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.
design a deep neural network for seizure non-seizure classification by using LSTM @cite_12 . It extracts temporal features by using LSTM. present a 13-layers deep neural network for seizure non-seizure classification by using CNN @cite_15 . The two approaches are evaluated over the same EEG data set provided by University of Bonn @cite_4 . The LSTM approach achieves performances of 100 These developed seizure-detection methods based on traditional machine learning techniques can work well with small amount of samples. They often need crafted features and manually selecting features. In the currently developed seizure non-seizure classification methods based on deep learning techniques, most methods are based on classical neural network models, such as CNN, RNN, and LSTM. These models have their limitations for processing EEG signal data, such as suffering from gradient vanishing or exploding problem.
@cite_15 @cite_4 @cite_12
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Recurrent Neural Networks (RNNs) with sophisticated units that implement a gating mechanism have emerged as powerful technique for modeling sequential signals such as speech or electroencephalography (EEG). The latter is the focus on this paper. A significant big data resource, known as the TUH EEG Corpus (TUEEG), has recently become available for EEG research, creating a unique opportunity to evaluate these recurrent units on the task of seizure detection. In this study, we compare two types of recurrent units: long short-term memory units (LSTM) and gated recurrent units (GRU). These are evaluated using a state of the art hybrid architecture that integrates Convolutional Neural Networks (CNNs) with RNNs. We also investigate a variety of initialization methods and show that initialization is crucial since poorly initialized networks cannot be trained. Furthermore, we explore regularization of these convolutional gated recurrent networks to address the problem of overfitting. Our experiments revealed that convolutional LSTM networks can achieve significantly better performance than convolutional GRU networks. The convolutional LSTM architecture with proper initialization and regularization delivers 30 sensitivity at 6 false alarms per 24 hours. Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1 up to 92.4 . Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach. Outstanding seizure detection algorithms have been developed over past two decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. These works are mainly based on heavily handcrafted feature extraction, which is computationally expensive and is shown to be data set specific. These issues greatly limit the applicability of such methods to hardware implementation, including in-silicon implementations such as application specific integrated circuits. In this paper, we propose an integer convolutional neural network (CNN) implementation, Integer-Net, as a memory-efficient unified hardware-friendly CNN framework. The performance of Integer-Net is evaluated with multiple time-series data sets consisting of intracranial and scalp electroencephalogram (EEG) signals. Integer-Net shows a consistent seizure detection performance across three data sets: Freiburg Hospital intracranial EEG data set, Children’s Hospital of Boston-MIT scalp EEG data set, and UPenn and Mayo Clinic’s seizure detection data set. Our experimental results show that a 4-bit Integer-Net leads to only 2 drop of accuracy compared with a 32-bit real-value resolution CNN model, while offering more than 7 times improvement in memory efficiency. We discuss the structure of the integer convolution to improve the computational gain and reduce the inference time that are crucial for real-time application. 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.