A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

Background The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. Methods A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. Results The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. Conclusions The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed

Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use?

This data-driven computational psychiatry research proposes a novel machine learning approach to developing predictive models for the onset of first-episode psychosis, based on artificial neural networks. The performance capabilities of the predictive models are enhanced and evaluated by a methodology consisting of novel model optimisation and testing, which integrates a phase of model tuning, a phase of model post-processing with ROC optimisation based on maximum accuracy, Youden and top-left methods, and a model evaluation with the k-fold cross-testing methodology. We further extended our framework by investigating the cannabis use attributes’ predictive power, and demonstrating statistically that their presence in the dataset enhances the prediction performance of the neural network models. Finally, the model stability is explored via simulations with 1000 repetitions of the model building and evaluation experiments. The results show that our best Neural Network model’s average accuracy of predicting first-episode psychosis, which is evaluated with Monte Carlo, is above 80%

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

In recent years, a number of researches started to investigate the existence of links between cannabis use and psychotic disorder. More recently, artificial neural networks and in particular deep learning have set a revolutionary wave in pattern recognition and machine learning. This study proposes a novel machine learning approach based on neural network and deep learning algorithms, to developing highly accurate predictive models for the onset of first-episode psychosis. Our approach is based also on a novel methodology of optimising and post-processing the predictive models in a computationally intensive framework. A study of the trade-off between the volume of the data and the extent of uncertainty due to missing values, both of which influencing the predictive performance, enhanced this approach. Furthermore, we extended our approach by proposing and encapsulating a novel post-processing k-fold cross-testing method in order to further optimise, and test these models. The results show that the average accuracy in predicting first-episode psychosis achieved by our models in intensive Monte Carlo simulation, is about 89%

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Generating thermal facial signatures using thermal infrared images

This paper presents preliminary findings using thermal infrared imaging for the detection of the human face vasculature network at the skin surface and the generation of thermal facial signatures. A thermal infrared camera with reasonable sensitivity provides the ability to image superficial blood vessels on the human skin. The experiment presented here consists of the image processing techniques used in thermal infrared images captured using a mid-wave infrared camera from FLIR systems. For the purpose of this experiment thermal images were obtained from 10 volunteers, they were asked to sit straight in front of the thermal infrared camera and a snapshot was taken of their frontal view. The thermal infrared images were then analyzed using digital image processing techniques to enhance and detect the facial vasculature network of the volunteers and generate a thermal facial signature for each volunteer

Scenic routing navigation using property valuation

Abstract Extensive prior work has provided methods for the optimization of routing based on weights assigned to travel duration, and/or travel cost, and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph in order to find the best route. Some users desire that the routing suggestion include consideration pertaining to the scenic-architectural quality of the path. For example, a user may seek a leisure walk via what they might deem as visually attractive architecture. Here, we are proposing a method to quantify such user preferences and scenic quality and to augment the standard routing methods by giving weight to the scenic quality. That is, instead of suggesting merely the time and cost-optimal route, we will find the best route that is tailored towards the user’s scenic quality preferences as an additional criterion to the time and cost. The proposed method uniquely weighs the scenic interest or residential street segments based on the property valuation data

Classification of Typical and Atypical Language Network Activations Using Nonlinear Decision Functions

Language dominance behavior is identified as typical/atypical based on the asymmetry of the brain activation. Typical language is commonly defined by left brain hemisphere activation dominance during performing language tasks, while atypical language involves either right or both brain hemispheres. Traditionally, the methods used to identify the asymmetry of the brain activation are expert visual assessment and lateralization index (LI) computation. This paper presents a novel application of a supervised learning machine paradigm called Nonlinear Decision Functions (NDF). The merits of this paradigm are exploited on providing an automatic procedure for the identification of typical/atypical language dominance. NDF are invaluable tools for the resolution of real-world problems such as the one addressed in this paper. To identify language behavior, the subject undergoes an fMRI test. The resulting 4-D dataset (3D spatial information plus time series) is processed. Based on statistical and image analyses, a brain activation map (BAM) is generated. A total of 103 fMRI datasets from 5 different hospitals were analyzed, with 64 healthy control (HC) datasets, and 39 LRE datasets. On using NDFs on the basis of the demographics as well as the extent and intensity of these BAMs,  the  results obtained yielded a sensitivity of 80.6%, a specificity of 70.5%, an accuracy of 97.8% and a precision of 98.2%

An effective novel patient specific Gaussian template based scheme for somatosensory evoked potential detection

Somatosensory evoked potentials (SSEPs) have been widely used for intra-operative neurophysiological monitoring (IONM) in surgeries for scoliosis and spinal cord related surgical procedures. Current technological trends show that at least 200-300 trials are required to generate a readable SSEP signal. A novel signal processing approach is outlined in the article, which aims at reducing the number of trials required for SSEP generation using only 30 trials. The analysis was performed on data recorded in seven patients undergoing surgery, where the posterior tibial nerve was stimulated and the SSEP response was recorded from scalp EEG using two bipolar electrodes, C 3 -C 4 and C Z -F Z . The time latencies of the P37 and the N45 peaks are detected along with the peak-to-peak amplitudes. The time latencies are detected with a mean accuracy greater than 95%. Also, the P37 and N45 peak latencies and the peak-to-peak amplitude were found to be consistent throughout the surgical procedure within the 10% and 50% clinical limits set for the time latencies and the peak-to-peak amplitude, respectively

A New Algorithm as an Extension to the Gradient Descent Method for Functional Brain Activation Classification

The functional activation of the brain gets affected in conditions such as brain-tumor, localization-related epilepsy (LRE) and lesions. Typical brain activation is such that the left brain is dominant as compared to the right brain. In order to distinguish between the two groups -typical and atypical - the patients undergo functional Magnetic Resonance Imaging (fMRI) test. Based on the processed fMRI maps, nonlinear decision functions (NDF) are used to determine the laterality. Here an alternate algorithm called the ‘Iterative Random Training-Testing Algorithm’, a modification of the well known gradient descent algorithm, which is used as a means for enhancing the results of the classification, is presented. The algorithm aims at improving the sensitivity of results obtained in earlier studies reported in literature. Improving the sensitivity is of prime importance since sensitivity suggests the proportion of false negatives in the classification results. False negatives are critical in clinical decision making. The algorithm divides the training data set randomly into a pure-training set and cross-validation training set. The decision function is trained with the elements assigned to the pure training set and then tested with the element of the cross validation training set. The whole process is repeated a number of times with the aim that the random division of the data set would take into consideration various formations of the data yielding better results. The results of the algorithm showed an improvement in the sensitivity of 2 to 5% with no significant changes in the accuracy, specificity or precision