ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no particular order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) an IC dataset containing spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings, (2) a website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier. The classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The ICLabel classifier outperforms or performs comparably to the previous best publicly available method for all measured IC categories while computing those labels ten times faster than that classifier as shown in a rigorous comparison against all other publicly available EEG IC classifiers.Comment: Intended for NeuroImage. Updated from version one with minor editorial and figure change

Wound Image Classification Using Deep Convolutional Neural Networks

Artificial Intelligence (AI) includes subfields like Machine Learning (ML) and DeepLearning (DL) and discusses intelligent systems that mimic human behaviors. ML has been used in a wide range of fields. Particularly in the healthcare domain, medical images often need to be carefully processed via such operations as classification and segmentation. Unlike traditional ML methods, DL algorithms are based on deep neural networks that are trained on a large amount of labeled data to extract features without human intervention. DL algorithms have become popular and powerful in classifying and segmenting medical images in recent years. In this thesis, we shall study the image classification problem in smartphone wound images using deep learning. Specifically, we apply deep convolutional neural networks (DCNN) on wound images to classify them into multiple types including diabetic, pressure, venous, and surgical. Also, we use DCNNs for wound tissue classification. First, an extensive review of existing DL-based methods in wound image classification is conducted and comprehensive taxonomies are provided for the reviewed studies. Then, we use a DCNN for binary and 3-class classification of burn wound images. The accuracy was considerably improved for the binary case in comparison with previous work in the literature. In addition, we propose an ensemble DCNN-based classifier for image-wise wound classification. We train and test our model on a new valuable set of wound images from different types that are kindly shared by the AZH Wound and Vascular Center in Milwaukee. The dataset has been shared for researchers in the field. Our proposed classifier outperforms the common DCNNs in classification accuracy on our own dataset. Also, it was evaluated on a public wound image dataset. The results showed that the proposed method can be used for wound image classification tasks or other similar applications. Finally, experiments are conducted on a dataset including different tissue types such as slough, granulation, callous, etc., annotated by the wound specialists from AZH Center to classify the wound pixels into different classes. The preliminary results of tissue classification experiments using DCNNs along with the future directions have been provided

Wildfire Risk Assessment Using Convolutional Neural Networks And MODIS Climate Data

Wildfires burn millions of acres of land each year leading to the destruction of homes and wildland ecosystems while costing governments billions in funding. As climate change intensifies drought volatility across the Western United States, wildfires are likely to become increasingly severe. Wildfire risk assessment and hazard maps are currently employed by fire services, but can often be outdated. This paper introduces an image-based dataset using climate and wildfire data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The dataset consists of 32 climate and topographical layers captured across 0.1 deg by 0.1 deg tiled regions in California and Nevada between 2015 and 2020, associated with whether the region later saw a wildfire incident. We trained a convolutional neural network (CNN) with the generated dataset to predict whether a region will see a wildfire incident given the climate data of that region. Convolutional neural networks are able to find spatial patterns in their multi-dimensional inputs, providing an additional layer of inference when compared to logistic regression (LR) or artificial neural network (ANN) models. To further understand feature importance, we performed an ablation study, concluding that vegetation products, fire history, water content, and evapotranspiration products resulted in increases in model performance, while land information products did not. While the novel convolutional neural network model did not show a large improvement over previous models, it retained the highest holistic measures such as area under the curve and average precision, indicating it is still a strong competitor to existing models. This introduction of the convolutional neural network approach expands the wealth of knowledge for the prediction of wildfire incidents and proves the usefulness of the novel, image-based dataset

The real-time molecular characterisation of human brain tumours during surgery using Rapid Evaporative Ionization Mass Spectrometry [REIMS] and Raman spectroscopy: a platform for precision medicine in neurosurgery

Aim: To investigate new methods for the chemical detection of tumour tissue during neurosurgery. Rationale: Surgeons operating on brain tumours currently lack the ability to directly and immediately assess the presence of tumour tissue to help guide resection. Through developing a first in human application of new technology we hope to demonstrate the proof of concept that chemical detection of tumour tissue is possible. It will be further demonstrated that information can be obtained to potentially aid treatment decisions. This new technology could, therefore, become a platform for more effective surgery and introducing precision medicine to Neurosurgery. Methods: Molecular analysis was performed using Raman spectroscopy and Rapid Evaporative Ionization Mass Spectrometry (REIMS). These systems were first developed for use in brain surgery. A single centre prospective observational study of both modalities was designed involving a total of 75 patients undergoing craniotomy and resection of a range of brain tumours. A neuronavigation system was used to register spectral readings in 3D space. Precise intraoperative readings from different tumour zones were taken and compared to matched core biopsy samples verified by routine histopathology. Results: Multivariate statistics including PCA/LDA analysis was used to analyse the spectra obtained and compare these to the histological data. The systems identified normal versus tumour tissue, tumour grade, tumour type, tumour density and tissue status of key markers of gliomagenesis. Conclusions: The work in this thesis provides proof of concept that useful real time intraoperative spectroscopy is possible. It can integrate well with the current operating room setup to provide key information which could potentially enhance surgical safety and effectiveness in increasing extent of resection. The ability to group tissue samples with respect to genomic data opens up the possibility of using this information during surgery to speed up treatment, escalate/deescalate surgery in specific phenotypic groups to introduce precision medicine to Neurosurgery.Open Acces

Razvoj metod strojnega učenja za identifikacijo kozmičnih delcev ekstremnih energij ter njihova implementacija pri iskanju fotonov ekstremnih energij s površinskimi detektorji Observatorija Pierre Auger

Despite their discovery already more than a century ago, Cosmic Rays (CRs) still did not divulge all their properties yet. Theories about the origin of ultra-high energy (UHE, > 10^18 eV) CRs predict accompanying primary photons. The existence of UHE photons can be investigated with the world’s largest ground-based experiment for detection of CR-induced extensive air showers (EAS), the Pierre Auger Observatory, which offers an unprecedented exposure to rare UHE cosmic particles. The discovery of photons in the UHE regime would open a new observational window to the Universe, improve our understanding of the origin of CRs, and potentially uncloak new physics beyond the standard model. The novelty of the presented work is the development of a "real-time" photon candidate event stream to a global network of observatories, the Astrophysical Multimessenger Observatory Network (AMON). The stream classifies CR events observed by the Auger surface detector (SD) array as regards their probability to be photon nominees, by feeding to advanced machine learning (ML) methods observational air shower parameters of individual CR events combined in a multivariate analysis (MVA). The described straightforward classification procedure further increases the Pierre Auger Observatory’s endeavour to contribute to the global effort of multi-messenger (MM) studies of the highest energy astrophysical phenomena, by supplying AMON partner observatories the possibility to follow-up detected UHE events, live or in their archival data

Characterizing the Spatial Patterns and Spatially Explicit Probabilities of Post-Fire Vegetation residual patches in Boreal Wildfire Scars

Wildfire is one of the main natural disturbances that consume a substantial amount of forest cover, influencing and reshaping the landscape mosaic of boreal forests. Wildfires do not burn the entire landscape; they rather create a complex mosaic of post-fire landscape structure with different degrees of burn severity. The resulting spatial mosaic includes fully burned, partially burned, and unburned areas. Even though the most visible components of a fire disturbed landscape are the completely burned areas, a considerable number of residual patches of various size, shape, and composition are retained following a fire. The residual patches refer to remnants of the pre-fire forest ecosystem that left completely unaltered within the fire footprint. Improved understanding of the patterns and characteristics of wildfire residuals provides insights for investigating the effects of fire disturbances, emulating forest disturbances in harvesting operations, and improving forest management planning. Knowledge about the post-fire residuals relies on how well we measure the patterns and characteristics of post-fire residuals, determine the factors that explain their occurrence and patterns, and what consistent measurement framework we use to understand the patterns and predict their likely occurrence. In this study, the patterns and characteristics of post-fire residuals was initially examined based on eleven boreal wildfire events within northwestern Ontario; each ignited by lightning and never suppressed. The wildfire events were occurred in ecoregion 2W during the fire seasons of 2002 and 2003. In order to design a consistent and repeatable method for measuring the patterns of residuals, an integrate approach has been designed. This involves assessing the spatial patterns where the composition, configuration, and fragmentation of residual patches were assessed based on selected spatial metrics; examining the importance of predictor variables that explain residuals and their marginal effects on residual patch occurrence using Random Forest (RF) ensemble method; and developing a spatially explicit predictive model using the RF method where the combined effects of the variables were examined. Finally, the three approaches are applied and evaluated using a recent and independent data from the extensive RED084 wildfire event that occurred in 2011 within the adjacent ecoregion (3S). The effects of analytical scale (i.e., spatial resolution) on characterizing the spatial patterns, determining the relative variable importance, and predicted probabilities of residual patches are assessed. The results show that the composition and configuration of wildfire residuals vary as a function of measurement, spatial resolutions, and fire event sizes, suggesting the variation in fire intensity and severity across the fire events. The patterns of wildfire residuals are also sensitive to changing scale, but the responses of the spatial metrics to changing spatial resolutions are grouped into three categories: monotonic change and predictable response in which three shape related metrics (LSI, MSI, and FRAC) show a predictable responsible; monotonic change with no simple scaling rule; and non-monotonic change with erratic response. The results also reveal that the factors that are incorporated in this study interactively affect the occurrence and distribution of residual patches, but natural firebreak features (e.g., wetlands and surface water) were among the most important predictors to explain wildfire residuals. Furthermore, the model implemented to predict residual patches has a reasonable or high predictive performance (‘marginal’ to ‘strong’ model performance) when it was applied in wildfire events that occurred in the same ecoregion. However, the predictive power of the model is low for the independent fire event (RED084). The overall findings of this dissertation reveal that the 1) predictive model based on RF is robust enough to determine the relative importance of the predictors and their marginal effect; 2) the model was flexible enough to identify areas where wildfire residuals are likely to occur; and 3) there is a repeatable, robust measurement framework for characterizing residual patches and understanding their variability across different wildfire events

The Application Of Chemometrics To The Detection And Classification Of Ignitable Liquids In Fire Debris Using The Total Ion Spectrum

Current methods in ignitable liquid identification and classification from fire debris rely on pattern recognition of ignitable liquids in total ion chromatograms, extracted ion profiles, and target compound comparisons, as described in American Standards for Testing and Materials E1618-10. The total ion spectra method takes advantage of the reproducibility among sample spectra from the same American Society for Testing and Materials class. It is a method that is independent of the chromatographic conditions that affect retention times of target compounds, thus aiding in the use of computer-based library searching techniques. The total ion spectrum was obtained by summing the ion intensities across all retention times. The total ion spectrum from multiple fire debris samples were combined for target factor analysis. Principal components analysis allowed the dimensions of the data matrix to be reduced prior to target factor analysis, and the number of principal components retained was based on the determination of rank by median absolute deviation. The latent variables were rotated to find new vectors (resultant vectors) that were the best possible match to spectra in a reference library of over 450 ignitable liquid spectra (test factors). The Pearson correlation between target factors and resultant vectors were used to rank the ignitable liquids in the library. Ignitable liquids with the highest correlation represented possible contributions to the sample. Posterior probabilities for the ASTM ignitable liquid classes were calculated based on the probability distribution function of the correlation values. The ASTM ignitable liquid class present in the sample set was identified based on the class with the highest posterior probability value. iv Tests included computer simulations of artificially generated total ion spectra from a combination of ignitable liquid and substrate spectra, as well as large scale burns in 20’x8’x8’ containers complete with furnishings and flooring. Computer simulations were performed for each ASTM ignitable liquid class across a range of parameters. Of the total number of total ion spectra in a data set, the percentage of samples containing an ignitable liquid was varied, as well as the percent of ignitable liquid contribution in a given total ion spectrum. Target factor analysis was them performed on the computer-generated sample set. The correlation values from target factor analysis were used to calculate posterior probabilities for each ASTM ignitable liquid class. Large scale burns were designed to test the detection capabilities of the chemometric approach to ignitable liquid detection under conditions similar to those of a structure fire. Burn conditions were controlled by adjusting the type and volume of ignitable liquid used, the fuel load, ventilation, and the elapsed time of the burn. Samples collected from the large scale burns were analyzed using passive headspace adsorption with activated charcoal strips and carbon disulfide desorption of volatiles for analysis using gas chromatography-mass spectrometr