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1. Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

   https://doi.org/10.1371/journal.pone.0264537  

   Craley, Jeff; Jouny, Christophe; Johnson, Emily; Hsu, David; Ahmed, Raheel; Venkataraman, Archana (February 2022, PLOS ONE)

   Bernhardt, Boris C (Ed.)

   We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization. We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital. Our network achieves similar seizure detection performance to state-of-the-art methods and provides valuable localization information that has not previously been demonstrated in the literature. We also show the cross-site generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network . To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG. 

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2. Kriging-Bootstrapped DNN Hierarchical Model for Real-Time Seizure Detection from EEG Signals

   Olokodana, Ibrahim; Mohanty, Saraju; Kougianos, Elias (January 2020, Proceedings of the 6th IEEE World Forum on Internet of Things (WF-IoT))

   The Deep Neural Network (DNN) model is known for its high accuracy in classification tasks due to its intrinsic ability to learn the underlying patterns existing in a set of data. Hence it has gained momentum in seizure detection research, as in many other fields. However, its high performance is at the expense of an extensive training time. This is not appropriate for a real-time application such as seizure detection in which a swift reaction is required to save the life of the patient. This paper presents a novel Kriging-Bootstrapped Deep Neural Network hierarchical model for early seizure detection in which Kriging is first used to generate a well-correlated intermediate data set from the original input. The correlated data is then fed into the DNN for the final training. Experiments were carried out using electroencephalogram (EEG) data from both normal and epileptic patients. Results show that, with the same architecture and data size, the cumulative training time of the Krigging-Bootstrapped DNN is about 75% lower than that of the ordinary DNN without a compromise in performance as the proposed hybrid model shows a slightly better accuracy than the baseline DNN model. 

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3. Accuracy of Automated Machine Learning Software in Identifying EEGs with Prolonged Seizures

   Lin, Rebecca; Marquez, Destiny; Jacobson, Mercedes; Castaldi, Hannah; Buckman, Samuel; Shah, Vinit; Picone, Joseph (April 2020, Annual Meeting of the American Academy of Neurology (AAN))

   null (Ed.)

   Objective: To demonstrate that combining automatic processing of EEG data using high performance machine learning algorithms with manual review by expert annotators can quickly identify subjects with prolonged seizures. Background: Prolonged seizures are markers of seizure severity, risk of transformation into status epilepticus, and medical morbidity. Early recognition of prolonged seizures permits intervention and reduces morbidity. Design/Methods: We triaged the TUH EEG Corpus, an open source database of EEGs, by running a state-of-the-art hybrid LSTM-based deep learning system. Then, we postprocessed the output to identify high confidence hypotheses for seizures that were greater than three minutes in duration. Results: The triaging method selected 25 subjects for further review. 17 subjects had seizures; only 5 met criteria for seizures greater than 3 minutes. 11 subjects did not have a prior diagnosis of epilepsy. Among these, 63% had acute respiratory failure and 36% had cardiac arrest leading to seizures secondary to anoxic brain injury. 18 (72%) EEGs were obtained in long-term monitoring (LTM), 1 (4%) in the epilepsy monitoring unit (EMU), and 6 (24%) as a routine EEG (rEEG). 72.2% of seizures in LTM were identified correctly versus 66.7% in rEEGs. Of the 9 subjects who were deceased, 7 (78%) had been on LTM. The seizure detection algorithm misidentified seizures in 7 subjects (28%). A total of 22 (88%) subjects had some ictal pattern. Patterns mistaken for seizure activity included muscle artifact, generalized periodic discharges, generalized spike-and-wave, triphasic waves, and interestingly, an EEG recording captured during CPR. Conclusions: This hybrid approach, which combines state-of-the-art machine learning seizure detection software with human annotation, successfully identified prolonged seizures in 72% of subjects; 88% had ictal patterns. Prolonged seizures were more common in LTM subjects than the EMU and were associated with acute cardiac or pulmonary insult. 

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4. Distributed Kriging-Bootstrapped DNN Model for Fast, Accurate Seizure Detection from EEG Signals

   Olokodana, Ibrahim; Mohanty, Saraju; Kougianos, Elias (January 2020, Proceedings of the 19th IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   The modeling of the brain as a three-dimensional spatial object, similar to a geographical landscape, has the paved way for the successful application of Kriging methods in solving the seizure detection problem with good performance but in cubic computational time complexity. The Deep Neural Network (DNN) has been widely used for seizure detection due to its effectiveness in classification tasks, although at the cost of a protracted training time. While Kriging exploits the spatial correlation between data locations, DNN relies on its capacity to learn intrinsic representations within the dataset from the basest unit parts. This paper presents a Distributed Kriging-Bootstrapped Deep Neural Network (DNN) model as a twofold solution for fast and accurate seizure detection using brain signals collected with the electroencephalogram (EEG) from healthy subjects and patients of epilepsy. The proposed model parallelizes the Kriging computation into different cores in a machine and then produces a strongly correlated, unified quasi-output data which serves as an input to the Deep Neural Network. Experimental results validate the proposed model as superior to conventional Kriging methods and DNN by training in 91% less time than the basic DNN and about three times as fast as the ordinary Kriging-Bootstrapped Deep Neural Network (DNN) model while maintaining good performance in terms of sensitivity, specificity and testing accuracy compared to other models and existing works. 

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5. Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms

   https://doi.org/10.1007/978-3-030-36844-9  

   Golmohammadi, Meysam; Shah, Vinit; Obeid, Iyad; Picone, Joseph (April 2020, Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications)

   Obeid, Iyad; Selesnick, Ivan; Picone, Joseph (Ed.)

   Scalp electroencephalograms (EEGs) are the primary means by which phy-sicians diagnose brain-related illnesses such as epilepsy and seizures. Au-tomated seizure detection using clinical EEGs is a very difficult machine learning problem due to the low fidelity of a scalp EEG signal. Neverthe-less, despite the poor signal quality, clinicians can reliably diagnose ill-nesses from visual inspection of the signal waveform. Commercially avail-able automated seizure detection systems, however, suffer from unaccepta-bly high false alarm rates. Deep learning algorithms that require large amounts of training data have not previously been effective on this task due to the lack of big data resources necessary for building such models and the complexity of the signals involved. The evolution of big data science, most notably the release of the Temple University EEG (TUEG) Corpus, has mo-tivated renewed interest in this problem. In this chapter, we discuss the application of a variety of deep learning ar-chitectures to automated seizure detection. Architectures explored include multilayer perceptrons, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), gated recurrent units and residual neural networks. We use the TUEG Corpus, supplemented with data from Duke University, to evaluate the performance of these hybrid deep structures. Since TUEG contains a significant amount of unlabeled data, we also dis-cuss unsupervised pre-training methods used prior to training these com-plex recurrent networks. Exploiting spatial and temporal context is critical for accurate disambigua-tion of seizures from artifacts. We explore how effectively several conven-tional architectures are able to model context and introduce a hybrid system that integrates CNNs and LSTMs. The primary error modalities observed by this state-of-the-art system were false alarms generated during brief delta range slowing patterns such as intermittent rhythmic delta activity. A varie-ty of these types of events have been observed during inter-ictal and post-ictal stages. Training models on such events with diverse morphologies has the potential to significantly reduce the remaining false alarms. This is one reason we are continuing our efforts to annotate a larger portion of TUEG. Increasing the data set size significantly allows us to leverage more ad-vanced machine learning methodologies. 

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