4.7 Article

Scalp EEG functional connection and brain network in infants with West syndrome

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Regional Scalp EEGs Analysis and Classification on Typical Childhood Epilepsy Syndromes

Xiaonan Cui et al.

Summary: Epilepsy syndromes are complex childhood nervous system diseases with various types of seizures. Accurate classification of these syndromes is crucial for diagnosis and treatment. This study analyzes the correlation between time/frequency-domain regional scalp EEG features and typical epilepsy syndromes, and proposes a transfer network-based classification model. The results show that specific EEG features are similar within each syndrome and different between syndromes, and a ResNet50 model based on deep transfer feature learning enhances the classification performance.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

Deep feature fusion based childhood epilepsy syndrome classification from electroencephalogram

Xiaonan Cui et al.

Summary: Accurate classification of children's epilepsy syndromes is crucial for the diagnosis and treatment of epilepsy. This paper presents a study on the classification of two common epilepsy syndromes using a novel feature fusion model based on deep transfer learning and conventional time-frequency representation. Experimental results demonstrate high classification accuracy of the proposed algorithm.

NEURAL NETWORKS (2022)

Article Computer Science, Artificial Intelligence

3D residual-attention-deep-network-based childhood epilepsy syndrome classification

Yuanmeng Feng et al.

Summary: In this study, a novel 3D residual-attention-module-based deep network (AR3D) is developed to explore the spatial and time-frequency features of multichannel EEGs for epilepsy analysis and diagnosis. The results show that the proposed AR3D algorithm outperforms several state-of-the-art 2D and 3D convolution deep networks in childhood epilepsy syndrome classification.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Engineering, Biomedical

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms

Colin M. McCrimmon et al.

Summary: This study aimed to characterize the spatial distribution and rates of HFOs in IS patients and healthy controls, finding that HFO rates were significantly higher in IS patients and exhibited spatial differences. The fully automated algorithm used in this study showed sufficient accuracy in detecting HFOs in long-term scalp EEG data.

JOURNAL OF NEURAL ENGINEERING (2021)

Article Clinical Neurology

Electroencephalography complexity in infantile spasms and its association with treatment response

Yen-Ju Chu et al.

Summary: The study found that the total EEG complexity of infantile spasms patients before treatment and 6 months after ACTH therapy is associated with spasm-freedom.

CLINICAL NEUROPHYSIOLOGY (2021)

Article Computer Science, Artificial Intelligence

Semi-dilated convolutional neural networks for epileptic seizure prediction

Ramy Hussein et al.

Summary: This study introduces a novel semi-dilated convolutional neural network architecture that outperforms previous methods in predicting epileptic seizures, achieving an average prediction sensitivity of 98.90% for scalp EEG.

NEURAL NETWORKS (2021)

Article Computer Science, Information Systems

Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model

Jiuwen Cao et al.

Summary: A novel unsupervised learning algorithm based on a hybrid thresholding and Gaussian mixture model is proposed for accurate and efficient eye blink detection in EEG signals. The algorithm extracts features and utilizes GMM training to achieve high detection precision and F1 score on EEG datasets with eye blink artifacts.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Computer Science, Artificial Intelligence

Epileptic Signal Classification Based on Synthetic Minority Oversampling and Blending Algorithm

Dinghan Hu et al.

Summary: This article introduces a novel algorithm for epileptic classification and seizure detection for imbalanced data, utilizing statistical features extracted from multichannel EEGs and applying data balancing techniques, followed by a blending algorithm for feature learning and epileptic signal classification, achieving high accuracy rates in the experiments.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2021)

Article Engineering, Biomedical

BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

Zhendi Xu et al.

Summary: The study introduces a novel spike detection algorithm for BECT based on time domain EEG sequence features and LSTM neural network. The algorithm addresses spike imbalance issue in EEGs using SMOTE and achieves promising results in terms of F1 score, sensitivity, and precision, outperforming several state-of-the-art methods.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Article Engineering, Biomedical

Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features

Jianhui Wang et al.

Summary: This paper proposes a novel eye blink artifact detection algorithm based on multi-dimensional EEG features, with specific focus on filtering frontal epileptiform discharges. The effectiveness of the algorithm is demonstrated through experiments and comparisons with other eye blink artifact detection methods.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Article Engineering, Biomedical

High-Frequency Hubs of the Ictal Cross-Frequency Coupling Network Predict Surgical Outcome in Epilepsy Patients

Chunsheng Li et al.

Summary: Seizure generation is thought to be driven by epileptogenic networks, and high-frequency hubs play a more critical role in determining treatment outcomes. HF hubs show increased activity in the early and middle stages of seizures, while LF hubs show increased activity in the late stages. HF hubs can more accurately predict treatment outcomes, providing more precise targets for surgical interventions or neuromodulation therapies.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Article Engineering, Biomedical

Identifying Refractory Epilepsy Without Structural Abnormalities by Fusing the Common Spatial Patterns of Functional and Effective EEG Networks

Yuhang Lin et al.

Summary: By extracting spatial pattern network features, resting-state scalp electroencephalogram can effectively differentiate patients with drug refractory epilepsy from those with medically controlled epilepsy, with high classification accuracy.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Article Engineering, Biomedical

Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning

Jiayang Guo et al.

Summary: Successful epilepsy surgeries rely heavily on pre-operative localization of epileptogenic zones, with high frequency oscillations being considered as potential biomarkers for identification. A hypergraph-based detector was developed to automatically detect high frequency oscillations in SEEG signals, achieving high accuracy, sensitivity, and specificity in clinical evaluations.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Article Clinical Neurology

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

Derek K. Hu et al.

CLINICAL NEUROPHYSIOLOGY (2020)

Review Clinical Neurology

Emerging roles of network analysis for epilepsy

William Stacey et al.

EPILEPSY RESEARCH (2020)

Article Computer Science, Artificial Intelligence

EEG based multi-class seizure type classification using convolutional neural network and transfer learning

S. Raghu et al.

NEURAL NETWORKS (2020)

Article Computer Science, Artificial Intelligence

Epileptic Signal Classification With Deep EEG Features by Stacked CNNs

Jiuwen Cao et al.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2020)

Review Pediatrics

Infantile Spasms: An Update on Pre-Clinical Models and EEG Mechanisms

Remi Janicot et al.

CHILDREN-BASEL (2020)

Article Clinical Neurology

Slower alpha rhythm associates with poorer seizure control in epilepsy

Eugenio Abela et al.

ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY (2019)

Article Mathematics, Interdisciplinary Applications

Directed clustering in weighted networks: A new perspective

G. P. Clemente et al.

CHAOS SOLITONS & FRACTALS (2018)

Article Neurosciences

Network dynamics in the healthy and epileptic developing brain

Richard Rosch et al.

NETWORK NEUROSCIENCE (2018)

Article Neurosciences

Electrocorticographic Dynamics as a Novel Biomarker in Five Models of Epileptogenesis

Dan Z. Milikovsky et al.

JOURNAL OF NEUROSCIENCE (2017)

Review Behavioral Sciences

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

Boris C. Bernhardt et al.

EPILEPSY & BEHAVIOR (2015)

Review Clinical Neurology

Infantile spasms syndrome, West syndrome and related phenotypes: What we know in 2013

Piero Pavone et al.

BRAIN & DEVELOPMENT (2014)

Article Clinical Neurology

Brain network connectivity assessed using graph theory in frontotemporal dementia

Federica Agosta et al.

NEUROLOGY (2013)

Article Neurosciences

Complex network measures of brain connectivity: Uses and interpretations

Mikail Rubinov et al.

NEUROIMAGE (2010)

Review Neurosciences

Oscillatory interactions between sensorimotor cortex and the periphery

Stuart N. Baker

CURRENT OPINION IN NEUROBIOLOGY (2007)

Article Genetics & Heredity

Neurobehavioral disorders in children, adolescents, and young adults with Down syndrome

George Capone et al.

AMERICAN JOURNAL OF MEDICAL GENETICS PART C-SEMINARS IN MEDICAL GENETICS (2006)

Article Mechanics

Cartography of complex networks:: modules and universal roles -: art. no. P02001

R Guimerà et al.

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2005)

Review Mathematics, Applied

The structure and function of complex networks

MEJ Newman

SIAM REVIEW (2003)

Article Physics, Multidisciplinary

Efficient behavior of small-world networks

V Latora et al.

PHYSICAL REVIEW LETTERS (2001)

Article Clinical Neurology

EEG-defined subtypes of children with attention-deficit/hyperactivity disorder

AR Clarke et al.

CLINICAL NEUROPHYSIOLOGY (2001)