PRIMARY OBJECTIVE: This study evaluated the hypothesis that neural networks derangement in patients with a vegetative state (VS) may cause an alteration of heart rate (HR) non-linear pattern. METHODS AND PROCEDURES: Fifteen consecutive patients with a persistent VS and 15 matched healthy control subjects were included in the study. A 6-hour continuous electrocardiographic recording was used for the time series analysis measuring the occurrence time of the intervals between consecutive normal sinus heart beats (RR' intervals). Parameters evaluating linear and non-linear HR variability were studied. Approximate Entropy (ApEn), a non-linear parameter that quantifies the unpredictability of fluctuations in an instantaneous HR time series, was calculated from the average values of time series with fixed input variables. MAIN OUTCOMES AND RESULTS: All linear parameters, with the only exception being the percentage of RR' intervals that were by at least 50 ms different from the previous interval (0.56, SD = 1.31 vs 10.35, SD = 12.58; p = 0.005) were similar in patients and in healthy control subjects. Mean ApEn values (0.68, SD = 0.24 vs 1.10, SD = 0.16; p = 0.0001) were lower in patients than in healthy control subjects. CONCLUSIONS: The findings support the hypothesis that derangement of neural networks may cause a reduction of non-linear behaviour in HR such as ApEn.
PRIMARY OBJECTIVE: This study evaluated the hypothesis that neural networks derangement in patients with a vegetative state (VS) may cause an alteration of heart rate (HR) non-linear pattern. METHODS AND PROCEDURES: Fifteen consecutive patients with a persistent VS and 15 matched healthy control subjects were included in the study. A 6-hour continuous electrocardiographic recording was used for the time series analysis measuring the occurrence time of the intervals between consecutive normal sinus heart beats (RR' intervals). Parameters evaluating linear and non-linear HR variability were studied. Approximate Entropy (ApEn), a non-linear parameter that quantifies the unpredictability of fluctuations in an instantaneous HR time series, was calculated from the average values of time series with fixed input variables. MAIN OUTCOMES AND RESULTS: All linear parameters, with the only exception being the percentage of RR' intervals that were by at least 50 ms different from the previous interval (0.56, SD = 1.31 vs 10.35, SD = 12.58; p = 0.005) were similar in patients and in healthy control subjects. Mean ApEn values (0.68, SD = 0.24 vs 1.10, SD = 0.16; p = 0.0001) were lower in patients than in healthy control subjects. CONCLUSIONS: The findings support the hypothesis that derangement of neural networks may cause a reduction of non-linear behaviour in HR such as ApEn.
Authors: Alexander A Fingelkurts; Andrew A Fingelkurts; Sergio Bagnato; Cristina Boccagni; Giuseppe Galardi Journal: PLoS One Date: 2011-10-05 Impact factor: 3.240
Authors: Francesco Riganello; Stephen Karl Larroque; Mohamed Ali Bahri; Lizette Heine; Charlotte Martial; Manon Carrière; Vanessa Charland-Verville; Charlène Aubinet; Audrey Vanhaudenhuyse; Camille Chatelle; Steven Laureys; Carol Di Perri Journal: Front Neurol Date: 2018-09-12 Impact factor: 4.003