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Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy

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Abstract

Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales ‘t’. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entropy to the HRV time series of certain pathologies than that of healthy subjects, and to their corresponding randomized surrogate time series. This incorrect assessment of signal complexity may be due to the fact that these techniques suffer from the following limitations: (1) threshold value ‘r’ is updated as a function of long-term standard deviation and hence unable to explore the short-term variability as well as substantial variability inherited in beat-to-beat fluctuations of long-term HRV time series. (2) In RMSE, entropy values assigned to different filtered scaled time series are the result of changes in variance, but do not completely reflect the real structural organization inherited in original time series. In the present work, we propose an improved RMSE (I-RMSE) technique by introducing a new procedure to set the threshold value by taking into account the period-to-period variability inherited in a signal and evaluated it on simulated and real HRV database. The proposed I-RMSE assigns higher entropy to the age-matched healthy subjects than that of patients suffering from atrial fibrillation, congestive heart failure, sudden cardiac death and diabetes mellitus, for the entire time scales. The results strongly support the reduction in complexity of HRV time series in female group, old-aged, patients suffering from severe cardiovascular and non-cardiovascular diseases, and in their corresponding surrogate time series.

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Acknowledgments

The authors are grateful to the Department of Electronics and Communication Engineering and administration of Dr. B. R. Ambedkar National Institute of Technology, Jalandhar (Punjab), for providing every kind of technical and administrative help for the present work carried out in its ‘Medical Imaging and Computational Modeling of Physiological Systems Research Laboratory.’ The authors also acknowledge all help provided by ‘Biomedical Signal Processing and Telemedicine Laboratory. The Authors are highly thankful to the unknown reviewers for their critical and valuable suggestions towards improvement of the research work.

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  1. Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India

    Puneeta Marwaha & Ramesh Kumar Sunkaria

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  1. Puneeta Marwaha
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  2. Ramesh Kumar Sunkaria
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Correspondence to Puneeta Marwaha.

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Marwaha, P., Sunkaria, R.K. Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy. Med Biol Eng Comput 55, 191–205 (2017). https://doi.org/10.1007/s11517-016-1476-y

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