Physiological signals are commonly the result of complex interactions between systems and organs, these interactions lead to signals that exhibit a non-stationary behaviour. For cardiac signals, non-stationary heart rate variability (HRV) may produce misinterpretations. A previous work proposed to divide a non-stationary signal into stationary segments by looking for changes in the signal’s properties related to changes in the mean of the signal. In this paper, we extract stationary segments from non-stationary synthetic and cardiac signals. For synthetic signals with different signal-to-noise ratio levels, we detect the beginning and end of the stationary segments and the result is compared to the known values of the occurrence of these events. For cardiac signals, RR interval (cardiac cycle length) time series, obtained from electrocardiographic records during stress tests for two populations (diabetic patients with cardiovascular autonomic neuropathy and control subjects), were divided into stationary segments. Results on synthetic signals reveal that the non-stationary sequence is divided into more stationary segments than needed. Additionally, due to HRV reduction and exercise intolerance reported on diabetic cardiovascular autonomic neuropathy patients, non-stationary RR interval sequences from these subjects can be divided into longer stationary segments compared to the control group.
Among non-invasive techniques, heart rate variability (HRV) analysis has become widely used for assessing the balance of the autonomic nervous system. Research in this area has not stopped and alternative tools for the study and interpretation of HRV, are still being proposed. Nevertheless, frequency-domain analysis of HRV is controversial when the heartbeat sequence is non-stationary. The Hilbert-Huang Transform (HHT) is a relative new technique for timefrequency analyses of non-linear and non-stationary signals. The main purpose of this work is to investigate the influence of time series´ length and noise in HRV from synthetic signals, using HHT and to compare it with Welch method. Synthetic heartbeat time series with different sizes and levels of signal to noise ratio (SNR) were investigated. Results shows i) sequence´s length did not affect the estimation of HRV spectral parameter, ii) favorable performance for HHT for different SNR. Additionally, HHT can be applied to non-stationary signals from nonlinear systems and it will be useful to HRV analysis to interpret autonomic activity when acute and transient phenomena are assessed.
This article presents a study of ventricular repolarization in diabetic and metabolic syndrome subjects. The corrected QT interval (QTc) was estimated using four correction formulas commonly employed in the literature: Bazett, Fridericia, Framingham and Hodges. After extracting the Q, R and T waves from the electrocardiogram of 52 subjects (19 diabetic, 15 with metabolic syndrome and 18 control), using a wavelet-based approach, the RR interval and QT interval were determined. Then, QTc interval was computed using the formulas previously mentioned. Additionally, laboratory test (fasting glucose, cholesterol, triglycerides) were also evaluated. Results show that metabolic syndrome subjects have normal QTc. However, a longer QTc in this population may be a sign of future complication. The corrected QT interval by Fridericia's formula seems to be the most appropriated for metabolic syndrome subjects (low correlation coefficient between RR and QTc). Significant differences were obtained in the blood glucose and triglyceride levels, principally due to the abnormal sugar metabolization of metabolic syndrome and diabetic subjects. Further studies are focused on the acquisition of a larger database of metabolic syndrome and diabetics subjects and the repetition of this study using other populations, like high performance athletes.