A Novel Multiscale Cross-Entropy Method Applied to Navigation Data Acquired with a Bike Simulator

• Published in: Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2019; pp. 733 - 736
• Main Authors: Jamin, Antoine, Duval, Guillaume, Annweiler, Cedric, Abraham, Pierre, Humeau-Heurtier, Anne
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2019
• Subjects: Bicycling; Biomedical measurement; Complexity theory; Entropy; Histograms; Physiology; Time measurement; Time series analysis
• ISSN: 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2019.8856815

We present herein a new approach - the so-called time-shift multiscale cross-distribution entropy (TSMCDE) - to quantify the complexity between two sequences. By analyzing biomedical data, we reveal that TSMCDE overperforms other cross-entropy measures. Thus, TSMCDE, multiscale cross-sample entropy (MCSE), multiscale cross-distribution entropy (MCDE), and time-shift multiscale cross-sample entropy (TSMCSE) were applied to handlebar angle and speed time series recorded from a bike simulator. Twenty-four subjects divided into two groups (12 subjects each) participated to the study. The first group corresponds to young healthy subjects. The second group corresponds to older adults with loss of autonomy. Our results show that a link may exist between complexity and the age and physical state of a population. Moreover, TSMCDE leads to a better differentiation of the two groups than MCSE, MCDE, and TSMCSE. TSMCDE should now be tested on other types of data and on larger datasets to prove its usefulness and its efficiency.