Multiscale entropy with electrocardiograph, electromyography, electroencephalography, and photoplethysmography signals in healthcare: A twelve-year systematic review

Understanding the complexity of living systems in both health and disease poses a substantial challenge in biomedical science. Traditional models and statistical analyses often prove inadequate in capturing the intricate workings of our bodies, resulting in unforeseen discoveries and limitations in drug development and public health strategies. Multiscale entropy (MSE) measures have emerged as a promising tool for assessing the complexity of time series data, considering the diverse time scales inherent in biological systems. This review paper aims to explore the utility of MSE in conjunction with electrocardiography, electromyography, electroencephalography and photoplethysmography signals for predicting and comprehending the behavior of healthy biological systems and their transformations with aging and disease. Emphasis would be placed on the significance of employing adaptive data analysis techniques, underscoring the limitations of fragmenting complex systems, a common practice in scientific investigations. The primary focus will center on the applications of MSE in analyzing human physiological signals, encompassing both one-dimensional and two-dimensional aspects, across various clinical scenarios such as heart diseases, type 2 diabetes, sleep apnea, bladder obstruction, and others. The objective of this review is also to illuminate the potential of MSE in aiding a more profound understanding of intervention effects, including carotenoid-containing supplements or clinical treatments, with substantial implications for clinical practice and research.