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Article
Computer Science, Interdisciplinary Applications
Huan Luo et al.
Summary: This paper proposes a novel computational method to improve the accuracy and efficiency of dynamic response computation. It models the parameters of the system by minimizing the objective function and bypasses the need for training data and time step requirements. It also develops an efficient step-by-step solver and analyzes the extension of this method for nonlinear dynamic response computation.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Wei Yan Peh et al.
Summary: This study proposes a patient-independent seizure detector that can automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). It utilizes a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments, extracts regional features from channel-level outputs to detect seizures in multi-channel EEG segments, and applies post-processing filters to determine seizure start and end points in multi-channel EEGs.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Ding et al.
Summary: In this paper, a novel model for seizure prediction is proposed, which combines a convolutional neural network (CNN) with multi-head attention mechanism. Compared with current CNN models, this model shows higher flexibility and training efficiency, and achieves better performance in terms of event-level sensitivity, false prediction rate (FPR), and epoch-level F1. Additionally, the proposed method achieves a stable length of seizure prediction time. Experimental comparisons demonstrate that this method outperforms other prediction methods in terms of prediction and generalization performance.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiale Wang et al.
Summary: In recent years, deep learning has been proven to be competitive in seizure detection, but existing methods have limitations in capturing temporal links between EEG signals. To overcome this, a Dual-Modal Information Bottleneck (Dual-modal IB) network is proposed, which extracts EEG features from both time series and spectrogram dimensions and utilizes a bidirectional LSTM to model temporal relationships. Experiments on CHB-MIT dataset demonstrate that the proposed framework achieves high sensitivity, specificity, and accuracy in segment-based and event-based evaluation metrics.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Islam A. Fouad
Summary: Vehicle accidents on long routes are often caused by drowsy drivers due to the lack of an alertness measurement system. This paper aims to detect drivers' sleepiness using a powerful software tool that analyzes EEG signals. Various machine learning algorithms were applied to the EEG signals of twelve subjects to measure their performance.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Yanna Zhao et al.
Summary: This paper proposes a hybrid attention network (HAN) based on deep learning for automatic seizure detection from electroencephalography (EEG), which extracts spatial features using graph attention network (GAT) at the front end and time features using Transformer at the back end. HAN leverages the attention mechanism to fully capture the spatial-temporal correlation of EEG signals. Experimental results on the public CHB-MIT database demonstrate the effectiveness of HAN in both patient-specific and patient-independent scenarios.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Khadijeh Raeisi et al.
Summary: In this study, a class-imbalance aware and explainable deep learning approach based on CNNs and GATs is proposed for accurate automated detection of neonatal seizures. The model integrates temporal and spatial information of EEG signals, develops a feature set using 1D CNNs, and utilizes GAT for attention mechanism and visualization of important regions. Under-sampling and focal loss techniques are used to tackle class imbalance. The final ST-GAT outperformed previous benchmarked methods with high accuracy and potential for clinical applications.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jian Lian et al.
Summary: This research proposes a hybrid deep learning framework for epileptic seizure recognition using electroencephalogram signals. The proposed model combines graph neural network and transformer architectures to handle the association between multichannel signals and shows promising classification performance.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Niki Kiriakidou et al.
Summary: Treatment effect estimation is important in various scientific and industrial fields. The abundance of observational data has led to an increasing use of these data for estimating causal effects. However, improper handling of these data can result in inaccurate estimations. To address this, machine learning techniques, especially neural network models, have been proposed to improve the precision of causal effect estimations. In this work, a new methodology called Nearest Neighboring Information for Causal Inference (NNCI) is proposed, which integrates valuable nearest neighboring information into neural network-based models for treatment effect estimation. Experimental results demonstrate that incorporating NNCI into state-of-the-art neural network models significantly enhances treatment effect estimations on challenging benchmarks.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dongsheng Liu et al.
Summary: In this study, a novel epileptic seizure prediction method based on multi-head attention augmented convolutional neural network is proposed to capture global information of input signals. The method involves data enhancement, EEG segmentation, Stockwell transform for time-frequency distribution, attention augmented convolutional network for feature extraction and classification, and post-processing to reduce false prediction rate. The validation results using the CHB-MIT EEG database showed high sensitivity and low false prediction rate, demonstrating the potential of this method for clinical applications.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Sepehr Shirani et al.
Summary: An adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to accurately localize delayed responses (DRs) from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The AI-LCMV method is able to capture the low-power DR sources, even when they are not visible in any of the electrode signals. Results show that the proposed method outperforms conventional beamformers in terms of distinctive localization and detection of invisible DR sources.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Energy & Fuels
Ana L. Martinez-Herrera et al.
Summary: In recent years, there has been great interest in induction motor fault detection among researchers. A new method has been proposed for identifying and classifying faults in induction motors based on the analysis of the startup transient current signal.
Article
Computer Science, Artificial Intelligence
Guoyang Liu et al.
Summary: Automatic seizure detection is important for epilepsy diagnosis and reducing the burden of manual inspection. This study proposes a patient-independent approach using multi-channel EEG recordings, Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) network for effective seizure onset detection, with postprocessing to improve performance. The approach is evaluated on two databases, achieving high accuracy, sensitivity, and AUC-ROC scores.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoshuang Wang et al.
Summary: In this study, a method combining one-dimensional convolutional neural networks and channel selection strategy was proposed for seizure prediction. The method showed high accuracy and sensitivity when evaluated on a real iEEG dataset.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaowei Che et al.
Summary: Color plays a crucial role in object recognition and visual working memory (VWM). Recent studies have demonstrated the possibility of decoding color information from scalp electroencephalogram (EEG) signals during different stages of VWM, and the decoding accuracy during the maintaining stage can predict participants' memory performance.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
De-Cheng Feng et al.
Summary: This paper proposes a two-stage strategy to map manifolds to 2D Euclidean space using Isometric feature mapping, allowing for the generation of random fields over irregular geometries. The stochastic harmonic function method is used to generate random fields, and case studies on stochastic finite element analysis of different structures are performed to demonstrate applicability and efficiency of the proposed method.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Seunghoon Jung et al.
Summary: This study proposes a one-stage model called ADVICE based on 3D CNN for real-time action detection in construction equipment videos, utilizing the 3D attention module and feature pyramid network to enhance performance. The evaluation results demonstrate that the proposed method can efficiently detect actions in diverse construction sites, potentially improving safety, productivity, and environmental management in construction projects.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jiazheng Zhou et al.
Summary: Automatic epilepsy detection is crucial for patient diagnosis and treatment. A proposed EEG classification model based on a self-organizing fuzzy logic (SOF) classifier shows promising results for both cross-patient and patient-specific seizure detection. The model is parameter-free and memory-efficient, suitable for large-scale EEG signals in cross-patient detection.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Camillo Porcaro et al.
Summary: Accurate diagnosis of disorders of consciousness is crucial for tailored treatment programs. Using tools like EEG and a nonlinear method such as Higuchi's Fractal Dimension can improve diagnostic accuracy for distinguishing between MCS and VS groups. HFD has been found to be more sensitive than linear methods and shows promise in reducing misdiagnosis.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chenchen Cheng et al.
Summary: This study developed a novel multiview feature fusion representation (MVFFR) method to detect EEG signals with/without interictal epileptiform spikes (IES). The experimental results showed that MVFFR achieved the optimal detection performance compared with other feature ranking methods, and the MVFFR-related methods were complementary and indispensable. Additionally, MVFFR maintained excellent generalization capacity in an independent test.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Mengfan Li et al.
Summary: This study proposes a 3D-CNN-based model for decoding EEG, which addresses the difficulty of traditional models in decoding dynamic EEG features. The results show that the 3D-CNN achieves higher accuracy and shorter computational cost compared to baseline models.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zuyi Yu et al.
Summary: Epilepsy is a common neurological disease that affects patients' psychological well-being and quality of life. This study proposes an efficient seizure prediction system based on deep learning, which has shown good robustness and generalization ability in both intracranial and scalp EEG signals.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Energy & Fuels
Masoud Ahmadipour et al.
Summary: In this paper, an intelligent method for fault detection and classification for a microgrid (MG) was proposed. The method combines signal processing, parameter optimization, and machine learning tools to achieve accurate and robust protection. Experimental results showed that the proposed method outperformed other protection models in terms of accuracy, speed, and robustness.
Article
S. Magesh et al.
Measurement Sensors
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad H. Rafiei et al.
Summary: This article introduces the application and advantages of self-supervised learning in EEG studies, and proposes future research directions and implementation tips.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
John Thomas et al.
Summary: This study proposes a diagnostic system for epilepsy based on multiple modalities extracted from EEG, achieving high accuracy in cross-validation results. The system, consisting of components like Convolutional Neural Network and spectral feature classifier, shows potential in aiding clinicians to diagnose epilepsy efficiently.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Alison O'Shea et al.
Summary: EEG interpretation in preterm infants poses challenges due to limited experts and differences in EEG morphology compared to term infants. Developing specific seizure detection algorithms for preterm infants is difficult due to limited annotated data. Novel DL architectures show promising results for accurately predicting seizures in preterm infants with minimal annotated data.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yu Xue et al.
Summary: This paper proposes an ensemble of five particle swarm optimization strategies to design the structure of ESN and reduce manual intervention. Experimental results show that the proposed method outperforms traditional evolutionary methods on time series and electrocardiogram datasets.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yanna Zhao et al.
Summary: A novel seizure detection method based on graph attention network (GAT) is proposed to effectively utilize the positional relationships between different EEG signals and address the data imbalance issue. Experimental results show that the proposed method achieves high accuracy, sensitivity, and specificity.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Prasanth Thangavel et al.
Summary: This study evaluated features for reliable IED detection and EEG classification by building an IED detector and automatic classification system based on convolutional neural network. Different ConvNet architectures and types were explored, with the best IED detection achieved by 1D ConvNet with preprocessed full-frequency EEG signal. The EEG classification system showed good performance with a mean balanced accuracy of 78.1% and AUC of 0.839, indicating potential for reducing human effort in epilepsy diagnosis.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Clinical Neurology
Hidir Selcuk Nogay et al.
Summary: The study utilized deep learning and transfer learning methods to detect epileptic seizures using EEG signals, achieving 100% accuracy without requiring additional feature extraction steps. This automatic identification and classification model can aid in early diagnosis of epilepsy, providing effective early treatment opportunities.
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IIUM ENGINEERING JOURNAL
(2020)
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Juan P. Amezquita-Sanchez et al.
JOURNAL OF MEDICAL SYSTEMS
(2018)
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Lucia Billeci et al.
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Denis Keuton Alves et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2017)
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Computer Science, Artificial Intelligence
Mohammad Hossein Rafiei et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2017)
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Rocio A. Lizarraga-Morales et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2017)
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Juan P. Amezquita-Sanchez et al.
DIGITAL SIGNAL PROCESSING
(2015)
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JOURNAL OF MEDICAL SYSTEMS
(2015)
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Siuly Siuly et al.
NEURAL COMPUTING & APPLICATIONS
(2015)
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Xueying Zhang et al.
INTELLIGENT DATA ANALYSIS
(2014)
Review
Clinical Neurology
Paulo A. Lotufo et al.
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Mehran Ahmadlou et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2012)
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Mehran Ahmadlou et al.
ALZHEIMER DISEASE & ASSOCIATED DISORDERS
(2011)
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J. Rafiee et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2011)
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S. Tigaran et al.
ACTA NEUROLOGICA SCANDINAVICA
(2010)
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H. ADELI et al.
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(2010)
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(2010)
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XM Jiang et al.
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(2004)
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H Adeli et al.
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