A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.

Support vector machines: Theory and applications

Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial pre-ictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients’ data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.

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Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network

Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.

A deep convolutional neural network model for automated identification of abnormal EEG signals

Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlation in the features obtained from multichannel EEG signals. Spectral band power, statistical moment and Hjorth parameters are used to reveal the frequency and time domain features of the EEG signals. The features are given as input to a convolutional neural network (CNN) by transforming into a sequence of multi-color images according to the topology of the EEG channels. The multi-frame 3D CNN model is proposed to evaluate the temporal and spatial correlation in training data collectively. The proposed 3D CNN model achieves a sensitivity of 85.7%, a false prediction rate of 0.096/h, and a proportion of time-in-warning of 10.5%, in the tests performed with 16 patients from the CHB-MIT scalp EEG dataset. The results show that the superiority of the proposed method to a Poisson based random predictor was statistically significant for 93.7% of the patients, at significance level of 0.05. Our experiments with various timing constraints show that epileptic stage lengths are an important factor affecting seizure performance. We present a subject-specific seizure prediction method that is robust for unbalanced data and can be generalized to any scalp EEG dataset without the need for subject-specific engineering.

Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach

This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase. A leave-one-out cross-validation strategy is adopted in the experiments. The experimental results for seizure prediction obtained from the records of 24 patients from the CHB-MIT database reveal that the proposed predictor can achieve an average sensitivity of 0.89, an average false prediction rate of 0.39, and an average prediction time of 68.71 minutes using a 120-minute prediction horizon.

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Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals

When deploying a model for object detection, a confidence score threshold is chosen to filter out false positives and ensure that a predicted bounding box has a certain minimum score. To achieve state-of-the-art performance on benchmark datasets, most neural networks use a rather low threshold as a high number of false positives is not penalized by standard evaluation metrics. However, in scenarios of Artificial Intelligence (AI) applications that require high confidence scores (e.g., due to legal requirements or consequences of incorrect detections are severe) or a certain level of model robustness is required, it is unclear which base model to use since they were mainly optimized for benchmark scores. In this paper, we propose a method to find the optimum performance point of a model as a basis for fairer comparison and deeper insights into the trade-offs caused by selecting a confidence score threshold.

Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation

Epilepsy is a neurological disorder that affects millions of people worldwide Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients It has achieved a 9175% average sensitivity and 9299% average specificity

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Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented. Two feature extraction approaches are investigated: direct and band-based approaches. In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment. In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector. Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined. The extracted feature vectors are applied to a random forest classifier, for the purpose of identification. This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB). The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach. A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.

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ECG-Based Subject Identification Using Statistical Features and Random Forest

In this work we focus on developing an accurate and lightweight automated diagnostic system for classifying abnormalities in breast cancer mammogram images using the Local Binary Patterns (LBP) feature extraction method at the heart of a machine learning model. We empirically show the success of the approach in recognising the presence or absence of abnormality(ies) with high predictive performance in terms of accuracy, precision, recall, and F1-score, and against other features extraction methods employed within different a number of classifiers. The reported results show a minimum of 13% performance gap in favour of our selected approach to the closest model. Our approach exceeds 90% in most of the models when applied over DDSM, the breast cancer mammography benchmark dataset.

Saud R. Alrshoud

Papers

Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals

Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

ECG-Based Subject Identification Using Statistical Features and Random Forest

An Efficient Abnormality Classification for Mammogram Images