Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

Zhong, Lisha; Wan, Jiangzhong; Yi, Fangji; He, Songnian; Wu, Jia; Huang, Zhiwei; Lu, Yi; Yang, Jiazhang; Li, Zhangyong

doi:10.3389/fnins.2023.1174005

Cited by 5 publications

(2 citation statements)

References 41 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous studies have documented the viability of employing EEG features for the identification of mental diseases [ 23 , 35 , 40 ]. To our knowledge, there are three mainstream analytical approaches to EEG feature extractions [ 41 , 42 ]: power spectral density analysis, nonlinear dynamics analysis, and functional connectivity analysis.…”

Section: Methodsmentioning

confidence: 99%

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

Qi,

Xu,

2023

Brain Sciences

View full text Add to dashboard Cite

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.

show abstract

Section: Methodsmentioning

confidence: 99%

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

Qi,

Xu,

2023

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…In the proposed system, EEG signal preprocessing is imperative in order to remove artifacts and noise. Recently, signal processing techniques have enabled the system to automatically identify and remove artifacts in EEG-based seizure prediction systems [ 34 , 35 ]. To model the end-to-end automatic epileptic seizure prediction system, in the proposed prediction approach, several preprocessing procedures are utilized alone, without the requirement of human interference for feature extraction.…”

Section: Proposed Epileptic Seizure Prediction Methodologymentioning

confidence: 99%

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning

Saemaldahr

Ilyas

2023

Sensors

View full text Add to dashboard Cite

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy. The determination of the preictal state is influenced by global and local model-assisted decision making by modeling the two-level edge layer. The Spiking Encoder (SE), integrated with the Graph Convolutional Neural Network (Spiking-GCNN), works as the local model trained using a bi-timescale approach. Each local model utilizes the aggregated seizure knowledge obtained from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. Thus, the proposed approach achieved 96.33% sensitivity and 96.14% specificity when tested on the CHB-MIT EEG dataset when modeling was performed using the bi-timescale approach and Spiking-GCNN-based epileptic pattern learning. Moreover, the adoption of federated learning greatly assists the proposed system, yielding a 96.28% higher accuracy as a result of addressing data scarcity.

show abstract

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction

Zhang,

Zheng,

Chen

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time–frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.

show abstract

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

Cited by 5 publications

References 41 publications

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction

Contact Info

Product

Resources

About