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The objective of this work is to compare the performances of two electroencephalogram based indices for detecting loss of consciousness and loss of response to nociceptive stimulation. Specifically, their behaviour after drug induction and during recovery of consciousness was pointed out. Data was recorded from 140 patients scheduled for general anaesthesia with a combination of propofol and remifentanil. The qCON 2000 monitor (Quantium Medical, Barcelona, Spain) was used to calculate the qCON and qNOX. Loss of response to verbal command and loss of eye-lash reflex were assessed during the transition from awake to anesthetized, defining the state of loss of consciousness. Movement as a response to laryngeal mask (LMA) insertion was interpreted as the response to the nociceptive stimuli. The patients were classified as movers or non-movers. The values of qCON and qNOX were statistically compared. Their fall times and rise times defined at the start and at the end of the surgery were calculated and compared. The results showed that the qCON was able to predict loss of consciousness such as loss of verbal command and eyelash reflex better than qNOX, while the qNOX has a better predictive value for response to noxious stimulation such as LMA insertion. From the analysis of the fall and rise times, it was found that the qNOX fall time (median: 217 s) was significantly longer ( p value <0.05) than the qCON fall time (median: 150 s). At the end of the surgery, the qNOX started to increase in median at 45 s before the first annotation related to response to stimuli or recovery of consciousness, while the qCON at 88 s after the first annotation related to response to stimuli or recovery of consciousness ( p value <0.05). The indices qCON and qNOX showed different performances in the detection of loss of consciousness and loss of response to stimuli during induction and recovery of consciousness. Furthermore, the qCON showed faster decrease during induction. This behaviour is associated with the hypothesis that the loss of response to stimuli (analgesic effect) might be reached after the loss of consciousness (hypnotic effect). On the contrary, the qNOX showed a faster increase at the end of the surgery, associated with the hypothesis that a higher probability of response to stimuli might be reached before the recovery of consciousness.

Comparison of two depth of anesthesia indices, qCON (Conox) and PSI (Sedline), during desflurane sedation and their sensitivity to random ketamine boluses in patients undergoing routine surgery. The performance of desflurane and ketamine on both indices was analyzed for 11 patients, and the ketamine sensitivity was compared with another group of 11 patients under sevoflurane and propofol.The MOAA/S was used to determine sedation level and pain. Different boluses of ketamine ranging from 10 to 30 mg where randomly administered in both groups and the effect on the indexes were measured after 4 min.The indices were recorded during the whole surgery, and their correlations with the desflurane concentration and the discrimination between awake and anesthetized states were evaluated with the prediction probability statistic (Pk). The Pk values, mean (se), discriminating between awake and anesthetized states were 0.974(0.016) for the qCON and 0.962(0.0123) for the PSI, while the 1-Pk statistic for the qCON and the PSI with respect to the desflurane concentration were 0.927(0.016) and 0.918(0.018), respectively, with no statistically significant differences.The agreement between both depth of hypnosis parameters was assessed under the Bland-Altman plot and the Spearman correlation, rs = 0.57(p < 0.001).During the sevoflurane-propofol anesthesia, which served as a control group, both indices experienced a similar behavior with a no significant change of their median values after ketamine. However, during desflurane anesthesia the qCON index did not change significantly after ketamine administration, qCON (before = 33 (4), after = 30 (17); Wilcoxon, p = 0.89), while the PSI experienced a significant increase, PSI (before = 31(6), after = 39(16) Wilcoxon, p = 0.013).This study shows that qCON and PSI have similar performance under desflurane with good discrimination between the awake and anesthetized states. While both indices exhibited similar behavior under ketamine boluses under a sevoflurane-propofol anesthesia, the qCON index had a better performance under ketamine during desflurane anesthesia.

The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the 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 improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS). Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.

The level of sedation in patients undergoing medical procedures is decided to assure unconsciousness and prevent pain. 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 was to analyze the capability of prediction of nociceptive responses based on refined multiscale entropy (RMSE) and auto mutual information function (AMIF) applied to EEG signals recorded in 378 patients scheduled to undergo ultrasonographic endoscopy under sedation-analgesia. Two observed categorical responses after the application of painful stimulation were analyzed: the evaluation of the Ramsay Sedation Scale (RSS) after nail bed compression and the presence of gag reflex (GAG) during endoscopy tube insertion. In addition, bispectrum (BIS), heart rate (HR), predicted concentrations of propofol (CeProp) and remifentanil (CeRemi) were annotated with a resolution of 1 s. Results showed that functions based on RMSE, AMIF, HR and CeRemi permitted predicting different stimulation responses during sedation better than BIS.

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.

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.

The present study aimed to analyse the autonomic nervous system activity using heart rate variability (HRV) to detect sleep disordered breathing (SDB) patients with and without excessive daytime sleepiness (EDS) before sleep onset. Two groups of 20 patients with different levels of daytime sleepiness -sleepy group, SG; alert group, AG- were selected consecutively from a Maintenance of Wakefulness Test (MWT) and Multiple Sleep Latency Test (MSLT) research protocol. The first waking 3-min window of RR signal at the beginning of each nap test was considered for the analysis. HRV was measured with traditional linear measures and with time-frequency representations. Non-linear measures -correntropy, CORR; auto-mutual-information function, AMIF- were used to describe the regularity of the RR rhythm. Statistical analysis was performed with non-parametric tests. Non-linear dynamic of the RR rhythm was more regular in the SG than in the AG during the first wakefulness period of MSLT, but not during MWT. AMIF (in high-frequency and in Total band) and CORR (in Total band) yielded sensitivity > 70%, specificity >75% and an area under ROC curve > 0.80 in classifying SG and AG patients. The regularity of the RR rhythm measured at the beginning of the MSLT could be used to detect SDB patients with and without EDS before the appearance of sleep onset.

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep-related disorders with a great impact on patient lives. While many studies have been carried out in order to assess daytime sleepiness, automatic EDS detection still remains an open problem. In this work, a detection approach based on the time–frequency analysis of electroencephalography (EEG) signals is proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency tests alternated throughout the day from patients suffering from sleep-disordered breathing. A group of 20 patients with EDS was compared with a group of 20 patients without daytime sleepiness (WDS) by analyzing 60-s EEG windows in the waking state. Measures obtained from the Choi–Williams distribution (CWD) and the cross-CWD were calculated in the EEG frequency bands δ (0.1–4 Hz), θ (4–8 Hz), α (8–12 Hz), β (12–30 Hz), and total band (TB, 0.1–45 Hz). Statistical differences between EDS and WDS groups were found in the δ and θ bands during MWT events ( p  < 0.0001). The results show that the EDS group presented more power in the θ band, while the WDS group presented higher spectral and cross-spectral entropy in the frontal zone in the δ band. In general, CWD and cross-CWD measures yielded sensitivities and specificities of above 80 %. The area under the receiver operating characteristic curve was above 0.85 for classifying EDS and WDS patients.

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.

The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the 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 improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS). Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.