In industrialized countries with aging populations, heart failure affects 0.3%-2% of the general population. The investigation of 24h-ECG recordings revealed the potential of nonlinear indices of heart rate variability (HRV) for enhanced risk stratification in patients with ischemic heart failure (IHF). However, long-term analyses are time-consuming, expensive and delay the initial diagnosis. The objective of this study was to investigate whether 30min short-term HRV analysis is sufficient for comparable risk stratification in IHF in comparison to 24h-HRV analysis. From 256 IHF patients (221 at low risk (IHFLR) and 35 at high risk (IHFHR)) a) 24h beat-to-beat time series b) the first 30min segment c) the 30min most stationary day segment and d) the 30min most stationary night segment were investigated. We calculated linear (time and frequency domain) and nonlinear HRV analysis indices. Optimal parameter sets for risk stratification in IHF were determined for 24 hours and for each 30min segment by applying discriminant analysis on significant clinical and non-clinical indices. Long- and short-term HRV indices from frequency domain and particularly from nonlinear dynamics revealed high univariate significances (p

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (ρ), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with ρ>0.85, C>0.8, and RAE<0.5. These values were significantly better than the performance of LMS adaptive filter (ρ<0.85, C1).

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.

The aim of the present study was to investigate the suitability of the Phase-Rectified Signal Averaging (PRSA) method for improved risk prediction in cardiac patients. Moreover, this technique, which separately evaluates acceleration and deceleration processes of cardiac rhythm, allows the effect of sympathetic and vagal modulations of beat-to-beat intervals to be characterized. Holter recordings of idiopathic dilated cardiomyopathy (IDC) patients were analyzed: high-risk (HR), who suffered sudden cardiac death (SCD) during the follow-up; and low-risk (LR), without any kind of cardiac-related death. Moreover, a control group of healthy subjects was analyzed. PRSA indexes were analyzed, for different time scales T and wavelet scales s, from RR series of 24 h-ECG recordings, awake periods and sleep periods. Also, the behavior of these indexes from simulated data was analyzed and compared with real data results. Outcomes demonstrated the PRSA capacity to significantly discriminate healthy subjects from IDC patients and HR from LR patients on a higher level than traditional temporal and spectral measures. The behavior of PRSA indexes agrees with experimental evidences related to cardiac autonomic modulations. Also, these parameters reflect more regularity of the autonomic nervous system (ANS) in HR patients.

The theory of Shannon entropy was applied to the Choi-Williams time-frequency distribution (CWD) of time series in order to extract entropy information in both time and frequency domains. In this way, four novel indexes were defined: (1) partial instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function at each time instant taken independently; (2) partial spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of each frequency value taken independently; (3) complete instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function of the entire CWD; (4) complete spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of the entire CWD. These indexes were tested on synthetic time series with different behavior (periodic, chaotic and random) and on a dataset of electroencephalographic (EEG) signals recorded in different states (eyes-open, eyes-closed, ictal and non-ictal activity). The results have shown that the values of these indexes tend to decrease, with different proportion, when the behavior of the synthetic signals evolved from chaos or randomness to periodicity. Statistical differences (p-value < 0.0005) were found between values of these measures comparing eyes-open and eyes-closed states and between ictal and non-ictal states in the traditional EEG frequency bands. Finally, this paper has demonstrated that the proposed measures can be useful tools to quantify the different periodic, chaotic and random components in EEG signals.