Non-linear Heart Rate Variability Analysis of Electrocardiogram Signal Under Different Body Posture

  • Prashant KumarEmail author
  • Ashis Kumar Das
  • Prachita
  • Suman Halder
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)


The present study highlights the analysis of heart rate variability (HRV) of thirty number of electrocardiogram (ECG) dataset acquired from ten participants in three different body posture namely standing, sitting and supine. Evaluation of cardiac rhythm can be performed non-invasively using HRV analysis. ECG derived R-peak is utilised for computation of RR interval which is being put to use non-linear analysis of HRV. HRV is interconnected with mean heart rate (HR) i.e. tachycardia, normal or bradycardia. Different non-linear HRV indices like long-term variability SD2, short-term variability SD1, ratio SD2/SD1 for balance between long-term variability and short-term variability, sample entropy and defragmented fluctuation analysis have been interpreted in three body posture to get an overall conclusion. The results conclude supine posture has a lower SD1/SD2 ratio than the sitting and supine indicates lower SD1/SD2 representing higher variability. In the same manner, complexity for supine posture is less than the other two posture as higher sample entropy value represents lower complexity. The results deviate in case of lower hemodynamic data and ECG having premature ventricular contraction (PVC), which is another area of research for HRV.


Electrocardiogram RR-interval Heart rate Heart rate variability Poincare plot Sample entropy 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Prashant Kumar
    • 1
    Email author
  • Ashis Kumar Das
    • 1
    • 2
  • Prachita
    • 1
  • Suman Halder
    • 1
  1. 1.National Institute of Technology DurgapurDurgapurIndia
  2. 2.Faculty of TechnologyUttar Banga Krishi ViswavidyalayaCooch BeharIndia

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