Dexmedetomidine Induced Deep Sedation Mimics Non-Rapid Eye Movement Stage 3 Sleep:Large Scale Validation using Machine Learning

STUDY OBJECTIVES: Dexmedetomidine induced electroencephalogram (EEG) patterns during deep sedation is comparable with natural sleep patterns. Using large scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine induced deep sedation indeed mimics natural sleep patterns. METHODS: We used EEG recordings from three sources in this study: 8707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine induced sedation levels were assessed using the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) score. We extracted twenty-two spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state. RESULTS: The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine induced deep sedation (MOAA/S = 0) with AUC > 0.8 outperforming other machine learning models. Power in the delta band (0-4Hz) was selected as an important feature for prediction in addition to power in theta (4-8 Hz) and beta (16-30Hz) bands. CONCLUSIONS: Using a large scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine induced deep sedation state mimics N3 sleep EEG patterns

Epileptic seizure detection and prediction based on EEG signal

Epilepsy is a kind of chronic brain disfunction, manifesting as recurrent seizures which is caused by sudden and excessive discharge of neurons. Electroencephalogram (EEG) recordings is regarded as the golden standard for clinical diagnosis of epilepsy disease. The diagnosis of epilepsy disease by professional doctors clinically is time-consuming. With the help artificial intelligence algorithms, the task of automatic epileptic seizure detection and prediction is called a research hotspot. The thesis mainly contributes to propose a solution to overfitting problem of EEG signal in deep learning and a method of multiple channels fusion for EEG features. The result of proposed method achieves outstanding performance in seizure detection task and seizure prediction task. In seizure detection task, this paper mainly explores the effect of the deep learning in small data size. This thesis designs a hybrid model of CNN and SVM for epilepsy detection compared with end-to-end classification by deep learning. Another technique for overfitting is new EEG signal generation based on decomposition and recombination of EEG in time-frequency domain. It achieved a classification accuracy of 98.8%, a specificity of 98.9% and a sensitivity of 98.4% on the classic Bonn EEG data. In seizure prediction task, this paper proposes a feature fusion method for multi-channel EEG signals. We extract a three-order tensor feature in temporal, spectral and spatial domain. UMLDA is a tensor-to-vector projection method, which ensures minimal redundancy between feature dimensions. An excellent experimental result was finally obtained, including an average accuracy of 95%, 94% F1-measure and 90% Kappa index

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ~85% vs. ~79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.Comment: Published at IEEE SPMB 2017 https://www.ieeespmb.org/2017

Uncovering the structure of clinical EEG signals with self-supervised learning

Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. Approach. We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. Main results. Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. Significance. We demonstrate the benefit of SSL approaches on EEG data. Our results suggest that self-supervision may pave the way to a wider use of deep learning models on EEG data.Peer reviewe

Advances in Quantitative Characterizations of Electrophysiological Neural Activity

Disorders of the brain and nervous system result in more hospitalizations and lost productivity than any other disease group. Electroencephalography (EEG), which measures brain electrical signals from the scalp, is a common neuro-monitoring technique used for diagnostic, rehabilitative, and therapeutic purposes. Understanding EEG quantitatively and its neural correlates with patient characteristics could inform the safety and efficacy of technologies that rely on EEG. In this dissertation, a large clinical data set comprised of over 35,000 recordings as well as data from previous research experiments are utilized to better quantify characteristics of neurological activity. We first propose non-parametric methods of evaluating consistency of quantitative EEG features (qEEG) by applying novel statistical approaches. These results provide data-driven methods of identifying qEEG and their spatial characteristics ideal for various applications, and determining consistencies of novel features using existing data. These qEEG are commonly used in feature-based machine learning applications. Further, EEG-driven deep learning has shown promising results in distinguishing recordings of subjects. To better understand the performance of these two machine learning approaches, we assess their ability to distinguish between subjects taking different anticonvulsants. Our methods could successfully discriminate between patients taking either anticonvulsant and those taking no medications solely from neural activity with similar performance from both feature-based and deep learning approaches. With feature-based methods, it is easier to interpret which qEEG have the most impact on algorithm performance. However, deep learning applications in EEG can present difficulty in understanding and investigating underlying neurophysiological implications. We propose and validate a method to investigate frequency band importance in EEG-driven deep learning models. The easy perturbation EEG algorithm for spectral importance (easyPEASI) is simpler than previous methods and is applied to classifications investigated in this work. Until this point, our work used well segmented EEG from clinical settings. However, EEG is usually corrupted by noise which can degrade its utility. We formulate and validate novel approaches to score electrophysiological signal quality based on the presence of noise from various sources. Further, we apply our method to compare and evaluate the performance of existing artifact removal algorithms

RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in sleep medicine. However, manual detection limits their study because it is time-consuming and affected by significant inter-expert variability, motivating automatic approaches. We propose a deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED). RED uses one of two input representations: a) the time-domain EEG signal, or b) a complex spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT). Unlike previous approaches, a fixed time window is avoided and temporal context is integrated to better emulate the visual criteria of experts. When evaluated on the MASS dataset, our detectors outperform the state of the art in both sleep spindle and K-complex detection with a mean F1-score of at least 80.9% and 82.6%, respectively. Although the CWT-domain model obtained a similar performance than its time-domain counterpart, the former allows in principle a more interpretable input representation due to the use of a spectrogram. The proposed approach is event-agnostic and can be used directly to detect other types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN 2020