Adaptive correlation dimension method for analysing heart rate variability during the menstrual cycle
Kirti Rawal1, B S Saini2, Indu Saini3
1Dr. B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India. nitkr1234@gmail.com.
Insights
An adaptive correlation dimension (CD) method improves nonlinear heart rate variability (HRV) analysis by optimizing time delays. This novel approach enhances accuracy in detecting HRV changes, particularly during menstrual cycles and differentiating between healthy and diseased individuals.
Area of Science:
- Physiology
- Nonlinear dynamics
- Biomedical signal processing
Background:
- Heart rate variability (HRV) analysis is crucial for understanding autonomic nervous system function.
- Conventional correlation dimension (CD) methods for nonlinear HRV analysis suffer from inaccurate time delay estimations.
- These inaccuracies limit the precise assessment of HRV variations, especially across physiological cycles like the menstrual cycle.
Purpose of the Study:
- To introduce an adaptive correlation dimension (CD) method for enhanced nonlinear heart rate variability (HRV) analysis.
- To optimize the calculation of time delays within HRV signals for improved accuracy.
- To validate the proposed method's efficacy in detecting subtle HRV changes and differentiating physiological states.
Main Methods:
- The proposed adaptive CD method segments HRV time series into overlapping windows.
- Optimal time delays are calculated for each window using an adaptive autocorrelation technique based on signal information content.
- CD is computed for each window with optimal delays, and the final adaptive CD is the average of these values.
Main Results:
- The adaptive CD method accurately differentiates between normal and diseased subjects.
- The method demonstrates superior accuracy in detecting HRV variations during the menstrual cycle in both lying and standing postures.
- Comparative analysis confirms the superiority of adaptive CD over conventional CD and detrended fluctuation analyses.
Conclusions:
- The adaptive correlation dimension method provides a more accurate estimation of time delays in HRV analysis.
- This enhanced accuracy leads to improved detection of physiological changes and disease states.
- The proposed method represents a significant advancement in nonlinear HRV analysis, particularly for dynamic physiological processes.
Abstract:
Correlation dimension (CD) is used for analysing the chaotic behaviour of the nonlinear heart rate variability (HRV) time series. In CD, the autocorrelation function is used to calculate the time delay. However, it does not provide optimum values of time delays, which leads to an inaccurate estimation of the HRV between phases of the menstrual cycle. Thus, an adaptive CD method is presented here to calculate the optimum value of the time delay based upon the information content in the HRV signal. In the proposed method, the first step is to divide the HRV signal into overlapping windows. Afterwards, the time delay is calculated for each window based on the features of the signal. This procedure of finding the optimum time delay for each window is known as adaptive autocorrelation. Then, the CD for each window is calculated using optimum time delays. Finally, adaptive CD is calculated by averaging the CD of all windows. The proposed method is applied on two data sets: (i) the standard Physionet dataset and (ii) the dataset acquired using BIOPAC(®)MP150. The results show that the proposed method can accurately differentiate between normal and diseased subjects. Further, the results prove that the proposed method is more accurate in detecting HRV variations during the menstrual cycles of 74 young women in lying and standing postures. Three statistical parameters are used to find the effectiveness of adaptive autocorrelation in calculating time delays. The comparative analysis validates the superiority of the proposed method over detrended fluctuation analyses and conventional CD.
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