Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy. Copyright:
Basic Science Research provided support for this study through the National Research Foundation of Korea (NRF) funded by the Ministry of Education in the form of a grant awarded to TSC (2019R1F1A1058548). This funder played a role in study design, decision to publish, and preparation of the manuscript, but did not play a role in data collection or analysis. Raptor Interactive (Pty) Ltd. provided support in the form of a salary for SSR. The specific roles of this author are articulated in the 'author contributions' section. This funder played a major role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
