Francesc Claria

Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects

Multiscale entropy (MSE) was proposed to characterize complexity as a function of the time-scale factor tau. Despite its broad use, this technique suffers from two limitations: (1) the artificial MSE reduction due to the coarse graining procedure and (2) the introduction of spurious MSE oscillations due to the suboptimal procedure for the elimination of the fast temporal scales. We propose a refined MSE (RMSE), and we apply it to simulations and to 24-h Holter recordings of heart rate variability (HRV) obtained from healthy and aortic stenosis (AS) groups. The study showed that the refinement relevant to the elimination of the fast temporal scales was more helpful at short scales (spanning the range of short-term HRV oscillations), while that relevant to the procedure of coarse graining was more useful at large scales. In healthy subjects, during daytime, RMSE was smaller at short scales (i.e., tau =1-2) and larger at longer scales (i.e., tau =4-20) than during nighttime. In AS population, RMSE was smaller during daytime both at short and long time scales (i.e., tau = 1 -11) than during nighttime. RMSE was larger in healthy group than in AS population during both daytime (i.e., tau = 2 -9) and nighttime (i.e., tau = 2). RMSE overcomes two limitations of MSE and confirms the complementary information that can be derived by observing complexity as a function of the temporal scale.

Heart rate variability analysis based on time?frequency representation and entropies in hypertrophic cardiomyopathy patients

In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time-frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz) and high frequency band (HF, 0.15-0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of the entropy being lower in high risk patients, p-value < 0.05, indicating an increase of predictability. Furthermore, measures from information entropy, but not from TFR, seem to be useful for enhanced risk stratification in HCM patients with an increased risk of sudden cardiac death.

Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals

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, C<0.6, and RAE>1).

The effects of coronary occlusion location on the RR signal

We carry out the study of the energy contribution of the RR signal in the three frequency bands (LF, MF, and HF) during percutaneous transluminal coronary angioplasty. Furthermore, variables based on instantaneous frequency and on group delay computed from RR signal in the three frequency bands are analyzed. All these variables are calculated by applying the Choi-Williams distribution to the RR signal. The purpose of studying these variables is to provide a punctual location of the energy in the time-frequency plane, since this can allow different kinds and locations of coronary lesions to be distinguished.

Information Flow to Assess Cardiorespiratory Interactions in Patients on Weaning Trials

Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variability in the pattern of breathing. In order to provide assess to these nonlinear processes, nonlinear statistical dependencies between heart rate variability and respiratory pattern variability are analyzed. In this way, auto-mutual information and cross-mutual information concepts are applied. This information flow analysis is presented as a short-term non linear analysis method to investigate the information flow interactions in patients on weaning trials. 78 patients from mechanical ventilation were studied: Group A of 28 patients that failed to maintain spontaneous breathing and were reconnected; Group B of 50 patients with successful trials. The results show lower complexity with an increase of information flow in group A than in group B. Furthermore, a more (weakly) coupled nonlinear oscillator behavior is observed in the series of group A than in B

Time-frequency representation of the HRV: a tool to characterize sudden cardiac death in hypertrophy cardiomyopathy patients

In the present work, Heart Rate Variability (HRV) is described by time-frequency representation (TFR), in order to stratify hypertrophic cardiomyopathy (HCM) patients with increasing risk of suffering sudden cardiac death (SCD). TFR and Fast Fourier Transform (FFT) analysis are also compared. The analysis is based on three frequency bands: VLF, 0-0.04 Hz; LF, 0.04-0.15 Hz; and KF, 0.15-0.45 Hz. New variables based on the instantaneous frequency and energy functions using TFR and time-domain analysis allow to discriminate HCM patients with high risk and low risk of SCD (p<0.05). Results shown that TFR analysis of the HRV seems to present more robustness than FFT analysis in order to characterize HRV.

Characterization of the cerebral activity by time?frequency representation of evoked EEG potentials

Event-related brain potentials (ERPs) are the electrical response of the brain while performing a particular task. Methods traditionally used to study ERPs measure the amplitude and duration of the waveform in order to quantify the changes, being signal morphology dependent. However, the frequency characteristics of those events remain uncovered. The aim of this work was the study of new measures to characterize, by means of time-frequency representation (TFR) techniques, the ERPs recorded while subjects conducted a choice reaction time task (Ericksen flanker task) following the administration of different alprazolam doses. Several measures defined from energy, instantaneous frequency and group delay functions were obtained by means of TFR techniques applied to the Choi-Williams distribution (CWD) of EEG signals. These measures, which are signal morphology independent, were studied in four frequency bands, δ (0-4 Hz), θ (4-8 Hz), α (8-15 Hz), β (15-30 Hz), and for certain time periods. Based on these measures, differences between ERPs were analyzed by comparing the different response types (successes or successfully corrected failures) of the subject performing the task, and comparing the applied drug doses. For each subject, the CWD of EEG signals was applied in two different ways: (a) all ERPs were averaged per channel, and then the CWD was applied; (b) the CWD was applied to each one of the ERPs. When the CWD was applied to each ERP, the energy measures in the δ, θ and β bands, the instantaneous frequency measures in the α and β bands, and the group delay measures in the δ, θ and α bands showed a statistically significant level p < 0.0005 in the analysis of the response type. Also, the energy measures in the θ and β bands and the instantaneous frequency measures in the α band showed statistically significant differences (p < 0.0005) between placebo and low and high drug doses. In contrast, poor results were obtained when all epochs of each subject were averaged per channel. Finally, it was concluded that these results showed that the new proposed measures based on the energy offered a new and more robust way to characterize ERP signals.

Prediction of nociceptive responses during sedation by time-frequency representation

The level of sedation in patients undergoing medical procedures evolves continuously, such as the effect of the anesthetic and analgesic agents is counteracted by pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to analyze the capability of prediction of nociceptive responses based on the time-frequency representation (TFR) of EEG signal. Functions of spectral entropy, instantaneous power and instantaneous frequency were calculated in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% and 65% respectively were achieved combining TFR functions with bispectral index (BIS) and with concentrations of propofol (CeProp) and remifentanil (CeRemi).

Time-frequency analysis of heart rate variability as an aid to diagnose pathologies

The heart rate variability is a diagnostic measure of great clinical interest. Previous results have shown that the position of the maximum peak of the heart rate power spectrum could be an index to distinguish in postmyocardial infarction (PMI) patients those that later develop malignant ventricular arrhythmia (MVB). The instantaneous frequencies in the low frequency band (LF, 0-0.07 Hz), mean frequency band (MF, 0.07-0.15 Hz), and high frequency band (HF, 0.15-0.45 Hz) have been obtained by means of the time-frequency analysis using the Choi-Williams distribution. Two groups of patients have been selected in this study: group (a) consisted of 9 PMI patients who presented MVA during or after Holter recording, and group (b) consisted of 10 PMI patients without MVA. This work shows that the Choi-Williams time-frequency distribution of the R-R signal is useful to obtain new discriminatory variables for these patients. The difference between the mean frequencies in the MF and LF bands during the daytime allows to separate both groups, and the statistical significance is greatly increased (p<0.001) in a multivariate analysis when the instantaneous frequencies in the LF, MF, HF bands at the daytime and nocturnal period are considered separately.

Removal of peak and spike noise in EEG signals based on the analytic signal magnitude

Peak and spike artifacts in time series represent a serious problem for signal analysis especially in biomedical field. From the last decades, different techniques have been used for their removal mainly based on adaptive filters. This work presents a new approach for removing peak and spike artifacts based on the analytic signal envelope, filtered with a low-pass filter. The proposed algorithm was tested on electroencephalogram signals containing peak and spike artifacts. Results showed that this method permitted to remove the peak and spike artifacts preserving both high correlation (ρ>;0.9) and spectral coherence (C(f))̅ >; 0.85) with the original signal.