In current clinical practice, electroencephalograms (EEG) are reviewed and analyzed by well-trained neurologists to provide supports for therapeutic decisions. The way of manual reviewing is labor-intensive and error prone. Automatic and accurate seizure nonseizure classification methods are needed. One major problem is that the EEG signals for seizure state and nonseizure state exhibit considerable variations. In order to capture essential seizure features, this paper integrates an emerging deep learning model, the independently recurrent neural network (IndRNN), with a dense structure and an attention mechanism to exploit temporal and spatial discriminating features and overcome seizure variabilities. The dense structure is to ensure maximum information flow between layers. The attention mechanism is to capture spatial features. Evaluations are performed in cross-validation experiments over the noisy CHB-MIT data set. The obtained average sensitivity, specificity and precision of 88.80 , 88.60 and 88.69 are better than using the current state-of-the-art methods. In addition, we explore how the segment length affects the classification performance. Thirteen different segment lengths are assessed, showing that the classification performance varies over the segment lengths, and the maximal fluctuating margin is more than 4 . Thus, the segment length is an important factor influencing the classification performance.
@cite_30: Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40 sensitivity and 89.24 specificity with a mean detection delay of 2.63 s for the test set. The area under the ROC curve (AUC) is 96.94 , that is comparable to the current state-of-the-art with AUC of 96.29 .
@cite_7: Abstract Objective A novel patient-specific seizure detection algorithm is presented. As the spatial distribution of the ictal pattern is characteristic for a patient’s seizures, this work incorporates such information into the data representation and provides a learning algorithm exploiting it. Methods The proposed training algorithm uses nuclear norm regularization to convey structural information of the channel-feature matrices extracted from the EEG. This method is compared to two existing approaches utilizing the same feature set, but integrating the multichannel information in a different manner. The performances of the detectors are demonstrated on a publicly available dataset containing 131 seizures recorded in 892 h of scalp EEG from 22 pediatric patients. Results The proposed algorithm performed significantly better compared to the reference approaches ( p = 0.0170 and p = 0.0002). It reaches a median performance of 100 sensitivity, 0.11 h −1 false detection rate and 7.8 s alarm delay, outperforming a method in the literature using the same dataset. Conclusion The strength of our method lies within conveying structural information from the multichannel EEG. Such formulation automatically includes crucial spatial information and improves detection performance. Significance Our solution facilitates accurate classification performance for small training sets, therefore, it potentially reduces the time needed to train the detector before starting monitoring.
@cite_1: We present and evaluate a machine learning approach to constructing patient-specific classifiers that detect the onset of an epileptic seizure through analysis of the scalp EEG, a non-invasive measure of the brain's electrical activity. This problem is challenging because the brain's electrical activity is composed of numerous classes with overlapping characteristics. The key steps involved in realizing a high performance algorithm included shaping the problem into an appropriate machine learning framework, and identifying the features critical to separating seizure from other types of brain activity. When trained on 2 or more seizures per patient and tested on 916 hours of continuous EEG from 24 patients, our algorithm detected 96 of 173 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period. We also provide information about how to download the CHB-MIT database, which contains the data used in this study.
@cite_15: In this paper we present the application of ensemble learning to epileptic seizure detection problem. We propose a robust learning framework to mitigate class imbalance in large CHB-MIT (982 hrs) scalp EEG dataset. The algorithm being used is RUSBoost which is a hybrid data sampling and boosting technique designed especially for skewed classes. The data that is being used in this study has severe class imbalance, with average representation of 0.38 of seizure class to that of 99.62 of non-seizure class. The proposed approach shows the power of RUSBoost in terms of robustness and generalization. We compared our method with the most successful Support Vector Machine (SVM) based approach and report competitive results of 97 seizure detection accuracy, mean detection delay of 2.7s and false detection rate of 0.08 seizure hr. We also report fast training times of just under three minutes on average for average training data of 21 hrs.
@cite_10: Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88 for sensitivity, 88 for specificity and 93 for the area under the curve, with a 12 global error, using the k-NN classifier.
The approaches developed in @cite_1 @cite_15 @cite_7 @cite_10 @cite_30 extracted multichannel information to do seizure detection. However, they did not differentiate channels.