Gustavo Dos Santos Ribeiro1, Victor Ribeiro Neves2, Luís Fernando Deresz3, Rosangela Domingues Melo1, Pedro Dal Lago4, Marlus Karsten5. 1. Graduate Program in Rehabilitation Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil. 2. Department of Physical Therapy, Universidade de Pernambuco - Campus Petrolina (UPE), Petrolina, PE, Brazil. 3. Department of Physical Education, Universidade Federal de Juiz de Fora (UFJF), Govenador Valadares, MG, Brazil. 4. Graduate Program in Rehabilitation Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil; Department of Physical Therapy, UFCSPA, Porto Alegre, RS, Brazil. 5. Graduate Program in Rehabilitation Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil; Department of Physical Therapy, Universidade do Estado de Santa Catarina (UDESC), Florianópolis, SC, Brazil; Graduate Program in Physical Therapy, UDESC, Florianópolis, SC, Brazil. Electronic address: marlus.karsten@udesc.br.
Abstract
BACKGROUND: Oscillation between successive sinus beats or RR intervals, termed heart rate variability, is an important marker of autonomic function of the heart. However, its analysis may be influenced by the database recorded based on the occurrence of interference. OBJECTIVE: To evaluate if the techniques of identification and editing of artifacts, as well as the selection methods of RR intervals, can interfere with heart rate variability analysis. METHODS: The RR intervals of 56 subjects (30 aortic stenosis patients, 14 physically active individuals, 12 amateur athletes) were recorded for 10min using a heart rate monitor. Values with differences greater than 20%, higher than three standard deviations or outside of the normal curve (95% confidence interval) were considered artifacts. These points were corrected through data replacement, adjacent, linear and polynomial interpolation, or excluded. Then, the 256 highest stability points and the last 5min of recordings were chosen. The software programs, Kubios HRV and GraphPAD, were used to calculate and to analyze the indices of heart rate variability, respectively. RESULTS: Strong agreement was observed among the identification algorithms; there was no difference between the correction techniques (p=0.95); and the selection methods exhibited different sections (p<0.01) with a direct influence on approximated entropy (p<0.05). CONCLUSION: With short-term recordings, selection methods may interfere with the non-linear heart rate variability analysis. The confidence interval, the replacement by the average of previous data and the selection of 256 of the highest stability points of the signal seem to be the most adequate procedures to treat the data with prior to analysis.
BACKGROUND: Oscillation between successive sinus beats or RR intervals, termed heart rate variability, is an important marker of autonomic function of the heart. However, its analysis may be influenced by the database recorded based on the occurrence of interference. OBJECTIVE: To evaluate if the techniques of identification and editing of artifacts, as well as the selection methods of RR intervals, can interfere with heart rate variability analysis. METHODS: The RR intervals of 56 subjects (30 aortic stenosispatients, 14 physically active individuals, 12 amateur athletes) were recorded for 10min using a heart rate monitor. Values with differences greater than 20%, higher than three standard deviations or outside of the normal curve (95% confidence interval) were considered artifacts. These points were corrected through data replacement, adjacent, linear and polynomial interpolation, or excluded. Then, the 256 highest stability points and the last 5min of recordings were chosen. The software programs, Kubios HRV and GraphPAD, were used to calculate and to analyze the indices of heart rate variability, respectively. RESULTS: Strong agreement was observed among the identification algorithms; there was no difference between the correction techniques (p=0.95); and the selection methods exhibited different sections (p<0.01) with a direct influence on approximated entropy (p<0.05). CONCLUSION: With short-term recordings, selection methods may interfere with the non-linear heart rate variability analysis. The confidence interval, the replacement by the average of previous data and the selection of 256 of the highest stability points of the signal seem to be the most adequate procedures to treat the data with prior to analysis.
Authors: Fabio Y Nakamura; Andrew A Flatt; Lucas A Pereira; Rodrigo Ramirez-Campillo; Irineu Loturco; Michael R Esco Journal: J Sports Sci Med Date: 2015-08-11 Impact factor: 2.988
Authors: Tuula H Tarkiainen; Tom A Kuusela; Kari U O Tahvanainen; Juha E K Hartikainen; Pekka Tiittanen; Kirsi L Timonen; Esko J Vanninen Journal: Clin Physiol Funct Imaging Date: 2007-03 Impact factor: 2.273
Authors: José F Valencia; Alberto Porta; Montserrat Vallverdú; Francesc Clarià; Rafal Baranowski; Ewa Orłowska-Baranowska; Pere Caminal Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2008
Authors: U Rajendra Acharya; Oliver Faust; Vinitha Sree; G Swapna; Roshan Joy Martis; Nahrizul Adib Kadri; Jasjit S Suri Journal: Comput Methods Programs Biomed Date: 2013-09-10 Impact factor: 5.428
Authors: Victor Ribeiro Neves; Anielle Cristhine Medeiros Takahashi; Michele Daniela Borges do Santos-Hiss; Antti Mikael Kiviniemi; Mikko Paavo Tulppo; Silvia Cristina Garcia de Moura; Marlus Karsten; Audrey Borghi-Silva; Alberto Porta; Nicola Montano; Aparecida Maria Catai Journal: Clin Auton Res Date: 2012-04-03 Impact factor: 4.435