Depth of anesthesia index using cumulative power spectrum.

Over the last ten years, monitors of depth of anesthesia have progressively been integrated in the clinical practice. Based on the analysis of the electroencephalogram (EEG), these monitors deliver an index that helps the anesthesiologist to determine the state of the patient during the surgery. Although they employ different kind of algorithms, spectral parameters are always taken into account to achieve the final indexes. In this work, a new spectral parameter based on the cumulative power spectrum is proposed. When compared to the Spectral Edge Frequency (SEF), a classic spectral parameter, the Cumulative Power Spectrum Index (CPSI) presents a higher correlation with reference indexes (AAI, BIS and CePROP) and a higher prediction probability of the state of the patient. Furthermore, when compared to the reference indexes, the CPSI shows similar performances in terms of correlation and presents a higher prediction probability than two of them (BIS and CePROP).

Detrended fluctuation analysis of EEG as a measure of depth of anesthesia.

For several decades, a number of methods have been developed for the noninvasive assessment of the level of consciousness during general anesthesia. In this paper, detrended fluctuation analysis is used to study the scaling behavior of the electroencephalogram as a measure of the level of consciousness. Three indexes are proposed in order to characterize the patient state. Statistical analysis demonstrates that they allow significant discrimination between the awake, sedated and anesthetized states. Two of them present a good correlation with established indexes of depth of anesthesia. The scaling behavior has been found related to the depth of anesthesia and the methodology allows real-time implementation, which enables its application in monitoring devices.

Approximate entropy of the electroencephalogram in healthy awake subjects and absence epilepsy patients.

The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (+/- SD) ApEn in EEG was 0.83 +/- 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 +/- 0.05) than during seizure-free intervals (0.80 +/- 0.13) (P < 0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.

Approximate entropy in the electroencephalogram during wake and sleep.

Entropy measurement can discriminate among complex systems, including deterministic, stochastic and composite systems. We evaluated the changes of approximate entropy (ApEn) in signals of the electroencephalogram (EEG) during sleep. EEG signals were recorded from eight healthy volunteers during nightly sleep. We estimated the values of ApEn in EEG signals in each sleep stage. The ApEn values for EEG signals (mean +/- SD) were 0.896 +/- 0.264 during eyes-closed waking state, 0.738 +/- 0.089 during Stage I, 0.615 +/- 0.107 during Stage II, 0.487 +/- 0.101 during Stage II, 0.397 +/- 0.078 during Stage IV and 0.789 +/- 0.182 during REM sleep. The ApEn values were found to differ with statistical significance among the six different stages of consciousness (ANOVA, p<0.001). ApEn of EEG was statistically significantly lower during Stage IV and higher during wake and REM sleep. We conclude that ApEn measurement can be useful to estimate sleep stages and the complexity in brain activity.

Approximate entropy of human respiratory movement during eye-closed waking and different sleep stages.

STUDY OBJECTIVE: The breath-to-breath variability of respiratory parameters changes with sleep stage. This study investigates any alteration in the approximate entropy (ApEn) of respiratory movement as a gauge of complexity in respiration, by stage of consciousness, in the light of putative brain interactions. PARTICIPANTS: Eight healthy men, who were between the ages of 23 and 29 years, were investigated. MEASUREMENTS AND RESULTS: The signals of chest wall movement and EEG were recorded from 10:30 PM to 6:00 AM. After analog-to-digital conversion, the ApEn of respiratory movement (3 min) and EEG (20 s) were computed. Surrogate data were tested for nonlinearity in the original time series. The most impressive reduction in the ApEn of respiratory movement was associated with stage IV sleep, when the ApEn of the EEG was also statistically significantly decreased. A statistically significant linear relation is found between the ApEn of both variables. Surrogate data indicated that respiratory movement had nonlinear properties during all stages of consciousness that were investigated. CONCLUSION: Respiratory movement and EEG signals are more regular during stage IV sleep than during other stages of consciousness. The change in complexity described by the ApEn of respiration depends in part on the ApEn of the EEG, suggesting the involvement of nonlinear dynamic processes in the coordination between brain and lungs.

Heart rate variability in emergency department patients with sepsis.

OBJECTIVE: To test the hypothesis that heart rate variability (HRV) can provide an early indication of illness severity among patients presenting to the emergency department (ED) with sepsis. METHODS: The authors enrolled a convenience sample of 15 ED patients meeting the American College of Chest Physicians/Society of Critical Care Medicine criteria for sepsis. Each patient had continuous Holter monitoring performed in the ED. Acute Physiology and Chronic Health II (APACHE II) and Sequential Organ Failure (SOFA) scores were calculated for the day of presentation. Holter tapes obtained in the ED were analyzed off-line to calculate HRV variables for the 5-minute segment with the least artifact and non-sinus beats. These variables were correlated with APACHE II and SOFA scores. RESULTS: LFnu (normalized low-frequency power), an assessment of the relative sympathetic contribution to overall HRV, was correlated with increased illness severity as calculated using APACHE II (r = -0.67, r(2) = 0.43) and SOFA (r = -0.80, r(2) = 0.64) scores. LF/HF ratio (low-frequency/high-frequency ratio), a measure of sympathovagal balance, was correlated with the SOFA score [r = -0.54 (95% CI = -0.83 to -0.01), r(2) = 0.29]. All five patients who required critical care monitoring or ventilatory support or who died during the first 5 days of their hospitalization had LFnu values below 0.5 and LF/HF ratios less than 1.0. None of the patients with measurements greater than these threshold values died or required these interventions during the five days following admission. CONCLUSIONS: A single variable, LFnu, which reflects sympathetic modulation of heart rate, accounted for 40-60% of the variance in illness severity scores among patients presenting to the ED with sepsis. HRV, as reflected in LFnu and the LF/HF ratio and measured with a single brief (5-minute) period of monitoring while in the ED, may provide the emergency physician with a readily available, noninvasive, early marker of illness severity. The threshold effect of LFnu and LF/HF in the prediction of early clinical deterioration was an unexpected finding and should be regarded as hypothesis-generating, pending further study.

Nonlinear noise reduction for electrocardiograms.

The electrical activity of the heart usually shows dynamical behavior which is neither periodic nor deterministically chaotic: The interbeat intervals seem to contain a random component. Although long term predictions are thus impossible, good predictions can be made for times smaller than one heart cycle. This fact is used in order to suppress measurement errors by a local geometric projection method which was originally developed for chaotic signals. The result constitutes evidence that techniques of time series analysis based on chaos theory can be useful despite the fact that very few natural phenomena have been actually established to be deterministically chaotic. (c) 1996 American Institute of Physics.

Dynamics of heart rate.

Heart rate oscillates on several different time scales and has long-term variability in the form of 1/f noise. The physiological control of heart rate is briefly reviewed, and several typical patterns of heart rate variability, in health and sickness, are described. Considered briefly are some possible dynamical mechanisms for heart rate variability.