OBJECTIVES: The purpose of this research was to study possible abnormalities in the beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction. BACKGROUND: Analysis of approximate entropy of time series data provides information on the complexity of both deterministic and random processes. It has been proposed that regularity or loss of complexity of RR interval dynamics may be related to pathologic states, but this hypothesis has not been well tested in cardiovascular disorders. METHODS: Approximate entropy and conventional time and frequency domain measures of RR interval variability were compared between 40 healthy subjects with no evidence of heart disease and 40 patients with coronary artery disease and a previous Q wave myocardial infarction. The groups were matched with respect to age, and cardiac medication was discontinued in the patients with coronary artery disease before the 24-h electrocardiographic recordings. RESULTS: Approximate entropy was significantly higher in the postinfarction patients (1.21 +/- 0.18 [mean +/- SD]) than in the healthy subjects (1.05 +/- 0.11, p < 0.001), whereas the standard deviation of RR intervals (63 +/- 19 vs. 86 +/- 23 ms, p < 0.001) and the very low, low and high frequency spectral components were lower (p < 0.01, p < 0.001, p < 0.05, respectively). Approximate entropy was not related to the time domain or the spectral components of heart rate variability and was more commonly abnormal in postinfarction patients (62.5%) than any linear measure (from 20% to 42.5%) when the 90% percentile of the values obtained for healthy subjects was defined as the normal range for each measure. CONCLUSIONS: Despite reduced linear measures of heart rate variability, the unpredictability or randomness of beat to beat heart rate dynamics is increased in patients with a previous myocardial infarction. Complexity analysis of RR interval dynamics may provide useful information on abnormalities in heart rate behavior that are not easily detected by the commonly used moment statistics.
OBJECTIVES: The purpose of this research was to study possible abnormalities in the beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction. BACKGROUND: Analysis of approximate entropy of time series data provides information on the complexity of both deterministic and random processes. It has been proposed that regularity or loss of complexity of RR interval dynamics may be related to pathologic states, but this hypothesis has not been well tested in cardiovascular disorders. METHODS: Approximate entropy and conventional time and frequency domain measures of RR interval variability were compared between 40 healthy subjects with no evidence of heart disease and 40 patients with coronary artery disease and a previous Q wave myocardial infarction. The groups were matched with respect to age, and cardiac medication was discontinued in the patients with coronary artery disease before the 24-h electrocardiographic recordings. RESULTS: Approximate entropy was significantly higher in the postinfarction patients (1.21 +/- 0.18 [mean +/- SD]) than in the healthy subjects (1.05 +/- 0.11, p < 0.001), whereas the standard deviation of RR intervals (63 +/- 19 vs. 86 +/- 23 ms, p < 0.001) and the very low, low and high frequency spectral components were lower (p < 0.01, p < 0.001, p < 0.05, respectively). Approximate entropy was not related to the time domain or the spectral components of heart rate variability and was more commonly abnormal in postinfarction patients (62.5%) than any linear measure (from 20% to 42.5%) when the 90% percentile of the values obtained for healthy subjects was defined as the normal range for each measure. CONCLUSIONS: Despite reduced linear measures of heart rate variability, the unpredictability or randomness of beat to beat heart rate dynamics is increased in patients with a previous myocardial infarction. Complexity analysis of RR interval dynamics may provide useful information on abnormalities in heart rate behavior that are not easily detected by the commonly used moment statistics.
Authors: Conrado J Calvo; Wilson M Lozano; Óscar J Arias-Mutis; Luis Such-Miquel; Luis Such; Patricia Genovés; Antonio Guill; José Millet; Francisco J Chorro; Antonio Alberola; Sandeep V Pandit; Manuel Zarzoso Journal: J Physiol Biochem Date: 2019-03-18 Impact factor: 4.158
Authors: Octávio Barbosa Neto; Gustavo Ribeiro da Mota; Carla Cristina De Sordi; Elisabete Aparecida M R Resende; Luiz Antônio P R Resende; Marco Antônio Vieira da Silva; Moacir Marocolo; Rafael Silva Côrtes; Lucas Felipe de Oliveira; Valdo José Dias da Silva Journal: Clin Auton Res Date: 2017-10-10 Impact factor: 4.435
Authors: E Ronkainen; H Ansakorpi; H V Huikuri; V V Myllylä; J I T Isojärvi; J T Korpelainen Journal: J Neurol Neurosurg Psychiatry Date: 2005-10 Impact factor: 10.154
Authors: H Ansakorpi; J T Korpelainen; H V Huikuri; U Tolonen; V V Myllylä; J I T Isojärvi Journal: J Neurol Neurosurg Psychiatry Date: 2002-01 Impact factor: 10.154