Automatic detection of seizure termination during electroconvulsive therapy using sample entropy of the electroencephalogram.

Determining the exact duration of seizure activity is an important factor for predicting the efficacy of electroconvulsive therapy (ECT). In most cases, seizure duration is estimated manually by observing the electroencephalogram (EEG) waveform. In this article, we propose a method based on sample entropy (SampEn) that automatically detects the termination time of an ECT-induced seizure. SampEn decreases during seizure activity and has its smallest value at the boundary of seizure termination. SampEn reflects not only different states of regularity and complexity in the EEG but also changes in EEG amplitude before and after seizure activity. Using SampEn, we can more precisely determine seizure termination time and total seizure duration.

Breakdown of the intermediate-term fractal scaling exponent in sinus node dysfunction. New method for non-invasive evaluation of sinus node function.

BACKGROUND: The aim of the present study was to characterize the heart rate dynamics of sinus bradycardia (SB) from sinus node dysfunction (SND) using non-linear dynamical system analysis. No data are yet available on how the dynamics change in the presence of SND. METHODS AND RESULTS: Conventional time and frequency domain analysis, the short- (DFAα(1)) and intermediate-term fractal scaling exponent (DFAα(2)), approximate entropy (ApEn) and sample entropy (SampEn) were calculated in 60-min sinus RR interval data of SB from 24-h ambulatory electrocardiograms of 110 patients: 44 SND patients, 44 age-matched controls, and 22 younger controls. All of the time and frequency domain parameters, ApEn and SampEn, were significantly reduced in the age-matched control group, compared with the young control group. DFAα(1) and DFAα(2) increased with aging. Both the DFAα(1) and DFAα(2) of SND patients were paradoxically reduced, which was not appropriate for their age. Only the percentage of consecutive RR intervals with absolute differences >50ms (pNN(50)), low-frequency power, and DFAα(2) made a significant contribution to prediction of SND on logistic regression analysis. Among them, DFAα(2) was the most significant variable for prediction of SND (odds ratio, 0.927; 95% confidence interval: 0.888-0.969, P=0.001). DFAα(2) remained as a significant variable for prediction of SND, when compared with overall control patients, combining the 2 control groups. CONCLUSIONS: Inappropriate reduction of DFAα(2) is a robust measure and could be an adjunctive tool for improvement of diagnostic performance in detection of SND.

Association of heart rate variability with the framingham risk score in healthy adults.

BACKGROUND: The aim of this study was to investigate the relationship between heart rate variability (HRV), the Framingham risk score (FRS), and the 10-year risk of coronary heart disease (CHD) development among Korean adults. METHODS: The subjects were 85 healthy Korean adults recruited from a health check-up center. The FRS and 10-year risk of CHD development were calculated. RESULTS: The FRS in men was inversely correlated with the standard deviation of all normal to normal RR-intervals (SDNN); the root mean square successive difference (RMSSD); the percentage of successive normal cardiac inter-beat intervals greater than 20 ms, 30 ms, and 50 ms (pNN20, pNN30, pNN50); the low frequency (LF); and the high frequency (HF) (P < 0.05). There was no significant relationship between the FRS and HRV in women. Overall, in the receiver operating characteristic (ROC) analysis, the RMSSD, HF, SDNN, LF, LF/HF ratio, and pNN30 predicted an increased 10-year CHD risk. After adjusting for sex and body mass index, those with greater than one standard deviation in the RMSSD, HF, and LF had a 52-59% reduction in their 10-year risk of CHD development ≥ 10%. CONCLUSION: This study therefore indicates that the HRV indices, particularly SDNN, RMSSD, pNN30, LF, and HF may be useful parameters for the assessment of CHD risk. Most notably, the usefulness of these HRV measures as indicators for CHD risk evaluation may be greater among men than among women.

Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the R-R interval dynamics before the spontaneous onset of atrial fibrillation.

BACKGROUND: New methods based on nonlinear theory have been developed to give more insight into complex heart rate (HR) dynamics. This study was designed to test the hypothesis that altered HR dynamics, as analyzed with complexity and fractal measures, may precede the spontaneous onset of paroxysmal atrial fibrillation (PAF). Secondly, the difference in the temporal change of these measurements between the different types of atrial fibrillation (AF) was assessed. METHODS AND RESULTS: From 105 Holter tapes in which PAF was recorded, 44 PAF (>or=5 min) episodes in 33 patients (22 men, 58 +/- 12 years), preceded by sinus rhythm for more than 1 h, were selected and submitted to time-and frequency-domain HR variability analyses, along with detrended fluctuation analysis, approximate entropy (ApEn) and sample entropy (SampEn). The 60 min before the onset of AF were divided into 6 10-min periods and studied using repeated measures ANOVA. PAF episodes were divided into 3 subgroups: an increased HF component and decreased L/H ratio (vagal type, n=20); increased L/H ratio and decreased HF component (sympathetic type, n=14); and non-related type (n=10). None of the time- or frequency-domain parameters showed any significant change before AF in any type of AF. The alpha(1) showed a tendency to decrease before the onset of AF and the change in alpha(1) was divergent according to the AF type. The ApEn decreased before the onset of AF (1.005+/-0.046, 60-50 min before AF to 0.894+/-0.052, 10-0 min before AF; p=0.032). The SampEn also decreased progressively before the start of AF (1.165 +/- 0.085, 60-50 min before AF to 0.887 +/- 0.077, 10-0 min before AF, p=0.003). The decrease in both the ApEn and SampEn was irrespective of the AF type. CONCLUSIONS: A reduction in the ApEn and SampEn, which reflects the nonlinear complexity of HR variability, is a hallmark of altered HR dynamics preceding the spontaneous onset of AF.