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You searched for subject:(seizure localization). Showing records 1 – 3 of 3 total matches.

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University of Kentucky

1. Huang, Yan. TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS.

Degree: 2020, University of Kentucky

The 2016 Epilepsy Innovation Institute (Ei2) community survey reported that unpredictability is the most challenging aspect of seizure management. Effective and precise detection, prediction, and localization of epileptic seizures is a fundamental computational challenge. Utilizing epilepsy data from multiple epilepsy monitoring units can enhance the quantity and diversity of datasets, which can lead to more robust epilepsy data analysis tools. The contributions of this dissertation are two-fold. One is the implementation of a temporal query for epilepsy data; the other is the machine learning approach for seizure detection, seizure prediction, and seizure localization. The three key components of our temporal query interface are: 1) A pipeline for automatically extract European Data Format (EDF) information and epilepsy annotation data from cross-site sources; 2) Data quantity monitoring for Epilepsy temporal data; 3) A web-based annotation query interface for preliminary research and building customized epilepsy datasets. The system extracted and stored about 450,000 epilepsy-related events of more than 2,497 subjects from seven institutes up to September 2019. Leveraging the epilepsy temporal events query system, we developed machine learning models for seizure detection, prediction, and localization. Using 135 extracted features from EEG signals, we trained a channel-based eXtreme Gradient Boosting model to detect seizures on 8-second EEG segments. A long-term EEG recording evaluation shows that the model can detect about 90.34% seizures on an existing EEG dataset with 961 hours of data. The model achieved 89.88% accuracy, 92.32% sensitivity, and 84.76% AUC based on the segments evaluation. We also introduced a transfer learning approach consisting of 1) a base deep learning model pre-trained by ImageNet dataset and 2) customized fully connected layers, to train the patient-specific pre-ictal and inter-ictal data from our database. Two convolutional neural network architectures were evaluated using 53 pre-ictal segments and 265 continuous hours of inter-ictal EEG data. The evaluation shows that our model reached 86.79% sensitivity and 3.38% false-positive rate. Another transfer learning model for seizure localization uses a pre-trained ResNext50 structure and was trained with an image augmentation dataset labeling by fingerprint. Our model achieved 88.22% accuracy, 34.99% sensitivity, 1.02% false-positive rate, and 34.3% positive likelihood rate.

Subjects/Keywords: EEG; Temporal Query; Data Quality; Seizure Detection; Seizure Prediction; Seizure Localization; Artificial Intelligence and Robotics; Databases and Information Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, Y. (2020). TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/cs_etds/98

Chicago Manual of Style (16th Edition):

Huang, Yan. “TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS.” 2020. Doctoral Dissertation, University of Kentucky. Accessed October 01, 2020. https://uknowledge.uky.edu/cs_etds/98.

MLA Handbook (7th Edition):

Huang, Yan. “TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS.” 2020. Web. 01 Oct 2020.

Vancouver:

Huang Y. TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS. [Internet] [Doctoral dissertation]. University of Kentucky; 2020. [cited 2020 Oct 01]. Available from: https://uknowledge.uky.edu/cs_etds/98.

Council of Science Editors:

Huang Y. TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS. [Doctoral Dissertation]. University of Kentucky; 2020. Available from: https://uknowledge.uky.edu/cs_etds/98


University of Pennsylvania

2. Blanco, Justin A. UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS.

Degree: 2010, University of Pennsylvania

Quality of life for the more than 15 million people with drug-resistant epilepsy is tied to how precisely the brain areas responsible for generating their seizures can be localized. High-frequency (100-500 Hz) field-potential oscillations (HFOs) are emerging as a candidate biomarker for epileptogenic networks, but quantitative HFO studies are hampered by selection bias arising out of the need to reduce large volumes of data in the absence of capable automated processing methods. In this thesis, I introduce and evaluate an algorithm for the automatic detection and classification of HFOs that can be deployed without human intervention across long, continuous data records from large numbers of patients. I then use the algorithm in analyzing unique macro- and microelectrode intracranial electroencephalographic recordings from human neocortical epilepsy patients and controls. A central finding is that one class of HFOs discovered by the algorithm (median bandpassed spectral centroid ~140 Hz) is more prevalent in the seizure onset zone than outside. The outcomes of this work add to our understanding of epileptogenic networks and are suitable for near-term translation into improved surgical and device-based treatments.

Subjects/Keywords: high-frequency oscillations; epilepsy; seizure localization; neocortex; automated EEG analysis; intracranial EEG; Biomedical Engineering and Bioengineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Blanco, J. A. (2010). UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/418

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Blanco, Justin A. “UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS.” 2010. Thesis, University of Pennsylvania. Accessed October 01, 2020. https://repository.upenn.edu/edissertations/418.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Blanco, Justin A. “UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS.” 2010. Web. 01 Oct 2020.

Vancouver:

Blanco JA. UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS. [Internet] [Thesis]. University of Pennsylvania; 2010. [cited 2020 Oct 01]. Available from: https://repository.upenn.edu/edissertations/418.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Blanco JA. UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS. [Thesis]. University of Pennsylvania; 2010. Available from: https://repository.upenn.edu/edissertations/418

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Adhikari, Bhim M. Oscillatory Network Activity in Brain Functions and Dysfunctions.

Degree: PhD, Physics and Astronomy, 2014, Georgia State University

Recent experimental studies point to the notion that the brain is a complex dynamical system whose behaviors relating to brain functions and dysfunctions can be described by the physics of network phenomena. The brain consists of anatomical axonal connections among neurons and neuronal populations in various spatial scales. Neuronal interactions and synchrony of neuronal oscillations are central to normal brain functions. Breakdowns in interactions and modifications in synchronization behaviors are usual hallmarks of brain dysfunctions. Here, in this dissertation for PhD degree in physics, we report discoveries of brain oscillatory network activity from two separate studies. These studies investigated the large-scale brain activity during tactile perceptual decision-making and epileptic seizures. In the perceptual decision-making study, using scalp electroencephalography (EEG) recordings of brain potentials, we investigated how oscillatory activity functionally organizes different neocortical regions as a network during a tactile discrimination task. While undergoing EEG recordings, blindfolded healthy participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. Based on the current dipole modeling in the brain, we found that the source-level peak activity appeared in the left primary somatosensory cortex (SI), right lateral occipital complex (LOC), right posterior intraparietal sulcus (pIPS) and finally left dorsolateral prefrontal cortex (dlPFC) at 45, 130, 160 and 175 ms respectively. Spectral interdependency analysis showed that fine tactile discrimination is mediated by distinct but overlapping ~15 Hz beta and ~80 Hz gamma band large-scale oscillatory networks. The beta-network that included all four nodes was dominantly feedforward, similar to the propagation of peak cortical activity, implying its role in accumulating and maintaining relevant sensory information and mapping to action. The gamma-network activity, occurring in a recurrent loop linked SI, pIPS and dlPFC, likely carrying out attentional selection of task-relevant sensory signals. Behavioral measure of task performance was correlated with the network activity in both bands. In the study of epileptic seizures, we investigated high-frequency (> 50 Hz) oscillatory network activity from intracranial EEG (IEEG) recordings of patients who were the candidates for epilepsy surgery. The traditional approach of identifying brain regions for epilepsy surgery usually referred as seizure onset zones (SOZs) has not always produced clarity on SOZs. Here, we investigated directed network activity in the frequency domain and found that the high frequency (>80 Hz) network activities occur before the onset of any visible ictal activity, andcausal relationships involve the recording electrodes where clinically identifiable seizures later develop. These findings suggest that high-frequency network activities and their causal relationships can… Advisors/Committee Members: Mukesh Dhamala, Vadym M Apalkov, Richard W Briggs, Gennady S Cymbalyuk, Brian D Thoms.

Subjects/Keywords: Perceptual decision-making; Spectral Granger causality; Cortical sources in tactile discrimination; High-frequency oscillations; seizure localization; Epilepsy surgery

…applications. Accurate localization of the seizure onset zones based on the information obtained from… …38 4 LOCALIZING EPILEPTIC SEIZURE ONSETS WITH GRANGER CAUSALITY ... 40… …seizure event in a patient. A green vertical line at t = 9.0 s marks the beginning time of a… …visually identified seizure event. …... 43 Figure 4.2… …The white dashed lines in (b) and (c) represent the seizure onset times… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Adhikari, B. M. (2014). Oscillatory Network Activity in Brain Functions and Dysfunctions. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/phy_astr_diss/67

Chicago Manual of Style (16th Edition):

Adhikari, Bhim M. “Oscillatory Network Activity in Brain Functions and Dysfunctions.” 2014. Doctoral Dissertation, Georgia State University. Accessed October 01, 2020. https://scholarworks.gsu.edu/phy_astr_diss/67.

MLA Handbook (7th Edition):

Adhikari, Bhim M. “Oscillatory Network Activity in Brain Functions and Dysfunctions.” 2014. Web. 01 Oct 2020.

Vancouver:

Adhikari BM. Oscillatory Network Activity in Brain Functions and Dysfunctions. [Internet] [Doctoral dissertation]. Georgia State University; 2014. [cited 2020 Oct 01]. Available from: https://scholarworks.gsu.edu/phy_astr_diss/67.

Council of Science Editors:

Adhikari BM. Oscillatory Network Activity in Brain Functions and Dysfunctions. [Doctoral Dissertation]. Georgia State University; 2014. Available from: https://scholarworks.gsu.edu/phy_astr_diss/67

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