Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize ...this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.
In steady-state visual evoked potential (SSVEP)based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the ...interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take time, cause visual fatigue and reduce usability. For the calibration-free scenario, we propose a cross-subject frequency identification method based on transfer superimposed theory for SSVEP frequency decoding. First, a multi-channel signal decomposition model was constructed. Next, we used the cross least squares iterative method to create individual specific transfer spatial filters as well as source subject transfer superposition templates in the source subject. Then, we identified common knowledge among source subjects using a prototype spatial filter to make common transfer spatial filters and common impulse responses. Following, we reconstructed a global transfer superimposition template with SSVEP frequency characteristics. Finally, an ensemble cross-subject transfer learning method was proposed for SSVEP frequency recognition by combining the sourcesubject transfer mode, the global transfer mode, and the sinecosine reference template. Offline tests on two public datasets show that the proposed method significantly outperforms the FBCCA, TTCCA, and CSSFT methods. More importantly, the proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm was robust, which is important for a practical BCI.
The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external ...devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated “decoding” strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response.
•CCA is a powerful, easy to use method for multichannel data analysis.•CCA finds an optimal linear model to relate stimulus and brain response.•Multiple speech components map to distinct spectro-spatial signatures.•CCA yields large stimulus-response correlation values.•CCA supports good performance in a classification task.
This study aimed to evaluate clinical efficacy and safety of purified pharmaceutical cannabidiol (CBD) as an adjunctive therapy in refractory childhood-onset epileptic spasms (ES).
Nine patients with ...ES were enrolled in an Institutional Review Board (IRB)- and Food and Drug Administration (FDA)-approved expanded access investigational new drug trial. Patients received plant-derived highly purified CBD in oral solution in addition to their baseline medications at an initial dosage of 5 mg/kg/day, which was increased by 5 mg/kg/day every week to an initial target dosage of 25 mg/kg/day. Seizure frequency, adverse event, and parents' subjective reports of cognitive and behavioral changes were recorded after 2 weeks and 1, 2, 3, 6, 9, and 12 months of CBD treatment. Responder rates (percent of patients with >50% reduction in ES frequency from baseline) were calculated. Electrographic changes were studied in relation to CBD initiation and clinical response.
Overall, the responder rates in 9 patients were 67%, 78%, 67%, 56%, 78%, 78%, and 78% after 2 weeks and 1, 2, 3, 6, 9, and 12 months of CBD treatment, respectively. Three out of nine patients (33%) were ES free after two months of treatment. Parents reported subjective improvements in cognitive and behavioral domains. Side effects, primarily drowsiness, were seen in 89% of patients (n = 8). Eight of the nine (89%) patients had electroencephalographic (EEG) studies prior to and after initiation of CBD. Three out of five patients (60%) had resolution in their hypsarrhythmia pattern.
Purified pharmaceutical CBD may be an effective and safe adjunctive therapy in refractory ES and may also be associated with improvements in electrographic findings.
•Purified pharmaceutical cannabidiol seems an effective adjunctive therapy in refractory epileptic spasms.•Purified pharmaceutical cannabidiol has corresponding electrographic changes.•Purified pharmaceutical cannabidiol seems to exhibit acceptable safety profile.
Postoperative delirium (POD) is the most common serious postoperative complication in older adults. It has uncertain aetiology, limited preventative strategies, and poor long-term outcomes. This ...updated systematic review and meta-analysis aimed to estimate the effect of processed electroencephalography (pEEG)-guided general anaesthesia during surgery on POD incidence.
We performed a systematic review and meta-analysis by searching OVID MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) electronic databases. Studies of adult patients having general anaesthesia for any surgery where pEEG was used and POD was an outcome measure were included. Full-text reports of RCTs published from database inception until August 28, 2021, were included. Trials were excluded if sedation rather than general anaesthesia was administered, or the setting was intensive care. The primary outcome was POD assessed by validated tools. The study was prospectively registered with PROSPERO.
Nine studies, which included 4648 eligible subjects, were identified. The incidence of POD in the pEEG-guided general anaesthesia or lighter pEEG target group was 19.0% (440/2310) compared with 23.3% (545/2338) in the usual care or deeper pEEG target group (pooled odds ratio=0.78; 95% confidence interval, 0.60–1.00; P=0.054). Significant heterogeneity was detected (I2=53%).
Our primary analysis demonstrated a highly sensitive result with a pooled analysis of trials in which the intervention group adhered to manufacturer's recommended guidelines, showing reduced incidence of POD with pEEG guidance. High clinical heterogeneity limits inferences from this and any future meta-analyses.
CRD42020199404 (PROSPERO).
Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for ...clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen's kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effect on their sleep, and subjects were generally able to apply the equipment without supervision. This finding marks a turning point on the road to clinical long term sleep monitoring: the question should no longer be whether ear-EEG could ever be used for clinical home sleep monitoring, but rather when it will be.
Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG ...recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.
BACKGROUND:Preexisting factors such as age and cognitive performance can influence the electroencephalogram (EEG) during general anesthesia. Specifically, spectral EEG power is lower in elderly, ...compared to younger, subjects. Here, the authors investigate age-related changes in EEG architecture in patients undergoing general anesthesia through a detailed examination of spectral and entropic measures.
METHODS:The authors retrospectively studied 180 frontal EEG recordings from patients undergoing general anesthesia, induced with propofol/fentanyl and maintained by sevoflurane at the Waikato Hospital in Hamilton, New Zealand. The authors calculated power spectral density and normalized power spectral density, the entropic measures approximate and permutation entropy, as well as the beta ratio and spectral entropy as exemplary parameters used in current monitoring systems from segments of EEG obtained before the onset of surgery (i.e., with no noxious stimulation).
RESULTS:The oldest quartile of patients had significantly lower 1/f characteristics (P < 0.001; area under the receiver operating characteristics curve, 0.84 0.76 0.92), indicative of a more uniform distribution of spectral power. Analysis of the normalized power spectral density revealed no significant impact of age on relative alpha (P = 0.693; area under the receiver operating characteristics curve, 0.52 0.41 0.63) and a significant but weak effect on relative beta power (P = 0.041; area under the receiver operating characteristics curve, 0.62 0.52 0.73). Using entropic parameters, the authors found a significant age-related change toward a more irregular and unpredictable EEG (permutation entropyP < 0.001, area under the receiver operating characteristics curve, 0.81 0.71 0.90; approximate entropyP < 0.001; area under the receiver operating characteristics curve, 0.76 0.66 0.85). With approximate entropy, the authors could also detect an age-induced change in alpha-band activity (P = 0.002; area under the receiver operating characteristics curve, 0.69 0.60 78).
CONCLUSIONS:Like the sleep literature, spectral and entropic EEG features under general anesthesia change with age revealing a shift toward a faster, more irregular, oscillatory composition of the EEG in older patients. Age-related changes in neurophysiological activity may underlie these findings however the contribution of age-related changes in filtering properties or the signal to noise ratio must also be considered. Regardless, most current EEG technology used to guide anesthetic management focus on spectral features, and improvements to these devices might involve integration of entropic features of the raw EEG.
Highlights • Automated processing of resting state quantitative EEG is as reliable as visually controlled processing. • Reduced inter-rater reliability is avoided by automated EEG processing. • ...Automated processing of EEG is less time consuming and reduces the need for trained raters.
•A robust method is proposed for efficient detection of seizures in EEG.•Dual tree-complex wavelet transform is used for feature extraction.•General regression neural network is employed to classify ...extracted features.•The proposed technique is giving ceiling level performance.•The model can be used for fast and accurate diagnosis of epilepsy.
Identifying seizure patterns in complex electroencephalography (EEG) through visual inspection is often challenging, time-consuming and prone to errors. These problems have motivated the development of various automated seizure detection systems that can aid neurophysiologists in accurate diagnosis of epilepsy. The present study is focused on the development of a robust automated system for classification against low levels of supervised training. EEG data from two different repositories are considered for analysis and validation of the proposed system. The signals are decomposed into time-frequency sub-bands till sixth level using dual-tree complex wavelet transform (DTCWT). All details and last approximation coefficients are used to calculate features viz. energy, standard deviation, root-mean-square, Shannon entropy, mean values and maximum peaks. These feature sets are passed through a general regression neural network (GRNN) for classification with K-fold cross-validation scheme under varying train-to-test ratios. The current model yields ceiling level classification performance (accuracy, sensitivity & specificity) in all combinations of datasets (ictal vs non-ictal) in less than 0.028s. The proposed scheme will not only maximize hit-rate and correct rejection rate but also will aid neurophysiologists in the fast and accurate diagnosis of seizure onset.
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