In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions. Manual reviews can be laborious and error prone. Automatic and accurate seizure non-seizure classification methods are desirable. A critical challenge is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure features, this paper leverages an emerging deep learning model, the independently recurrent neural network (IndRNN), to construct a new approach for the seizure non-seizure classification. This new approach gradually expands the time scales, thereby extracting temporal and spatial features from the local time duration to the entire record. Evaluations are conducted with cross-validation experiments across subjects over the noisy data of CHB-MIT. Experimental results demonstrate that the proposed approach outperforms 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.
Shoeb and Guttag propose a method to construct a patient-specific detector for seizure detection by using the support vector machine (SVM) @cite_1 . The method leverages filters to extract spectral features over each channel, and then stacks feature vectors to catch time-evolution information. A sensitivity of 96 propose a wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG @cite_3 . In this algorithm, the EEG from each channel is decomposed by wavelet packet transform, and a patient-specific measure is deployed by using wavelet coefficients to separate the seizure and non-seizure states. Utilizing the measure, a combined seizure index is derived for each epoch of every EEG channel. Through inspecting the combined seizure index, proper channel alarms are generated.
@cite_1 @cite_3
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A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5 , a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy. 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. 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. Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25 and a false detection rate of 0.13 h with a mean delay time of 13.8 s.