Monitoring of nighttime EEG slow-wave activity during dexmedetomidine infusion in patients with hyperactive ICU delirium: An observational pilot study.

BACKGROUND: The disturbance of sleep has been associated with intensive care unit (ICU) delirium. Monitoring of EEG slow-wave activity (SWA) has potential in measuring sleep quality and quantity. We investigated the quantitative monitoring of nighttime SWA and its association with the clinical evaluation of sleep in patients with hyperactive ICU delirium treated with dexmedetomidine. METHODS: We performed overnight EEG recordings in 15 patients diagnosed with hyperactive delirium during moderate dexmedetomidine sedation. SWA was evaluated by offline calculation of the C-Trend Index, describing SWA in one parameter ranging 0 to 100 in values. Average and percentage of SWA values <50 were categorized as poor. The sleep quality and depth was clinically evaluated by the bedside nurse using the Richards-Campbell Sleep Questionnaire (RCSQ) with scores <70 categorized as poor. RESULTS: Nighttime SWA revealed individual sleep structures and fundamental variation between patients. SWA was poor in 67%, sleep quality (RCSQ) in 67%, and sleep depth (RCSQ) in 60% of the patients. The category of SWA aligned with that of RCSQ-based sleep quality in 87% and RCSQ-based sleep depth in 67% of the patients. CONCLUSION: Both, SWA and clinical evaluation suggested that the quality and depth of nighttime sleep were poor in most patients with hyperactive delirium despite dexmedetomidine infusion. Furthermore, the SWA and clinical evaluation classifications were not uniformly in agreement. An objective mode such as practical EEG-based solution for sleep evaluation and individual drug dosing in the ICU setting could offer potential in improving sleep for patients with delirium.

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.

A sustainable artificial-intelligence-augmented digital care pathway for epilepsy: Automating seizure tracking based on electroencephalogram data using artificial intelligence.

Objective: Scalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance. Method: The study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway. Results: The study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance. Conclusion: The proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.

A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia.

Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.

Deep Learning-Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors.

BACKGROUND: Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative information from high-dimensional data collected from multiple sources. The available fusion algorithms with their complex structure are poorly adopted to the computationally constrained environment which requires integrating information directly at the source. As a result, more simple low-level fusion methods are needed. OBJECTIVE: In the absence of a data combining process, the cost of directly applying high-dimensional raw data to a deep classifier would be computationally expensive with regard to the response time, energy consumption, and memory requirement. Taking this into account, we aimed to develop a data fusion technique in a computationally efficient way to achieve a more comprehensive insight of human activity dynamics in a lower dimension. The major objective was considering statistical dependency of multisensory data and exploring intermodality correlation patterns for different activities. METHODS: In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and the covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep model to learn specific patterns associated with specific activity. RESULTS: In order to show the generalizability of the proposed fusion algorithm, 2 different scenarios were taken into account. These scenarios were different in terms of temporal segment size, type of activity, wearable device, subjects, and deep learning architecture. The first scenario used a data set in which a single participant performed a limited number of activities while wearing the Empatica E4 wristband. In the second scenario, a data set related to the activities of daily living was used where 10 different participants wore inertial measurement units while performing a more complex set of activities. The precision metric obtained from leave-one-subject-out cross-validation for the second scenario reached 0.803. The impact of missing data on performance degradation was also evaluated. CONCLUSIONS: To conclude, the proposed fusion technique provides the possibility of embedding joint variability information over different modalities in just a single 2D representation which results in obtaining a more global view of different aspects of daily human activities at hand, and yet preserving the desired performance level in activity recognition.

Reconstruction of missing channel in electroencephalogram using spatiotemporal correlation-based averaging.

Objective.Electroencephalogram (EEG) recordings often contain large segments with missing signals due to poor electrode contact or other artifact contamination. Recovering missing values, contaminated segments and lost channels could be highly beneficial, especially for automatic classification algorithms, such as machine/deep learning models, whose performance relies heavily on high-quality data. The current study proposes a new method for recovering missing segments in EEG.Approach.In the proposed method, the reconstructed segment is estimated by substitution of the missing part of the signal with the normalized weighted sum of other channels. The weighting process is based on inter-channel correlation of the non-missing preceding and proceeding temporal windows. The algorithm was designed to be computationally efficient. Experimental data from patients (N= 20) undergoing general anesthesia due to elective surgery were used for the validation of the algorithm. The data were recorded using a portable EEG device with ten channels and a self-adhesive frontal electrode during induction of anesthesia with propofol from waking state until burst suppression level, containing lots of variation in both amplitude and frequency properties. The proposed imputation technique was compared with another simple-structure technique. Distance correlation (DC) was used as a measure of comparison evaluation.Main results.: The proposed method, with an average DC of 82.48 ± 10.01 (µ± σ)%, outperformed its competitor with an average DC of 67.89 ± 14.12 (µ± σ)%. This algorithm also showed a better performance when increasing the number of missing channels.Significance.the proposed technique provides an easy-to-implement and computationally efficient approach for the reliable reconstruction of missing or contaminated EEG segments.

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.

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG.

OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance. APPROACH: A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact. MAIN RESULTS: The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset. SIGNIFICANCE: This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing.

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.

Effects of remifentanil on the spectrum and quantitative parameters of electroencephalogram in propofol anesthesia.

BACKGROUND: A high dose of opioids associated with a low dose of propofol has become a popular anesthetic technique. However, the influence of opioids on the electroencephalographic phenomenon related to induction of anesthesia and, thereby, on the quantitative parameters used in the depth-of-anesthesia estimation is not well known. METHODS: Twenty-seven patients were divided into three groups to receive saline, low-dose remifentanil (7.5 microg x kg x h) or high-dose remifentanil (30 microg x kg x h) during induction of anesthesia with propofol (30 mg . kg . h). Electroencephalogram was recorded from Fz electrode, and its time-frequency properties in the patient groups were analyzed from the induction of anesthesia to the occurrence of burst suppression pattern. The group differences in 14 quantitative spectral parameters used in the depth-of-anesthesia estimation were examined as well. RESULTS: The time-frequency properties of electroencephalogram were different between groups. The high-frequency (greater than 14 Hz) activity during light anesthesia was decreased in remifentanil groups; whereas, increased activity in extended alpha band (7-14 Hz) and decreased activity in delta band (0.5-4 Hz) was observed during deep anesthesia. This resulted in statistically significant changes in all 14 quantitative parameters. CONCLUSIONS: The effect of remifentanil on the spectrum and quantitative parameters of electroencephalogram is significant and strongly dependent on the level of anesthesia. Coadministration of opioids therefore challenges the reliability of the spectral properties of electroencephalogram in the depth-of-anesthesia estimation by using a frontal montage. Furthermore, the finding has implications for design of opioid coadministration studies.

Remifentanil modifies the relation of electroencephalographic spectral changes and clinical endpoints in propofol anesthesia.

BACKGROUND: Depth-of-anesthesia monitoring with the electroencephalogram has become widely used in anesthesia practice. Generally, the methods presented are based on the spectral changes of the electroencephalogram. In this study, the authors evaluate the influence of remifentanil on the relation of timely occurrence of clinical endpoints and the spectral behavior of the electroencephalogram. METHODS: Twenty-seven patients scheduled to undergo a surgical procedure were randomly assigned to three groups. Patients blindly received equal volumes of saline or remifentanil (7.5 or 30 microg.kg.h) 1 min before induction of anesthesia with infusion of propofol (30 mg.kg.h). The occurrence of loss of counting, loss of obeying verbal command, and loss of reaction to tetanic stimulation was assessed. The electroencephalogram was recorded from electrode Fz referenced to the common average, and an iterative algorithm was applied to solve the underlying frequency progression pattern. The positions of the clinical endpoints on the pattern were analyzed. RESULTS: The administration of remifentanil during induction of anesthesia with propofol led to an earlier occurrence of the clinical endpoints on the frequency progression pattern. A significant difference (P < 0.05) was observed between the saline and high-dose patient groups in all three endpoints. The effect of remifentanil was proportional to the infusion rate. CONCLUSIONS: The infusion of remifentanil during propofol anesthesia significantly modifies the mutual relations of the electroencephalographic spectral characteristics and the endpoints in a predictable and quantifiable manner. This finding suggests that the electroencephalographic phenomena and the endpoints may not be identical but rather to some extent separate manifestations of hypnotic drug effect.

Time-frequency properties of electroencephalogram during induction of anesthesia.

A method for detailed description of the time-frequency characteristics of electroencephalogram during induction of anesthesia is proposed. The method, based on averaging of time-normalized smoothed pseudo-Wigner-Ville distributions, is applied to data recorded from nine patients undergoing propofol anesthesia. An extensive representation of the frequency progression pattern related to the induction of anesthesia is given and the time-frequency characteristics that are consistent/not consistent between patients are determined. It is also illustrated how four different clinical end-points, generally used in the assessment of the depth of anesthesia, can be related to different phases of the frequency progression pattern. The method presented has importance in providing information about the neurophysiological phenomenon during induction of anesthesia and can therefore be used in the development of new monitoring algorithms.

Novel Modeling of Somatosensory Evoked Potentials for the Assessment of Spinal Cord Injury.

OBJECTIVE: Previous work has shown that differences in the somatosensory evoked potential (SEP) signals between a normal spinal pathway and spinal pathway affected by spinal cord injury (SCI) provide a means to study the degree of injury. This paper proposes a novel quantitative SCI assessment method using time-domain SEP signals. METHODS: A pruned and unstructured fit between SEP signals from a normal spinal pathway and a spinal pathway affected by SCI is developed using methods inspired by recent results in sparse reconstruction theory. The coefficients from the resulting fit are used to develop a quantitative assessment of SCI that is tested on actual SEP signals collected from rodents that have been subjected to partial and complete spinal cord transection. RESULTS: The proposed method provides a rich parametric measure that integrates SEP amplitude, time latency, and morphology, while exhibiting a high degree of correlation with existing subjective and quantitative SCI assessment methods. CONCLUSION: The proposed SCI encapsulates a model of the injury to quantify SCI. SIGNIFICANCE: The proposed SCI quantification method may be used to complement existing SCI assessment methods.

Real-time monitoring of human blood-brain barrier disruption.

Chemotherapy aided by opening of the blood-brain barrier with intra-arterial infusion of hyperosmolar mannitol improves the outcome in primary central nervous system lymphoma. Proper opening of the blood-brain barrier is crucial for the treatment, yet there are no means available for its real-time monitoring. The intact blood-brain barrier maintains a mV-level electrical potential difference between blood and brain tissue, giving rise to a measurable electrical signal at the scalp. Therefore, we used direct-current electroencephalography (DC-EEG) to characterize the spatiotemporal behavior of scalp-recorded slow electrical signals during blood-brain barrier opening. Nine anesthetized patients receiving chemotherapy were monitored continuously during 47 blood-brain barrier openings induced by carotid or vertebral artery mannitol infusion. Left or right carotid artery mannitol infusion generated a strongly lateralized DC-EEG response that began with a 2 min negative shift of up to 2000 µV followed by a positive shift lasting up to 20 min above the infused carotid artery territory, whereas contralateral responses were of opposite polarity. Vertebral artery mannitol infusion gave rise to a minimally lateralized and more uniformly distributed slow negative response with a posterior-frontal gradient. Simultaneously performed near-infrared spectroscopy detected a multiphasic response beginning with mannitol-bolus induced dilution of blood and ending in a prolonged increase in the oxy/deoxyhemoglobin ratio. The pronounced DC-EEG shifts are readily accounted for by opening and sealing of the blood-brain barrier. These data show that DC-EEG is a promising real-time monitoring tool for blood-brain barrier disruption augmented drug delivery.

The effect of anaesthesia on somatosensory evoked potential measurement in a rat model.

Somatosensory evoked potentials (SEPs) are widely used to study the functional integrity of ascending sensory pathways. For animal studies, SEPs provide a convenient method to quantitatively assess the functionality of the nervous system with low invasiveness. Even though they are frequently used in animal models, little attention is paid to the fact that SEPs are vulnerable to contamination from experimental factors such as anaesthetic delivery. In this study, the effect of isoflurane on SEP measurement was investigated in a rat model. The aim was to find out the adjustments for anaesthetic delivery optimizing the quality of the recordings. Two aspects were studied: the effect of isoflurane dosage on the SEP parameters and on the repeatability of the measurements. The SEP quality was found to be best when 1.5% isoflurane concentration was used. This dosage resulted in the best signal-to-noise ratio and equal repeatability of the measurements compared with the others. Our findings can help in refining the anaesthetic protocols related to SEP recordings in a rat model and, by improving the quality of the measurements, potentially reducing the number of subjects needed to carry out studies.

Natural Progression of Spinal Cord Transection Injury and Reorganization of Neural Pathways.

The spinal cord injury (SCI) transection model accurately represents traumatic laceration and has been widely used to study the natural history and reorganization of neuropathways and plasticity in the central nervous system (CNS). This model is highly reproducible, which makes it ideal for studying the progression of injury as well as endogenous recovery and plasticity in the CNS. Five experimental groups of transection injury were designed: left hemitransection; right hemitransection; double hemitransection; complete transection injuries; and laminectomy-only control. We used somatosensory evoked potentials (SSEPs) as an objective electrophysiological assessment tool and motor behavior testing (Basso, Beattie, and Bresnahan [BBB] scoring) to functionally assess the neural pathways post-injury. Histological examinations were carried out to investigate the extent of injury and spinal cord morphological changes. Significant (p < 0.05) electrophysiological changes were observed and were verified by an increase in SSEP amplitude in somatosensory cortices for all four injury groups during days 4 and 7 post-injury. Degree of plasticity among the groups was distinguished by changes in SSEP amplitude and BBB scores. Our results support our previous published findings (using a contusive model of SCI), which shows that the reorganization of neuropathways and plasticity persist in time and are not transient phenomena. SSEPs are a reliable tool to assess the functionality of neural pathways and their projections to higher CNS structures such as the cortices. They enable us to determine residual function and the changes within the CNS post-injury and consistently track these events over time. The results from our study provide supporting evidence for the presence of neuronal network reorganization and plasticity in the CNS after transection SCI.

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.

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.