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Measuring EEG signals as a diagnostic tool for Epileptic patient is a well traveled way. Different non-linear statistical approaches have been applied over the past years on this signal to reveal the nature of connection between epilepsy and EEG. In this work we study the post-ictal ECG signals of epileptic patient collected from MIT-BIH database using mono-fractal methods as well as multifractal...
Generally, Change Point (CP) detection is time-consuming, especially from large-scale time series. In this paper, a fast method of CP detection is proposed based on Haar Wavelet (HW) and Binary Search Tree (BST), named HWBST. In this method, by multi-level HW, a Binary Search Tree, termed BSTcD, is constructed from a diagnosed time series, and two binary search criteria are introduced to detect abrupt...
In this study we analyzed the heart rate variability of 21 subjects, 12 females and 9 males performing a two-step task. The subjects were requested to watch a relaxing movie for 30 minutes and, in a different day, to watch a stressful movie again for 30 minutes. The electrocardiographic signal was recorded for all the duration of the sessions. The series of the beat-to-beat time intervals were analyzed...
We study time series of RR intervals of subjects with normal sinus rhythm (NSR) during sleep and wake phases and we also analyze time series of subjects with congestive heart failure (CHF) in both phases with the method of Higuchi's fractal dimension. We have found the presence of oscillations in the plots we have used to evaluate the Higuchi's fractal dimension; these oscillations seem to be associated...
Sleep-wake stages discrimination is an important task in the study of cardiorespiratory diseases. Usually this is done by processing physiological signals such as electroencephalogram (EEG) that are, exclusively, recorded in hospitals using polysomnography (PSG) systems. In this paper, we report a simple automatic sleep-wake stages classifier using only RR series obtained from electrocardiogram (ECG)...
Detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnosis of patients with cardiac diseases. The scaling exponents obtained with DFA are an indicator of power-law correlations in signal fluctuation, independently of signal amplitude and external trends. In this work, an approach based on DFA was proposed for analyzing heart rate variability (HRV) by means of RR series....
Chronic heart failure (CHF) is a major and growing public health concern (~23 million people worldwide) with five-year survival rates of 25% in men and 38% in women. Objective of this study was to investigate whether linear and nonlinear heart rate variability (HRV) indices enhance risk prediction in patients with CHF. To discriminate between low risk (stable condition, N = 459) and high risk (cardiac...
Heart rate variability (HRV) data display non-stationary characteristics and exhibit long-range correlation (memory). Detrended fluctuation analysis (DFA) has become a widely-used technique for long memory estimation in non-stationary HRV data. Recently, we have proposed an alternative approach based on fractional integrated autoregressive moving average (ARFIMA) models. ARFIMA models, combined with...
Aim of the study was to assess the effect of the adrenergic stimulation on the dynamic of atrial signals (AS) during atrial fibrillation (AF). The AS, derived from surface ECG after having removed ventricular activity, were analyzed using the detrended fluctuation analysis. A sympathomimetic drug, the isoproterenol mimicking adrenergic activation, was administered in 13 patients with paroxysmal or...
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