Automatic EEG processing for the early diagnosis of traumatic brain injury

Traumatic Brain Injury (TBI) is recognized as an important cause of death and disabilities after an accident. The availability a tool for the early diagnosis of brain dysfunctions could greatly improve the quality of life of people affected by TBI and even prevent deaths. The contribution of the paper is a process including several methods for the automatic processing of electroencephalography (EEG) data, in order to provide a fast and reliable diagnosis of TBI. Integrated in a portable decision support system called EmerEEG, the TBI diagnosis is obtained using discriminant analysis based on quantitative EEG (qEEG) features extracted from data recordings after the automatic removal of artifacts. The proposed algorithm computes the TBI diagnosis on the basis of a model extracted from clinically-labelled EEG records. The system evaluations have confirmed the speed and reliability of the processing algorithms as well as the system's ability to deliver accurate diagnosis. The developed algorithms have achieved 79.1% accuracy in removing artifacts, and 87.85% accuracy in TBI diagnosis. Therefore, the developed system enables a short response time in emergency situations and provides a tool the emergency services could base their decision upon, thus preventing possibly miss-diagnosed injuries

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios

Portable decision support for diagnosis of traumatic brain injury

Early detection and diagnosis of Traumatic Brain Injury (TBI) could reduce significantly the death rate and improve the quality of life of the people affected if emergency services are equipped with tools for TBI diagnosis at the place of the accident. This problem is addressed here by proposing a portable decision support system called EmerEEG, which is based on Quantitative Electroencephalography (qEEG). The contributions of the paper are the proposed system concept, architecture and decision support for TBI diagnosis. By the virtue of its easily operable mobile system, the proposed solution for emergency TBI diagnosis provides valuable decision support at a very early stage after an accident, thereby enabling a short response time in critical situations and better prospects for the people affected

Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing to high morbidity and mortality rates and placing a substantial economic burden on healthcare systems worldwide. The diagnosis of TBI relies on clinical information along with Computed Tomography (CT) scans. Addressing the multifaceted challenges posed by TBI has seen the development of innovative, data-driven approaches, for this complex condition. Particularly noteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority of TBI cases where conventional methods often fall short. As such, we review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI. We categorize ML applications based on their data sources, and there is a spectrum of ML techniques used to date. Most of these techniques have primarily focused on diagnosis, with relatively few attempts at predicting the prognosis. This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data-driven approaches and standard diagnostic data.Comment: The manuscript has 34 pages, 3 figures, and 4 table

A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroen-cephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions. This work can enable development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.Comment: 12 pages, 6 figure

Residual Deficits Observed In Athletes Following Concussion: Combined Eeg And Cognitive Study

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Emerging evidence suggests that the residual deficits can persist one year or more following a brain injury. Detecting and quantifying the residual deficits are vital in making a decision about the treatment plan and may prevent further damage. For example, improper return to play (RTP) decisions in sports such as football have proven to be associated with the further chance of recurring injury, long-term neurophysiological impairments, and worsening of brain functional activity. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits in two different datasets. One dataset contains a combination of EEG analysis with three standard post-concussive assessment tools. The data for this dataset were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. Another dataset contains post-concussion eyes closed EEG signal for twenty concussed and twenty age-matched controls. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency and nonlinear features for the first time to explore post-concussive deficits. In conjunction with traditional frequency band analysis, we also presented a new individual frequency based approach for EEG assessment. A set of linear, time-frequency and nonlinear EEG markers were found to be significantly different in the concussed group compared to their matched peers in the healthy group. Although EEG analysis exhibited discrepancies, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlight that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management. Moreover, a number of studies have clearly demonstrated the feasibility of supervised and unsupervised pattern recognition algorithms to classify patients with various health-related issues. Inspired by these studies, we hypothesized that a set of robust features would accurately differentiate concussed athletes from control athletes. To verify it, features such as power spectral, statistical, wavelet, and other nonlinear features were extracted from the EEG signal and were used as an input to various classification algorithms to classify the concussed individuals. Various techniques were applied to classify control and concussed athletes and the performance of the classifiers was compared to ensure the best accuracy. Finally, an automated approach based on meaningful feature detection and efficient classification algorithm were presented to systematically identify concussed athletes from healthy controls with a reasonable accuracy. Thus, the study provides sufficient evidence that the proposed analysis is useful in evaluating the post-concussion deficits and may be incorporated into clinical assessments for a standard evaluation of athletes after a concussion

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

Event-related Potential Markers of Perceptual and Conceptual Speech Processes in Patients with Disorders of Consciousness.

Vegetative state (VS) and minimally conscious state (MCS) patients behaviorally demonstrate absent or fluctuating levels of awareness. Functional magnetic resonance imaging evidence of covert perceptual and semantic speech processing provides prognostic value for these patients. In this thesis, I examined the utility of high-density electroencephalography (EEG) in this regard. A contrast between event-related potentials (ERPs) elicited by primed and unprimed word pairs was used to isolate conceptual (semantic) processes, while ERPs elicited by signal-correlated noise were contrasted with those elicited by speech to isolate pre-semantic, perceptual aspects of speech processing. These ERP effects were found to be both temporally and spatially dissociable, indicating the contributions of not entirely overlapping regions of cortex. Four out of ten VS/MCS patients demonstrated significant perceptual effects, while no conceptual effects were observed for any patient. It is therefore possible to identify low-level stages of language processing that can now be tested for prognostic value given future follow-up studies