Nonlinear Analysis of Stride Interval Time Series in Gait Maturation Using Distribution Entropy

• Published in: Ingénierie et recherche biomédicale 2022-08, Vol.43 (4), p.309-316
• Main Author: Alcan, V.
• Format: Article
• Language: English
• Subjects: Distribution entropy; Gait; Irregularity; Nonlinear time analysis
• ISSN: 1959-0318
• DOI: 10.1016/j.irbm.2021.02.001

•Many aspects of the growth of the human body have shown a nonlinear feature.•Entropy can analyze nonlinear time series in terms of irregularity and complexity.•Distribution Entropy (DistEn) showed high parametric consistency.•DistEn provided information about the irregularity of the movement pattern. This study aimed to investigate whether DistEn was capable of identifying complexity or irregularity for gait data and whether having low parameter-dependency sensitivity by comparing with the Approximate Entropy (ApEn) and Sample Entropy (SampEn). The data were divided into three groups according to gait maturation. Firstly, the mean amplitude histogram, standard deviation (SD), and the power spectrum were calculated for each group. Secondly, ApEn, SampEn, and DistEn algorithms were calculated. Statistical analyses were then performed to compare groups. For m=3 with M= 256 and M=512 parameters, DistEn showed a statistically significant difference between in pairwise comparisons between all groups (Pa, Pb, and Pc < 0.05). DistEn consistently decreased from Group1, to Group2, and to Group 3. For m=2 with r=0.30 values, SampEn showed a statistically significant difference only in pairwise comparisons between Group1 and Group3 (Pb < 0.05). For with m=3 and r=0.30 parameters, SampEn also showed a statistically significant difference in pairwise comparisons between Group1 and Group3 (Pc < 0.05) as well as Group2 and Group3 (Pc < 0.05) SampEn increased from Group1 to Group3 and from Group2 to Group3. There was not any statistically significant difference in pairwise comparisons of groups for ApEn. Furthermore, DistEn showed less parameter consistency than ApEn and SampEn. DistEn showed the best performance in capture the complexity changes in gain patterns with growth.