Remote monitoring of heart rate variability for obese children. (April 2021)

• Record Type: Journal Article
• Authors: Goroso, Daniel Gustavo; Watanabe, William Tsutomu; Napoleone, Fernanda; da Silva, Diego Pereira; Salinet, João Loures; da Silva, Robson Rodrigues; Puglisi, José Luis
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Children obesity -- Heart rate variability -- Internet-of-things -- Remote monitoring; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102453
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Heart rate variability in eutrophic, overweight and obese children. Remote monitoring of heart rate with smartphone. Internet-of-things applied to health awareness. Abstract: Background: Heart rate variability (HRV) is a measurement of time variation between heartbeats. HRV analysis in children and adolescents with overweight and obesity is used as an indicator of cardiovascular system function. Because of that it is important to monitor HR with a non-invasive and reliable method. This paper describes a less traumatic and non-expensive method for online monitoring of HR and calculation of HRV parameters in eutrophic, overweight and obese children. Methods: Cardiac activity from eutrophic (n = 6), overweight (n = 11), and obese (n = 9) children, (10.3 ± 1.3 years old) were performed with a smartphone App connected via Bluetooth to a smartwatch sensor during night-, morning- and afternoon/evening-time for sixty days. Signals were transmitted from the smartphone to an online monitoring system. HRV analysis was performed on the web platform, eighteen parameters were calculated (time domain, frequency domain, Poincaré Plot and Sample Entropy). Normality, Two-way repeated measures Anova and Bonferroni tests were applied, p < 0.05 were considered statistically significant. Results: Each group showed statistical differences in the time domain parameters SDNN and RMSDD, p < 0.05 in all cases. The parameters SD2 and ratio SD1-SD2 were significantly different in all cases (p < 0.05). Likewise, there were differences between night-, morning- and afternoon/evening-time for all groups in the same parameters SDNN and RMSDD (p < 0.05 in all cases) and SD2 (p < 0.05), while the ratio SD1-SD2 was marginally significant (p = 0.052). Conclusions: The described methodology provides a new, inexpensive and easy to implement approach for remote monitoring and analysis of cardiovascular parameters from children.