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Patient-Specific Seizure Detection from Intra-cranial EEG Using High Dimensional Clustering

Haimonti Dutta, David Waltz, Karthik M. Ramasamy, Phil Gross, Ansaf Salleb-Aouissi, Hatim Diab, Manoj Pooleery, Catherine A. Schevon, Ronald Emerson
2010 2010 Ninth International Conference on Machine Learning and Applications  
In this paper, we describe the application of machine learning algorithms for building patient-specific seizure detectors on multiple frequency bands of intra-cranial electroencephalogram (iEEG) recorded  ...  We explore subsets of this data to build seizure detectors -we discuss several methods for extracting univariate and bivariate features from the channels and study the effectiveness of using high dimensional  ...  Features are extracted from very high-dimensional time series data and the task of the learning algorithm is to identify one of two clusters -seizure vs non-seizure.  ... 
doi:10.1109/icmla.2010.119 dblp:conf/icmla/DuttaWRGSDPSE10 fatcat:rtu22cq25jgrhdp6jcd36on2ca

Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings [article]

Melisa Maidana Capitán, Nuria Cámpora, Claudio Sebastián, Sigvard Silvia Kochen, Inés Samengo
2020 arXiv   pre-print
The algorithm is unsupervised, and performs a principal component analysis of intra-cranial EEG recordings, detecting transient fluctuations of the power in each frequency band.  ...  We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal.  ...  Materials and Methods EEG data sets Long-term intra-cranial EEG recordings were obtained from 5 hospitalized patients during 24hour video-EEG monitoring, lasting for 5 days.  ... 
arXiv:1902.11236v2 fatcat:2rhdcb4x2vdcnn5tmrqwqo6ibe

Towards Early Diagnosis of Epilepsy from EEG Data [article]

Diyuan Lu, Sebastian Bauer, Valentin Neubert, Lara Sophie Costard, Felix Rosenow, Jochen Triesch
2020 arXiv   pre-print
Here, we investigate if modern machine learning (ML) techniques can detect EPG from intra-cranial electroencephalography (EEG) recordings prior to the occurrence of any seizures.  ...  Specifically, the neural network is trained to distinguish five second segments of EEG recordings taken from either the pre-stimulation period or the post-stimulation period.  ...  Thodoroff et al. applied a deep RNN with a CNN to perform automated patient specific seizure detection with scalp EEG recovery 7 days No PPS 3 days 1 day 30 days Control B Late Early 3  ... 
arXiv:2006.06675v2 fatcat:hpzdfqusl5hvpfbs7icom4xuqy

Robust Unsupervised Transient Detection With Invariant Representation based on the Scattering Network [article]

Randall Balestriero, Behnaam Aazhang
2016 arXiv   pre-print
In this work, our primary application consists of predicting the onset of seizure in epileptic patients from subdural recordings as well as detecting inter-ictal spikes.  ...  This unsupervised approach is based on wavelet transforms and leverages the scattering network from Mallat et al. by deriving frequency invariance.  ...  EEG Data Presentation EEG data are recordings of neural activities through electrodes. In our cases these electrodes were intra-cranial and with a frequency sampling of 1000Hz.  ... 
arXiv:1611.07850v1 fatcat:dqaz3x4a7bh2fmp4kkzqipkomq

A case-study on learning from large-scale intracranial EEG data using multi-core machines and clusters

Haimonti Dutta, Huascar Fiorletta, Manoj Pooleery, Hatim Diab, Stanley German, David Waltz, Catherine A. Schevon
2011 Proceedings of the Third Workshop on Large Scale Data Mining Theory and Applications - LDMTA '11  
undergoing surgery to remove the portion of the brain from where seizures originate.  ...  An important reason why studies so far have been less than successful is that electroencephalogram (EEG) is not recorded at the granularity of the seizure generation process.  ...  (5) Each patient typically has one or more unique seizure signatures which vary from patient to patient. Thus most seizure detection algorithms typically provide patient specific solutions.  ... 
doi:10.1145/2002945.2002949 fatcat:2q4nua4m6jechhrkdwfe5cmuni

Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection

Naveen Verma, Kyong Ho Lee, Ali Shoeb
2011 Journal of Low Power Electronics and Applications  
The case study for this is a seizure-detection SoC that includes instrumentation and computation blocks in support of a system that exploits patient-specific modeling to achieve accurate performance for  ...  Intelligent biomedical devices implies systems that are able to detect specific physiological processes in patients so that particular responses can be generated.  ...  As an example, Figure 5a shows the linear decision boundary separating intra-cranial EEG (IEEG) seizure feature vectors (circles) from non-seizure feature vectors (crosses).  ... 
doi:10.3390/jlpea1010150 fatcat:5jfc45gvtjcnta5fsflsua7pmi

EEG Classification using Semi Supervised Learning

Shivshankar Kumar Yadav, Veena S.
2019 International Journal of Trend in Scientific Research and Development  
This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals.  ...  Decision making was performed in three stages:(i)feature extraction using Welch method power spectrum density estimation (PSD) (ii)dimensionality reduction using statistics over extracted features and  ...  CONCLUSION The aim of this study was to find a new scheme for classification of EEG signals and detection of epileptic seizure with high accuracy using power levels of PSD and neural networks.  ... 
doi:10.31142/ijtsrd23355 fatcat:vl24eoifyvhvhmfqcslohgfv4a

Epileptic Neuronal Networks: Methods of Identification and Clinical Relevance

Hermann Stefan, Fernando H. Lopes da Silva
2013 Frontiers in Neurology  
It is proposed that so-called "generalized epilepsies," such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate  ...  Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed.  ...  This is nicely illustrated in the case of patients with intractable Jacksonian seizures in whom intra-cranial EEG recordings (iEEG) were made in order to assess the indication of surgery (Akiyama et al  ... 
doi:10.3389/fneur.2013.00008 pmid:23532203 pmcid:PMC3607195 fatcat:7wqy2iqc2zh75blj6b2xk7fkra

Characterizing the seizure onset zone and epileptic network using EEG-fMRI in a rat seizure model

Junling Wang, Bin Jing, Ru Liu, Donghong Li, Wei Wang, Jiaoyang Wang, Jianfeng Lei, Yue Xing, Jiaqing Yan, Horace H Loh, Guangming Lu, Xiaofeng Yang
2021 NeuroImage  
This was to determine the optimal use of simultaneous EEG-fMRI recording in the SOZ localization. We observed a high spatial consistency between BOLD responses and the SOZ.  ...  In this study, we used simultaneous EEG-fMRI recording in a rat model of 4-aminopyridine-induced acute focal seizures to assess the spatial concordance between individual BOLD responses and the SOZ.  ...  EEG-fMRI or fMRI could be used to detect cortical BOLD activations related to seizures.  ... 
doi:10.1016/j.neuroimage.2021.118133 pmid:33951515 fatcat:yoxj3dmhyzftrjqro3gnn7npii

Disentangling the dynamic core: a research program for a neurodynamics at the large-scale

2003 Biological Research  
I emphasize how these nonlinear methods can be applied, what property might be inferred from neuronal signals, and where one might productively proceed for the future.  ...  Recent neuroimaging evidence appears to broadly support this hypothesis and suggests that a global brain dynamics emerges at the large scale level from the cooperative interactions among widely distributed  ...  Figure 3: A: Detection of the transient synchronous patterns between intra-cranial electrodes.  ... 
doi:10.4067/s0716-97602003000100006 fatcat:dr3jlhzwvbh7rmhlv2hgvperrq

Long-Term Effects of Temporal Lobe Epilepsy on Local Neural Networks: A Graph Theoretical Analysis of Corticography Recordings

Edwin van Dellen, Linda Douw, Johannes C. Baayen, Jan J. Heimans, Sophie C. Ponten, W. Peter Vandertop, Demetrios N. Velis, Cornelis J. Stam, Jaap C. Reijneveld, Olaf Sporns
2009 PLoS ONE  
Graphs (abstract network representations) were reconstructed from the PLI matrix and characterized by the clustering coefficient C (local clustering), the path length L (overall network interconnectedness  ...  Methods: Functional connectivity of the temporal lobe at the time of surgery was assessed by means of interictal electrocorticography (ECoG) recordings of 27 TLE patients by using the phase lag index (  ...  course when patients suffer from seizures more frequently.  ... 
doi:10.1371/journal.pone.0008081 pmid:19956634 pmcid:PMC2778557 fatcat:2oksqrd2abgm5ecopeisokz3pq

Remission of benign epilepsy with rolandic spikes: An EEG-based connectivity study at the onset of the disease and at remission

B. Clemens, S. Puskás, M. Besenyei, T. Spisák, M. Emri, I. Fekete
2013 Epilepsy Research  
Remote EEG synchronization (intra-hemispheric, cortico-cortical EEG functional connectivity, EEGfC) was computed by the LSC (LORETA Source Correlation) method, among 23 regions of interest (ROI) in both  ...  Both local and remote EEG synchronization were evaluated in very narrow frequency bands of 1 Hz bandwidth (VNB), from 1 to 25 Hz. Results. Individual results were presented.  ...  Additional bipolar derivations were used to differentiate between EEG and eye movement potentials and to detect myogenic activity. For EEG, the filters were set at 0.1 and 33.6 Hz.  ... 
doi:10.1016/j.eplepsyres.2013.04.006 pmid:23693025 fatcat:pagkvpxkgjeqrnowguo6cmj2km

Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions

Wei-Liang Chen, Julie Wagner, Nicholas Heugel, Jeffrey Sugar, Yu-Wen Lee, Lisa Conant, Marsha Malloy, Joseph Heffernan, Brendan Quirk, Anthony Zinos, Scott A. Beardsley, Robert Prost (+1 others)
2020 Frontiers in Neuroscience  
The use of high-density whole head optode arrays, precise sensor locations relative to the head, anatomical co-registration, short-distance channels, and multi-dimensional signal processing can be combined  ...  Advances in signal processing have moved fNIRS toward individual clinical use for detecting certain types of seizures, assessing autonomic function and cortical spreading depression.  ...  Using a voxel cluster correlation, they reported correlations ranging from 0.62 to 0.99 for both oxy-and deoxyhemoglobin.  ... 
doi:10.3389/fnins.2020.00724 pmid:32742257 pmcid:PMC7364176 fatcat:5viowfaqangnlgljildbio7uam

Delayed Arousal

Zirka H. Anastasian, Eugene Ornstein, Eric J. Heyer
2009 Anesthesiology Clinics  
Structural problems that impair consciousness arise from a small number of focal lesions to specific areas of the central nervous system, or from pathology affecting the cerebrum.  ...  Delayed arousal, therefore, may arise from structural problems that are pre-existent or new, or metabolic or functional disorders such as convulsive or nonconvulsive seizures.  ...  RO1-AG16404 from the National Institutes of Health.  ... 
doi:10.1016/j.anclin.2009.07.007 pmid:19825485 pmcid:PMC3036001 fatcat:sy6xhmvx4fazbk5lapq2bukpg4

32nd Meeting of the Canadian Congress of Neurological Sciences

1997 Canadian Journal of Neurological Sciences  
Muscle recordings were concomitantly recorded from the left biceps, triceps, first dorsal interosseous, tibialis anterior (TA) and soleus.  ...  SCEP and muscle responses were obtained as TCMS intensity increased from threshold ("T") for activation of a SCEP to T + 30% of the maximum output of the stimulator in 3 steps.  ...  We monitored 22 patients with acute MI in a CCU, using TCD to detect high intensity transient signals (HITS).  ... 
doi:10.1017/s0317167100038130 fatcat:l2qcc4qtubckhcmrsebny5e65m
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