We found decreased HRV immediately after the seizures, which lasted at least 5-6 h postictally, indicating a long-term postictal disturbance of the autonomous nervous system. GTCS were accompanied by a more decreased HRV than other seizures. Our results may have relevance in explaining pathomechanism of SUDEP.
Heart rate variability (HRV) analysis is considered a simple method for investigating neurocardiac regulation. However, measures of HRV may be corrupted by technical artifacts. In order to investigate the consequences of digitization errors on the time domain parameters, HRV measures from model tachograms resampled at different rates were compared. Two 375-element tachograms from human ECG tracings were shifted to a mean of 800 ms and stretched to standard deviations (SD) of 5-120 ms in 5 ms steps. All were resampled at 1-10 ms in 1 ms steps, 10 times repetitively at each interval. The mean, SD, relative accuracy error (RAE) and relative precision error (RPE) were calculated from the mean RR-interval, SD (SDNN), root mean square of successive RR-differences (RMSSD) and the percentage of consecutive RR-interval differences greater than 50 ms (pNN50). The RAE and RPE of the mean heart rate were below 0.1%. In the series with 5 ms SD, the SDNN-RAE exceeded 30% at 10 ms SI, its RPE was lower than 2% all through. Resampling the 15 ms SD tachogram at 1, 2, 4, or 10 ms resulted in RMSSD-RAE of 0.7%, 2.5%, 7.8% and 45.1%, respectively, while its RPE remained below 5%. The pNN50 shows poor accuracy and precision. An ECG sampling interval of 1 ms is recommended for HRV analysis without interpolation in order to get accurate time domain measures even in seriously reduced-variability samples. However, a lower sampling rate may be satisfactory in cases where higher variability is expected.
Heart rate asymmetry (HRA) quantifies the uneven distribution of points above and below the identity-line in a Poincaré plot of RR-intervals. The authors investigated if HRA could be influenced by the inspiration/expiration ratio. Healthy volunteers (n = 18) were studied in the supine position at 4.5 s metronome breathing. ECG and breathing signals were recorded for 360 s at each breathing pattern: inspiration controlled, inspiration/expiration controlled (1:2, 1:1, 2:1 ratio), inspiration controlled again. Time domain, frequency domain and Poincaré plot heart rate variability (HRV) analysis with Porta's and Guzik's indices were performed on 300 s tachograms. There were no statistically significant differences in time domain, frequency domain and standard Poincaré plot parameters during the various breathing patterns, whereas Porta's and Guzik's indices significantly rose at 1:1 and 2:1 compared to physiological 1:2 breathing. There were no significant differences in the HRA parameters between the first and the last runs. In our population the inspiration/expiration ratio significantly influenced HRA, but not standard HRV parameters. Positive correlation of Guzik's and Porta's index reflects reciprocal changes of the number of points and their dispersion in the accelerating and decelerating sets of RR-intervals. HRA-analysis can be a promising method for investigating cardiovascular regulation/health particularly with further spreading of wearable monitors.
The expansion of heart rate variability analysis has been facilitated by the remarkable development of computer sciences and digital signal processing during the last thirty years. The beat-to-beat fluctuation of the heart rate originates from the momentary summing of sympathetic and parasympathetic influences on the sinus node. According to the extensive associations of the autonomic nervous system, several factors affect heart rate and its variability such as posture, respiration frequency, age, gender, physical or mental load, pain, numerous disease conditions, and different drugs. Heart rate variability can be quantitatively measured by time domain and frequency domain methods that are detailed in the paper. Non-linear methods have not spread in the clinical practice yet. Various cardiovascular and other pathologies as well as different forms of mental and physical load are associated with altered heart rate variability offering the possibility of predicting disease outcome and assessing stress.
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