Abstract
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.
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Kumar, P., Das, A.K., Prachita, Halder, S. (2020). Non-linear Heart Rate Variability Analysis of Electrocardiogram Signal Under Different Body Posture. In: Dawn, S., Balas, V., Esposito, A., Gope, S. (eds) Intelligent Techniques and Applications in Science and Technology. ICIMSAT 2019. Learning and Analytics in Intelligent Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42363-6_115
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DOI: https://doi.org/10.1007/978-3-030-42363-6_115
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