119836 20240319081009.0 doi 10.3390/s22155774 sideral 130592 ART-2022-130592 eng Cajal, Diego Universidad de Zaragoza Effects of missing data on heart rate variability metrics 2022 Access copy available to the general public Unrestricted Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics. info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PID2021-126734OB-C21 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.9 2022 CHEMISTRY, ANALYTICAL 26 / 86 = 0.302 2022 Q2 T1 INSTRUMENTS & INSTRUMENTATION 19 / 63 = 0.302 2022 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 100 / 274 = 0.365 2022 Q2 T2 0.764 2022 Instrumentation 2022 Q1 Analytical Chemistry 2022 Q1 Medicine (miscellaneous) 2022 Q2 Information Systems 2022 Q2 Biochemistry 2022 Q2 Atomic and Molecular Physics, and Optics 2022 Q2 Electrical and Electronic Engineering 2022 Q2 6.8 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Hernando, David (orcid)0000-0002-1462-4880 Lázaro, Jesús Universidad de Zaragoza (orcid)0000-0001-8742-0072 Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Gil, Eduardo Universidad de Zaragoza (orcid)0000-0001-7285-0715 Bailón, Raquel Universidad de Zaragoza (orcid)0000-0003-1272-0550 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 22, 15 (2022), 5774 [22 pp.] Sensors Sensors 1424-8220 1502604 http://zaguan.unizar.es/record/119836/files/texto_completo.pdf Versión publicada 2722372 http://zaguan.unizar.es/record/119836/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:119836 articulos driver 2024-03-18-14:56:14 ARTICLE