-
[show abstract]
[hide abstract]
ABSTRACT: The aim of the study was to investigate heart rate (HR) and respiratory rate (RR) complexity in patients with weaning failure or success, using both linear and nonlinear techniques.
Forty-two surgical patients were enrolled in the study. There were 24 who passed and 18 who failed a weaning trial. Signals were analyzed for 10 minutes during 2 phases: (1) pressure support (PS) ventilation (15-20 cm H(2)O) and (2) weaning trials with PS (5 cm H(2)O). Low- and high-frequency (LF, HF) components of HR signals, HR multiscale entropy (MSE), RR sample entropy, cross-sample entropy between cardiorespiratory signals, Poincaré plots, and α1 exponent were computed in all patients and during the 2 phases of PS.
Weaning failure patients exhibited significantly decreased RR sample entropy, LF, HF, and α1 exponent, compared with weaning success subjects (P < .001). Their changes were opposite between the 2 phases, except for MSE that increased between and within groups (P < .001). A new model including rapid shallow breathing index (RSBI), α1 exponent, RR, and cross-sample entropies predicted better weaning outcome compared with RSBI, airway occlusion pressure at 0.1 second (P(0.1)), and RSBI × P(0.1) (conventional model, R(2) = 0.887 vs 0.463; P < .001). Areas under the curve were 0.92 vs 0.86, respectively (P < .005).
We suggest that nonlinear analysis of cardiorespiratory dynamics has increased prognostic impact upon weaning outcome in surgical patients.
Journal of critical care 06/2011; 26(3):262-72. · 2.13 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Abstract Background Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients. Results Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H2O) and 2. weaning trials with PS: 5 cm H2O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P0.1 and RSBI* P0.1 (conventional model, R2 = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05). Conclusions We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.
BMC Physiology. 01/2011;
-
[show abstract]
[hide abstract]
ABSTRACT: The purpose of the study was to investigate longitudinally over time heart rate (HR) and blood pressure variability and baroreflex sensitivity in acute brain injury patients and relate them with the severity of neurologic dysfunction and outcome.
Data from 20 brain injured patients due to multiple causes and treated in the intensive care unit were used, with HR and blood pressure recorded from monitors and analyzed on a daily basis. We performed power spectral analysis estimating low frequencies (LF: 0.04-0.15 Hz), high frequencies (HF: 0.15-0.4 Hz), and their ratio and calculated the approximate entropy, which assesses periodicity within a signal and transfer function (TF), that estimates baroreflex sensitivity. Heart rate variance was considered as a measure of HR variability.
Nonsurvivors (brain dead) had lower approximate entropy (0.65 +/- 0.24 vs 0.84 +/- 0.26, P < .05) and lower variance mean values (0.48 +/- 0.54 vs 1.29 +/- 0.42 ms(2)/Hz, P < .01), lower LF and HF minimum values (0.31 +/- 0.88 vs 1.11 +/- 0.46, P < .01; and 0.27 +/- 0.42 vs 0.86 +/- 0.30, P < .01, respectively), lower LF/HF (0.22 +/- 0.29 vs 0.62 +/- 0.28, P < .01), and lower TF mean values (0.43 +/- 0.29 vs 1.11 +/- 0.74, P < .05) during their whole stay in the intensive care unit in relation with survivors. The mean variance (P < .05), mean TF (P < .05), and mean LF/HF (P < .05) were significantly successful in separating survivors from nonsurvivors.
We conclude that in acute brain injury patients, low variability, low baroreflex sensitivity, and sustained decrease in LF/HF of HR signals are linked with a high mortality rate.
Journal of critical care 10/2008; 23(3):380-6. · 2.13 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system.
Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools.
We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback-Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors.
EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device.
The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.
Clinical Neurophysiology 10/2007; 118(9):1906-22. · 3.41 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity.
Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined.
A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions.
The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies.
The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.
Clinical Neurophysiology 02/2007; 118(1):31-52. · 3.41 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The problem addressed in this work is sleepiness during driving, which often leads to accidents in the streets. Experiments with sleepy drivers took place and the EEG data were analysed in terms of non-linear methods. Sample entropy and phase synchronization variations were investigated within the signal sections corresponding to "driving events", i.e. driving mistakes or loss of control, as well as to periods of drowsiness and sleepiness, as compared to the periods of normal driving. Decreased sample entropy, indicating loss of complexity, and an increased phase synchronisation have been found in the preliminary study presented. The results are encouraging towards developing an alerting system for predicting and preventing driving accidents.
Studies in health technology and informatics 02/2007; 129(Pt 2):1294-8.
-
[show abstract]
[hide abstract]
ABSTRACT: The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' recovery outcome predicting the optimal time-point to disconnect the patient from mechanical ventilation.
Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO2) of 100%. Neurological Deficit Scores (NDS) were measured 24 h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO2 was decreased to 40% (weaning session), followed by a 5 min early recovery session, a 5 min recovery 1 session and a 5 min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order p were decomposed into mp and p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models.
A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome.
Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, compact, and substantial novel indicator of cerebral hypoxia and a potential predictor of the optimal time-point to disconnect the patient from the ventilator.
Quantitative EEG seems to be an important tool for ICU clinicians assisting them to decide for the patients' optimal time-point to disconnect the patient from the ventilator.
Clinical Neurophysiology 05/2006; 117(4):752-70. · 3.41 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To investigate longitudinally over time heart rate dynamics and relation with mortality and organ dysfunction alterations in patients admitted to a multidisciplinary intensive care unit.
Data from 53 patients were used, with heart rate recorded from monitors and analyzed on a daily basis (every morning) for 600 seconds and sampling rate at 250 Hz, from admission to the intensive care unit until final discharge from the unit. Variance, which is a measure of heart rate variability; exponent alpha2; and approximate entropy (ApEn), which assess long-range correlations and periodicity within a signal, respectively; were measured and compared with every day Sequential Organ Failure Assessment Score (SOFA) and mortality.
Nonsurvivors had lower ApEn mean (greater periodicity in their signals) and minimum values compared to survivors (0.53 +/- 0.25 vs 0.62 +/- 0.23, P = .04; 0.24 +/- 0.23 vs 0.48 +/- 0.23, P = .01, respectively). Patients in better conditions with SOFA of less than 7 (mean value) had higher variance and ApEn (more variable, less periodic signals) than those with SOFA of 7 or higher (0.47 +/- 0.51 vs 0.10 +/- 0.65, P < .001; 0.67 +/- 0.28 vs 0.49 +/- 0.24, P < .001, respectively). The alpha2 exponent and variance were correlated with length of stay (r = 0.55, P = .02, and r = 0.53, P = .02, respectively) and minimum ApEn with mortality (r = 0.41, P = .01).
Loss of variability and increase in periodicity in heart rate of critically ill patients are linked with parallel deterioration of organ dysfunction and high mortality.
Journal of Critical Care 04/2006; 21(1):95-103; discussion 103-4. · 2.13 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Driver sleepiness due to sleep deprivation is a causative factor in 1% to 3% of all motor vehicle crashes. In recent studies, the importance of developing driver fatigue countermeasure devices has been stressed, in order to help prevent driving accidents and errors. Although numerous physiological indicators are available to describe an individual's level of alertness, the EEG signal has been shown to be one of the most predictive and reliable, since it is a direct measure of brain activity. In the present study, multichannel EEG data that were collected from 20 sleep-deprived subjects during real environmental conditions of driving are presented for the first time. EEG data's annotation made by two independent Medical Doctors revealed an increase of slowing activity and an acute increase of the alpha waves 5-10 seconds before driving events. From the EEG data that were collected, the Relative Band Ratio (RBR) of the EEG frequency bands, the Shannon Entropy, and the Kullback-Leibler (KL) Entropy were estimated for each one second segment. The mean values of these measurements were estimated for 5 minutes periods. Analysis revealed a significant increase of alpha waves relevant band ratios (RBR), a decrease of gamma waves RBR, and a significant decrease of KL entropy when the first and the last 5-min periods were compared. A rapid decrease of both Shannon and K-L entropies was observed just before the driving events. Conclusively, EEG can assess effectively the brain activity alterations that occur a few seconds before sleeping/drowsiness events in driving, and its quantitative measurements can be used as potential sleepiness indicators for future development of driver fatigue countermeasure devices.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2006; 1:6201-4.
-
[show abstract]
[hide abstract]
ABSTRACT: Caffeine is characterised as a central nervous system stimulant, also affecting metabolic and cardiovascular functions. A number of studies have demonstrated an effect of caffeine on the excretion of catecholamines and their metabolites. Urinary epinephrine and norepinephrine have been shown to increase after caffeine administration. Similar trends were observed in our study in adrenaline (ADR) and noradrenaline (NORADR) levels and additionally a dose dependent effect of caffeine. The effect of caffeine on cognitive performance, blood pressure, and catecholamines was tested under resting conditions and under mental workload. Each subject performed the test after oral administration of 1 cup and then 3 cups of coffee. Root mean square error (RMSE) for the tracking task was continuously monitored. Blood pressure was also recorded before and after each stage of the experiment. Catecholamines were collected and measured for three different conditions as: at rest, after mental stress alone, after one dose of caffeine under stress, and after triple dose of caffeine under stress. Comparison of the performance of each stage with the resting conditions revealed statistically significant differences between group of smokers/coffee drinkers compared with the other two groups of non-coffee drinkers/non-smokers and non-smokers/coffee drinkers. There was no statistically significant difference between the last two groups. There was an increase of urine adrenaline with 1 cup of coffee and statistically significant increase of urine noradrenaline. Both catecholamines were significantly increased with triple dose of caffeine. Mental workload increased catecholamines. There was a dose dependent effect of caffeine on catecholamines.
Brain and Cognition 03/2003; 51(1):143-54. · 3.17 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Healthcare organizations are sufficiently rich in their infrastructure to handle the internal administrative and clinical processes, but the need to integrate the processes of geographically distributed and organizationally independent organizations is evident. Building business processes in the health care sector from a local process is a new challenge. We propose an extension of UML Language to model processes in the health care domain using workflow modelling techniques and examining interoperability concepts among heterogeneous environments. We extend the UML Activity Diagram –to HWADD 1 diagram-to support workflow topics as well as standardized Clinical Documents that are handled by the processes. The diagram can be used to identify the activities in the processes in health care and also the resources for the execution of the activities. Adding also the concept of clinical documents the diagram can represent, in a sound way, the processes in a regional health network and the required resources in a homogenized way. A use case is described through the use of the HWADD and detailed work on the documents to be assigned in the diagram should be scheduled.
