An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced. The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Machine Learning in Population Health: Frequent Emergency Department Utilization Pattern Identification and Prediction

Emergency Department (ED) overcrowding is an emerging risk to patient safety and may significantly affect chronically ill people. For instance, overcrowding in an ED may cause delays in patient transportation or revenue loss for hospitals due to hospital diversion. Frequent users with avoidable visits play a significant role in imposing such challenges to ED settings. Non-urgent or "avoidable" ED use induces overcrowding and cost increases due to unnecessary tests and treatment. It is, therefore, valuable to understand the pattern of the ED visits among a population and prospectively identify ED frequent users, to provide stratified care management and resource allocation. Although most current models use classical methods like descriptive analysis or regression modelling, more sophisticated techniques may be needed to increase the accuracy of outcomes where big data is in use. This study focuses on the Machine Learning (ML) techniques to identify the ED usage pattern among frequent users and to evaluate the predicting ability of the models. I performed an extensive literature review to generate a list of potential predictors of ED frequent use. For this thesis, I used Korean Health Panel data from 2008 to 2015. Individuals with at least one ED visit were included, among whom those with four or more visits per year were considered frequent ED users. Demographic and clinical data was collected. The relationship between predictors and ED frequent use was examined through multivariable analysis. A K-modes clustering algorithm was applied to identify ED utilization patterns among frequent users. Finally, the performance of four machine learning classification algorithms was assessed and compared to logistic regression. The classification algorithms used in my thesis were Random Forest, Support Vector Machine (SVM), Bagging, and Voting. The models' performance was evaluated based on Positive Predictive Value (PPV), sensitivity, Area Under Curve (AUC), and classification error. A total of 9,348 individuals with 15,627 ED visits were eligible for this study. Frequent ED users accounted for 2.4% of all ED visits. Frequent ED users tended to be older, male, and more likely to be using ambulance as a mode of transport than non‐frequent ED users. In the cluster analysis, we identified three subgroups among frequent ED users: (i) older patients with respiratory system complaints, the highest discharged rates who were more likely to visit in Spring and Winter, (ii) older patients with the highest rate of hospitalization, who are also more likely to have used ambulance, and visited ED due to circulatory system complaints, (iii) younger patients, mostly female, with the highest rate of ED visits in summer, and lowest rate of using an ambulance, who visited ED mostly due to damages such as injuries, poisoning, etc. The ML classification algorithms predicted frequent ED users with high precision (90% - 98%) and sensitivity (87% - 91%), while showed high AUC scores from 89% for SVM to 96% for Random Forest, as well. The classification error varied among algorithms; logistic regression had the highest classification error (34.9%) while Random Forest had the least (3.8%). According to the Random Forest Importance Score, the top 5 factors predicting frequent users were disease category, age, day of the week, season, and sex. In this thesis, I showed how ML methods applies to ED users in population health. The study results show that ML classification algorithms are robust techniques with predictive power for future ED visit identification and prediction. As more data are collected and the amount of data availability increases, machine learning approaches is a promising tool for advancing the understanding of such ‘Big’ data

Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm

Epilepsy is a disorder of the nervous system that can affect people of any age group. With roughly 50 million people worldwide diagnosed with the disorder, it is one of the most common neurological disorders. The EEG is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic person will present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multichannel signal, which introduce a great challenge for seizure detection. For this study, the Temple University Hospital (TUH) EEG Seizure Corpus dataset was used. This paper proposes a novel channel selection method which isolates different frequency ranges within five channels. This is based upon the frequencies at which normal brain waveforms exhibit. A one second window was selected, with a 0.5 second overlap. Wavelet signal denoising was performed using Daubechies 4 wavelet decomposition, thresholding was applied using minimax soft thresholding criteria. Filter banking was used to localise frequency ranges from five specific channels. Statistical features were then derived from the outputs. After performing bagged tree classification using 500 learners, a test accuracy of 0.82 was achieved.Comment: 8 pages, 6 figures, accepted for publication at the 13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC

Multivariate Analysis of MR Images in Temporal Lobe Epilepsy

Epilepsy stands aside from other neurological diseases because clinical patterns of progression are unknown: The etiology of each epilepsy case is unique and so it is the individual prognosis. Temporal lobe epilepsy (TLE) is the most frequent type of focal epilepsy and the surgical excision of the hippocampus and the surrounding tissue is an accepted treatment in refractory cases, specially when seizures become frequent increasingly affecting the performance of daily tasks and significantly decreasing the quality of life of the patient. The sensitivity of clinical imaging is poor for patients with no hippocampal involvement and invasive procedures such as the Wada test and intracranial EEG are required to detect and lateralize epileptogenic tissue. This thesis develops imaging processing techniques using quantitative relaxometry and diffusion tensor imaging with the aiming to provide a less invasive alternative when detectability is low. Chapter 2 develops the concept of individual feature maps on regions of interest. A laterality score on these maps correctly distinguished left TLE from right TLE in 12 out of 15 patients. Chapter 3 explores machine learning models to detect TLE, obtaining perfect classification for left patients, and 88.9% accuracy for right TLE patients. Chapter 4 focuses on temporal lobe asymmetry developing a voxel-based method for assessing asymmetry and verifying its applicability to individual predictions (92% accuracy) and group-wise statistical analyses. Informative ROI and voxel-based informative features are described for each experiment, demonstrating the relative importance of mean diffusivity over other MR imaging alternatives in identification and lateralization of TLE patients. Finally, the conclusion chapter discuss contributions, main limitations and outlining options for future research