Early recovery of frontal EEG slow wave activity during propofol sedation predicts outcome after cardiac arrest.

AIM OF THE STUDY: EEG slow wave activity (SWA) has shown prognostic potential in post-resuscitation care. In this prospective study, we investigated the accuracy of continuously measured early SWA for prediction of the outcome in comatose cardiac arrest (CA) survivors. METHODS: We recorded EEG with a disposable self-adhesive frontal electrode and wireless device continuously starting from ICU admission until 48 h from return of spontaneous circulation (ROSC) in comatose CA survivors sedated with propofol. We determined SWA by offline calculation of C-Trend® Index describing SWA as a score ranging from 0 to 100. The functional outcome was defined based on Cerebral Performance Category (CPC) at 6 months after the CA to either good (CPC 1-2) or poor (CPC 3-5). RESULTS: Outcome at six months was good in 67 of the 93 patients. During the first 12 h after ROSC, the median C-Trend Index value was 38.8 (interquartile range 28.0-56.1) in patients with good outcome and 6.49 (3.01-18.2) in those with poor outcome showing significant difference (p < 0.001) at every hour between the groups. The index values of the first 12 h predicted poor outcome with an area under curve of 0.86 (95% CI 0.61-0.99). With a cutoff value of 20, the sensitivity was 83.3% (69.6%-92.3%) and specificity 94.7% (83.4%-99.7%) for categorization of outcome. CONCLUSION: EEG SWA measured with C-Trend Index during propofol sedation offers a promising practical approach for early bedside evaluation of recovery of brain function and prediction of outcome after CA.

Forehead electrodes sufficiently detect propofol-induced slow waves for the assessment of brain function after cardiac arrest.

In a recent study, we proposed a novel method to evaluate hypoxic ischemic encephalopathy (HIE) by assessing propofol-induced changes in the 19-channel electroencephalogram (EEG). The study suggested that patients with HIE are unable to generate EEG slow waves during propofol anesthesia 48 h after cardiac arrest (CA). Since a low number of electrodes would make the method clinically more practical, we now investigated whether our results received with a full EEG cap could be reproduced using only forehead electrodes. Experimental data from comatose post-CA patients (N = 10) were used. EEG was recorded approximately 48 h after CA using 19-channel EEG cap during a controlled propofol exposure. The slow wave activity was calculated separately for all electrodes and four forehead electrodes (Fp1, Fp2, F7, and F8) by determining the low-frequency (< 1 Hz) power of the EEG. HIE was defined by following the patients' recovery for six months. In patients without HIE (N = 6), propofol substantially increased (244 ± 91%, mean ± SD) the slow wave activity in forehead electrodes, whereas the patients with HIE (N = 4) were unable to produce such activity. The results received with forehead electrodes were similar to those of the full EEG cap. With the experimental pilot study data, the forehead electrodes were as capable as the full EEG cap in capturing the effect of HIE on propofol-induced slow wave activity. The finding offers potential in developing a clinically practical method for the early detection of HIE.

Pilot Study of Propofol-induced Slow Waves as a Pharmacologic Test for Brain Dysfunction after Brain Injury.

BACKGROUND: Slow waves (less than 1 Hz) are the most important electroencephalogram signatures of nonrapid eye movement sleep. While considered to have a substantial importance in, for example, providing conditions for single-cell rest and preventing long-term neural damage, a disturbance in this neurophysiologic phenomenon is a potential indicator of brain dysfunction. METHODS: Since, in healthy individuals, slow waves can be induced with anesthetics, the authors tested the possible association between hypoxic brain injury and slow-wave activity in comatose postcardiac arrest patients (n = 10) using controlled propofol exposure. The slow-wave activity was determined by calculating the low-frequency (less than 1 Hz) power of the electroencephalograms recorded approximately 48 h after cardiac arrest. To define the association between the slow waves and the potential brain injury, the patients' neurologic recovery was then followed up for 6 months. RESULTS: In the patients with good neurologic outcome (n = 6), the low-frequency power of electroencephalogram representing the slow-wave activity was found to substantially increase (mean ± SD, 190 ± 83%) due to the administration of propofol. By contrast, the patients with poor neurologic outcome (n = 4) were unable to generate propofol-induced slow waves. CONCLUSIONS: In this experimental pilot study, the comatose postcardiac arrest patients with poor neurologic outcome were unable to generate normal propofol-induced electroencephalographic slow-wave activity 48 h after cardiac arrest. The finding might offer potential for developing a pharmacologic test for prognostication of brain injury by measuring the electroencephalographic response to propofol.

How young adults with autism spectrum disorder watch and interpret pragmatically complex scenes.

The aim of the current study was to investigate subtle characteristics of social perception and interpretation in high-functioning individuals with autism spectrum disorders (ASDs), and to study the relation between watching and interpreting. As a novelty, we used an approach that combined moment-by-moment eye tracking and verbal assessment. Sixteen young adults with ASD and 16 neurotypical control participants watched a video depicting a complex communication situation while their eye movements were tracked. The participants also completed a verbal task with questions related to the pragmatic content of the video. We compared verbal task scores and eye movements between groups, and assessed correlations between task performance and eye movements. Individuals with ASD had more difficulty than the controls in interpreting the video, and during two short moments there were significant group differences in eye movements. Additionally, we found significant correlations between verbal task scores and moment-level eye movement in the ASD group, but not among the controls. We concluded that participants with ASD had slight difficulties in understanding the pragmatic content of the video stimulus and attending to social cues, and that the connection between pragmatic understanding and eye movements was more pronounced for participants with ASD than for neurotypical participants.

Automatic Parametrization of Somatosensory Evoked Potentials With Chirp Modeling.

In this paper, an approach using polynomial phase chirp signals to model somatosensory evoked potentials (SEPs) is proposed. SEP waveforms are assumed as impulses undergoing group velocity dispersion while propagating along a multipath neural connection. Mathematical analysis of pulse dispersion resulting in chirp signals is performed. An automatic parameterization of SEPs is proposed using chirp models. A Particle Swarm Optimization algorithm is used to optimize the model parameters. Features describing the latencies and amplitudes of SEPs are automatically derived. A rat model is then used to evaluate the automatic parameterization of SEPs in two experimental cases, i.e., anesthesia level and spinal cord injury (SCI). Experimental results show that chirp-based model parameters and the derived SEP features are significant in describing both anesthesia level and SCI changes. The proposed automatic optimization based approach for extracting chirp parameters offers potential for detailed SEP analysis in future studies. The method implementation in Matlab technical computing language is provided online.

Using Hilbert-Huang Transform to assess EEG slow wave activity during anesthesia in post-cardiac arrest patients.

Hypoxic ischemic encephalopathy (HIE) is a severe consequence of cardiac arrest (CA) representing a substantial diagnostic challenge. We have recently designed a novel method for the assessment of HIE after CA. The method is based on estimating the severity of the brain injury by analyzing changes in the electroencephalogram (EEG) slow wave activity while the patient is exposed to an anesthetic drug propofol in a controlled manner. In this paper, Hilbert-Huang Transform (HHT) was used to analyze EEG slow wave activity during anesthesia in ten post-CA patients. The recordings were made in the intensive care unit 36-48 hours after the CA in an experiment, during which the propofol infusion rate was incrementally decreased to determine the drug-induced changes in the EEG at different anesthetic levels. HHT was shown to successfully capture the changes in the slow wave activity to the behavior of intrinsic mode functions (IMFs). While, in patients with good neurological outcome defined after a six-month control period, propofol induced a significant increase in the amplitude of IMFs representing the slow wave activity, the patients with poor neurological outcome were unable to produce such a response. Consequently, the proposed method offer substantial prognostic potential by providing a novel approach for early estimation of HIE after CA.

High-frequency electroencephalographic activity in left temporal area is associated with pleasant emotion induced by video clips.

Recent findings suggest that specific neural correlates for the key elements of basic emotions do exist and can be identified by neuroimaging techniques. In this paper, electroencephalogram (EEG) is used to explore the markers for video-induced emotions. The problem is approached from a classifier perspective: the features that perform best in classifying person's valence and arousal while watching video clips with audiovisual emotional content are searched from a large feature set constructed from the EEG spectral powers of single channels as well as power differences between specific channel pairs. The feature selection is carried out using a sequential forward floating search method and is done separately for the classification of valence and arousal, both derived from the emotional keyword that the subject had chosen after seeing the clips. The proposed classifier-based approach reveals a clear association between the increased high-frequency (15-32 Hz) activity in the left temporal area and the clips described as "pleasant" in the valence and "medium arousal" in the arousal scale. These clips represent the emotional keywords amusement and joy/happiness. The finding suggests the occurrence of a specific neural activation during video-induced pleasant emotion and the possibility to detect this from the left temporal area using EEG.

Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform.

Slow waves (<; 1 Hz) are considered to be the most important electroencephalogram (EEG) signature of non-rapid eye movement sleep and have substantial physiological importance. In addition to natural sleep, slow waves can be seen in the EEG during general anesthesia offering great potential for depth of anesthesia monitoring. In this paper, Hilbert-Huang Transform, an adaptive data-driven method designed for the analysis on non-stationary data, was used to investigate the dynamical changes in the EEG slow wave activity during induction of anesthesia with propofol. The method was found to be able to extract stable signal components representing slow wave activity that were consistent between patients. The signal analysis revealed a possible specific structure between different components dependent on the depth of anesthesia on which further studies are needed.

EEG-based detection of awakening from isoflurane anesthesia in rats.

In animal studies, reliable measures for depth of anesthesia are frequently required. Previous findings suggest that the continuous depth of anesthesia indices developed for humans might not be adequate for rats whose EEG changes during anesthesia represent more of quick transitions between discrete states. In this paper, the automatic EEG-based detection of awakening from anesthesia was studied in rats. An algorithm based on Bayesian Information Criterion (BIC) is proposed for the assessment of the switch-like change in the signal characteristics occurring just before the awakening. The method was tested with EEGs recorded from ten rats recovering from isoflurane anesthesia. The algorithm was shown to be able to detect the sudden change in the EEG related to the moment of awakening with a precision comparable to careful visual inspection. Our findings suggest that monitoring such signal changes may offer an interesting alternative to the application of continuous depth of anesthesia indices when avoiding the awakening of the animal during e.g. a clinical experiment.

Depth of anesthesia during multidrug infusion: separating the effects of propofol and remifentanil using the spectral features of EEG.

General anesthesia is usually induced with a combination of drugs. In addition to the hypnotic agent, such as propofol, opioids are often used due to their synergistic hypnotic and analgesic properties. However, the effects of opioids on the EEG changes and the clinical state of the patient during anesthesia are complex and hinder the interpretation of the EEG-based depth of anesthesia indexes. In this paper, a novel technology for separating the anesthetic effects of propofol and an ultrashort-acting opioid, remifentanil, using the spectral features of EEG is proposed. By applying a floating search method, a well-performing feature set is achieved to estimate the effects of propofol during induction of anesthesia and to classify whether or not remifentanil has been coadministered. It is shown that including the detection of the presence of opioids to the estimated effect of propofol significantly improves the determination of the clinical state of the patient, i.e., if the patient will respond to a painful stimulation.

Isomap approach to EEG-based assessment of neurophysiological changes during anesthesia.

Increasing concentrations of anesthetics in the blood induce a continuum of neurophysiological changes, which reflect on the electroencephalogram (EEG). EEG-based depth of anesthesia assessment requires that the signal samples are correctly associated with the neurophysiological changes occurring at different anesthetic levels. A novel method is presented to estimate the phase of the continuum using the feature data extracted from EEG. The feature data calculated from EEG sequences corresponding to continuously deepening anesthesia are considered to form a one-dimensional nonlinear manifold in the multidimensional feature space. Utilizing a recently proposed algorithm, Isomap, the dimensionality of the feature data is reduced to achieve a one-dimensional embedding representing this manifold and thereby the continuum of neurophysiological changes during induction of anesthesia. The Isomap-based estimation is validated with data recorded from nine patients during induction of propofol anesthesia. The proposed method provides a novel approach to assess neurophysiological changes during anesthesia and offers potential for the development of more advanced systems for the depth of anesthesia monitoring.

Effect of cognitive load on speech prosody in aviation: Evidence from military simulator flights.

Mental overload directly affects safety in aviation and needs to be alleviated. Speech recordings are obtained non-invasively and as such are feasible for monitoring cognitive load. We recorded speech of 13 military pilots while they were performing a simulator task. Three types of cognitive load (load on situation awareness, information processing and decision making) were rated by a flight instructor separately for each flight phase and participant. As a function of increased cognitive load, the mean utterance-level fundamental frequency (F0) increased, on average, by 7 Hz and the mean vocal intensity increased by 1 dB. In the most intensive simulator flight phases, mean F0 increased by 12 Hz and mean intensity, by 1.5 dB. At the same time, the mean F0 range decreased by 5 Hz, on average. Our results showed that prosodic features of speech can be used to monitor speaker state and support pilot training in a simulator environment.

Perception of basic emotions from speech prosody in adolescents with Asperger's syndrome.

Asperger's syndrome (AS) belongs to the group of autism spectrum disorders and is characterized by deficits in social interaction, as manifested e.g. by the lack of social or emotional reciprocity. The disturbance causes clinically significant impairment in social interaction. Abnormal prosody has been frequently identified as a core feature of AS. There are virtually no studies on recognition of basic emotions from speech. This study focuses on how adolescents with AS (n=12) and their typically developed controls (n=15) recognize the basic emotions happy, sad, angry, and 'neutral' from speech prosody. Adolescents with AS recognized basic emotions from speech prosody as well as their typically developed controls did. Possibly the recognition of basic emotions develops during the childhood.

Monopitched expression of emotions in different vowels.

Fundamental frequency (F(0)) and intensity are known to be important variables in the communication of emotions in speech. In singing, however, pitch is predetermined and yet the voice should convey emotions. Hence, other vocal parameters are needed to express emotions. This study investigated the role of voice source characteristics and formant frequencies in the communication of emotions in monopitched vowel samples [a:], [i:] and [u:]. Student actors (5 males, 8 females) produced the emotional samples simulating joy, tenderness, sadness, anger and a neutral emotional state. Equivalent sound level (L(eq)), alpha ratio [SPL (1-5 kHz) - SPL (50 Hz-1 kHz)] and formant frequencies F1-F4 were measured. The [a:] samples were inverse filtered and the estimated glottal flows were parameterized with the normalized amplitude quotient [NAQ = f(AC)/(d(peak)T)]. Interrelations of acoustic variables were studied by ANCOVA, considering the valence and psychophysiological activity of the expressions. Forty participants listened to the randomized samples (n = 210) for identification of the emotions. The capacity of monopitched vowels for conveying emotions differed. L(eq) and NAQ differentiated activity levels. NAQ also varied independently of L(eq). In [a:], filter (formant frequencies F1-F4) was related to valence. The interplay between voice source and F1-F4 warrants a synthesis study.

Emotions in [a]: a perceptual and acoustic study.

The aim of this investigation is to study how well voice quality conveys emotional content that can be discriminated by human listeners and the computer. The speech data were produced by nine professional actors (four women, five men). The speakers simulated the following basic emotions in a unit consisting of a vowel extracted from running Finnish speech: neutral, sadness, joy, anger, and tenderness. The automatic discrimination was clearly more successful than human emotion recognition. Human listeners thus apparently need longer speech samples than vowel-length units for reliable emotion discrimination than the machine, which utilizes quantitative parameters effectively for short speech samples.

Automatic discrimination of emotion from spoken Finnish.

In this paper, experiments on the automatic discrimination of basic emotions from spoken Finnish are described. For the purpose of the study, a large emotional speech corpus of Finnish was collected; 14 professional actors acted as speakers, and simulated four primary emotions when reading out a semantically neutral text. More than 40 prosodic features were derived and automatically computed from the speech samples. Two application scenarios were tested: the first scenario was speaker-independent for a small domain of speakers while the second scenario was completely speaker-independent. Human listening experiments were conducted to assess the perceptual adequacy of the emotional speech samples. Statistical classification experiments indicated that, with the optimal combination of prosodic feature vectors, automatic emotion discrimination performance close to human emotion recognition ability was achievable.