Introduction: Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error. To address these limitations, researchers have turned to machine learning and deep learning techniques to automate the seizure detection process.
Methods: In this work, we propose a novel method for epileptic seizure detection, leveraging the power of 1-D Convolutional layers in combination with Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Average pooling Layer as a single unit. This unit is repeatedly used in the proposed model to extract the features. The features are then passed to the Dense layers to predict the class of the EEG waveform. The performance of the proposed model is verified on the Bonn dataset. To assess the robustness and generalizability of our proposed architecture, we employ five-fold cross-validation. By dividing the dataset into five subsets and iteratively training and testing the model on different combinations of these subsets, we obtain robust performance measures, including accuracy, sensitivity, and specificity.
Results: Our proposed model achieves an accuracy of 99-100% for binary classifications into seizure and normal waveforms, 97.2%-99.2% accuracy for classifications into normal-interictal-seizure waveforms, 96.2%-98.4% accuracy for four class classification and accuracy of 95.81%-98% for five class classification.
Discussion: Our proposed models have achieved significant improvements in the performance metrics for the binary classifications and multiclass classifications. We demonstrate the effectiveness of the proposed architecture in accurately detecting epileptic seizures from EEG signals by using EEG signals of varying lengths. The results indicate its potential as a reliable and efficient tool for automated seizure detection, paving the way for improved diagnosis and management of epilepsy.
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http://dx.doi.org/10.3389/fncom.2024.1340251 | DOI Listing |
Front Hum Neurosci
December 2024
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.
Introduction: As brain-computer interfacing (BCI) systems transition fromassistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy.
View Article and Find Full Text PDFJ Pain Res
January 2025
Department of Rehabilitation Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Purpose: Pain is a multidimensional, unpleasant emotional and sensory experience, and accurately assessing its intensity is crucial for effective management. However, individuals with cognitive impairments or language deficits may struggle to accurately report their pain. EEG provides insight into the neurological aspects of pain, while facial EMG captures the sensory and peripheral muscle responses.
View Article and Find Full Text PDFNeurocrit Care
January 2025
Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
Background: Neurovascular coupling (NVC) refers to the process of aligning cerebral blood flow with neuronal metabolic demand. This study explores the potential of contralateral NVC-linking neural electrical activity on the stroke side with cerebral blood flow velocity (CBFV) on the contralesional side-as a marker of physiological function of the brain. Our aim was to examine the association between contralateral NVC and neurological outcomes in patients with ischemic stroke following endovascular thrombectomy.
View Article and Find Full Text PDFPsychophysiology
January 2025
Consiglio Nazionale delle Ricerche, Istituto di Neuroscienze, Parma, Italy.
Attention-deficit hyperactivity disorder (ADHD) is a neurobiological condition that affects both children and adults. Microstate (MS) analyses, a data-driven approach that identifies stable patterns in EEG signals, offer valuable insights into the neurophysiological characteristics of ADHD. This review summarizes findings from 13 studies that applied MS analyses to resting-state and task-based brain activity in individuals with ADHD.
View Article and Find Full Text PDFRev Sci Instrum
January 2025
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.
Emotion recognition based on electroencephalogram (EEG) has always been a research hotspot. However, due to significant individual variations in EEG signals, cross-subject emotion recognition based on EEG remains a challenging issue to address. In this article, we propose a dynamic domain-adaptive EEG emotion recognition method based on multi-source selection.
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