19]  Jarosław Piskorski,et al.  Filtering Poincaré plots , 2005 .

[20]  Peter Hoonakker,et al.  Development and Psychometric Qualities of the SEIPS Survey to Evaluate CPOE/EHR Implementation in ICUs , 2011, Int. J. Heal. Inf. Syst. Informatics.

[21]  Jesús Favela,et al.  Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays , 2009, Int. J. Ambient Comput. Intell..

[22]  Marimuthu Palaniswami,et al.  Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? , 2001, IEEE Transactions on Biomedical Engineering.

[23]  Roger Hainsworth,et al.  Physiology of the Cardiac Autonomic System , 1998 .