Comparison of the Conox (qCON) and Sedline (PSI) depth of anaesthesia indices to predict the hypnotic effect during desflurane general anaesthesia with ketamine.

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.

Thermal conductivity in disordered porous nanomembranes.

In this work we study the effects of disorder on the thermal conductivity of porous 100 nm thick silicon membranes, in which the size, shape and position of the pores were varied randomly. Measurements using two-laser Raman thermometry on both non-patterned and porous membranes revealed more than a 10-fold reduction of the thermal conductivity compared to that of bulk silicon and a six-fold reduction compared to non-patterned membranes for the sample with random pore shapes. Using Monte Carlo methods we solved the Boltzmann transport equation for phonons and compared different possibilities of pore organization and its influence on the thermal conductivity of the samples. The simulations confirmed that the strongest reduction of thermal conductivity is achieved for a distribution of pores with arbitrary shapes that partially overlap. Up to a 15% reduction of the thermal conductivity with respect to the purely circular pores was predicted for a porous membrane with 37% filling fraction. The effect of the pore shape and distribution was further studied. Maps of temperature and heat flux distributions clearly showed that for particular pore placement heat transport can be efficiently blocked and hot spots can be found in narrow channels between pores. These findings have an impact on the fabrication of membrane-based thermoelectric devices, where low thermal conductivity is required. This work shows that for porous membranes with a given filling fraction the thermal conductivity can be further modified by introducing disorder in the shape and placement of the pores.

Comparison of the qCON and qNOX indices for the assessment of unconsciousness level and noxious stimulation response during surgery.

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.

Regularity of cardiac rhythm as a marker of sleepiness in sleep disordered breathing.

AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Prediction of Nociceptive Responses during Sedation by Linear and Non-Linear Measures of EEG Signals in High Frequencies.

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.

Mutual information measures applied to EEG signals for sleepiness characterization.

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in ß band during MSLT events (p-value < 0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients.

Measuring heart rate variability by means of information entropies based on Choi-Williams distribution.

The Shannon entropy theory was applied to the Choi-Williams time-frequency distribution (CWD) of cardiac time series (RR series) in order to extract entropy information in both time and frequency domains. From this distribution, four indexes were defined: (1) instantaneous partial entropy; (2) spectral partial entropy; (3) instantaneous complete entropy; (4) spectral complete entropy. These indexes were used for analyzing the heart rate variability of ischemic cardiomyopathy patients (ICM) with different sudden cardiac death risk. The results have shown that the values of these indexes tend to decrease, with different proportion, when the severity of pathological condition increases. Statistical differences (p-value < 0.0005) of these indexes were found comparing low risk and high risk of cardiac death during night and between daytime and nighttime periods of ICM patients. Finally, these indexes have demonstrated to be useful tools to quantify the different complex components of the cardiac time series.

Correntropy measures to detect daytime sleepiness from EEG signals.

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders and has a great impact on patients' 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 novel approach to this issue based on correntropy function analysis of EEG signals was proposed in order to detect patients suffering from EDS. Multichannel EEG signals were recorded during five Maintenance of Wakefulness Tests (MWT) and Multiple Sleep Latency Tests (MSLT) alternated throughout the day for patients suffering from sleep disordered breathing (SDB). 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 a waking state. Measures obtained from the cross-correntropy function (CCORR) and auto-correntropy function (ACORR) were calculated in the EEG frequency bands: δ, 0.1-4 Hz; θ, 4-8 Hz; α, 8-12 Hz; ß, 12-30 Hz; total band TB, 0.1-45 Hz. These functions permitted the quantification of complex signal properties and the non-linear couplings between different areas of the scalp. Statistical differences between EDS and WDS groups were mainly found in the ß band during MSLT events (p-value < 0.0001). The WDS group presented more complexity in the occipital zone than the EDS group, while a stronger nonlinear coupling between the occipital and frontal regions was detected in EDS patients than in the WDS group. At best, ACORR and CCORR measures yielded sensitivity and specificity above 80% and the area under ROC curve (AUC) was above 0.85 in classifying EDS and WDS patients. These performances represent an improvement with respect to classical EEG indices applied in the same database (sensitivity and specificity were never above 80% and AUC was under 0.75).

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).

Clinical decision support system to enhance quality control of spirometry using information and communication technologies.

BACKGROUND: We recently demonstrated that quality of spirometry in primary care could markedly improve with remote offline support from specialized professionals. It is hypothesized that implementation of automatic online assessment of quality of spirometry using information and communication technologies may significantly enhance the potential for extensive deployment of a high quality spirometry program in integrated care settings. OBJECTIVE: The objective of the study was to elaborate and validate a Clinical Decision Support System (CDSS) for automatic online quality assessment of spirometry. METHODS: The CDSS was done through a three step process including: (1) identification of optimal sampling frequency; (2) iterations to build-up an initial version using the 24 standard spirometry curves recommended by the American Thoracic Society; and (3) iterations to refine the CDSS using 270 curves from 90 patients. In each of these steps the results were checked against one expert. Finally, 778 spirometry curves from 291 patients were analyzed for validation purposes. RESULTS: The CDSS generated appropriate online classification and certification in 685/778 (88.1%) of spirometry testing, with 96% sensitivity and 95% specificity. CONCLUSIONS: Consequently, only 93/778 (11.9%) of spirometry testing required offline remote classification by an expert, indicating a potential positive role of the CDSS in the deployment of a high quality spirometry program in an integrated care setting.

Algorithm for automatic forced spirometry quality assessment: technological developments.

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.

Interaction between EEG and drug concentration to predict response to noxious stimulation during sedation-analgesia: effect of the A118G genetic polymorphism.

The level of sedation in patients undergoing medical procedures is affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The presence of the A118G single nucleotide polymorphism (SNP) in the OPRM1 gene affects the requirements of opioids for patients undergoing sedation-analgesia. The purpose of this work is to evaluate the influence of the SNP A118G in OPRM1 on EEG measures for the prediction of the response to pain stimulation during endoscopy procedure. The proposed measures were based on power spectral density and auto-mutual information function. It was found that the statistical performances of the EEG measures improved when the presence of the SNP was taken into account (prediction probability Pk>0.9).

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).

Analysis of epileptic EEG signals in children by symbolic dynamics.

Epilepsy is one of the most prevalent neurological disorders among children. The study of surface EEG signals in patients with epilepsy by techniques based on symbolic dynamics can provide new insights into the epileptogenic process and may have considerable utility in the diagnosis and treatment of epilepsy. The goal of this work was to find patterns from a methodology based on symbolic dynamics to characterize seizures on surface EEG in pediatric patients with intractable epilepsy. A total of 76 seizures were analyzed by their pre-ictal, ictal and post-ictal phases. An analytic signal envelope algorithm was applied to each EEG segment and its performance was evaluated. Several variables were defined from the distribution of words constructed on the EEG transformed into symbols. The results showed strong evidences of detectable non-linear changes in the EEG dynamics from pre-ictal to ictal phase and from ictal to post-ictal phase, with an accuracy higher than 70%.

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 equation with the original signal.

Auto-mutual information function of the EEG as a measure of depth of anesthesia.

Monitoring the depth of anesthesia (DOA) is necessary in order to decrease the incident of awareness in anesthesia and to prevent delays in the recovery phase. In the last decades a number of noninvasive methods have been proposed for the analysis of the electroencephalogram (EEG) for monitoring DOA. The objective of this work was to apply auto mutual information function (AMIF) to EEGs of patients under anesthesia in order to find variables able to characterize the following 4 states: awake, sedated, anesthetized and burst suppression episodes. The results show that the single and combined AMIF parameters were able to correctly classify the states in the range 72.2%-94.1% and 61.1%-100%, respectively.

Time-frequency characterization of mismatch negativity in nociceptive responses.

An efficient way to investigate the neural basis of nociceptive responses is the event-related brain potentials (ERPs). One component belonging to this family of ERPs is the mismatch negativity (MMN). It reflects pre-attentive detection of changes in the incoming stimulus by comparing the new stimulus with sensory memory traces. In this work, single trials of ERP taken from EEG signal recorded under thermal and electric stimulation were analyzed with time-frequency representation (TFR). The main objective of this work was to characterize responses to frequent and infrequent stimuli with TFR functions. Variables defined on instantaneous frequency and instantaneous power presented a statistical significance (p-value<0.0001) differentiating these two kind of responses. Furthermore, differences between the averaged instantaneous power and instantaneous frequency were also analyzed. It was found that instantaneous power and instantaneous frequency were able to better isolate the MMN components from EEG noise in certain frequency bands.

Exons and introns characterization in nucleic acid sequences by time-frequency analysis.

A current problem in deoxyribonucleic acid (DNA) sequence analysis is to determine the exact locations of the genes and also in eukaryotes, the protein-coding regions in the mRNA primary transcript (pre-mRNA).The conversion into discrete numerical values of the symbols associated to the nucleotides of these sequences allows for a signal to address the problems related to localization and annotation of genes. In this work, thermodynamic data of free energy changes (ΔG°) on the formation of a duplex structure of DNA or RNA are used to convert the symbols into numerical values associated with the nucleotide sequence pre-mRNA. This study presents an analysis, based on techniques of time-frequency representation of a large number of gene sequences, in order to find variables related to pre-mRNA that could best characterize and discriminate coding regions from non-coding regions. It has been found that instantaneous frequency variables and instantaneous spectral energy variables in different frequency bands, allowed exons and introns to be correctly classified with more than 85%.