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Combining temporal and spatial attention for seizure prediction
Purpose:Approximately 1% of the world population is currently suffering from epilepsy. Successful seizure prediction is necessary for those patients....
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Efficient frameworks for statistical seizure detection and prediction
This paper presents two efficient frameworks for seizure detection and prediction that depend on statistical analysis. The common thread between them...
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Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
Epilepsy affects more than 50 million people worldwide, making it one of the world’s most prevalent neurological diseases. The main symptom of...
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Epilepsy seizure prediction with few-shot learning method
Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural...
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Domain Incremental Learning for EEG-Based Seizure Prediction
When building seizure prediction systems, the typical research scenario is patient-specific. In this scenario, the model is limited to performing...
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Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network
The Internet of Things (IoT) is an indispensable part of the healthcare system since it creates a link between the doctor and the patient for remote...
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A Hybrid Model for Epileptic Seizure Prediction Using EEG Data
More than 65 million people’s quality of life is affected by a neurological brain condition epilepsy. When a seizure can be anticipated, therapeutic...
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A Mutual Information-Based Many-Objective Optimization Method for EEG Channel Selection in the Epileptic Seizure Prediction Task
Epileptic seizure prediction using multi-channel electroencephalogram (EEG) signals is very important in clinical therapy. A large number of channels...
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EEG-based seizure prediction with machine learning
Epilepsy is a well-recognized neurological illness which affects millions of people worldwide. This illness has long been considered important in...
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Epileptic Seizure Prediction Using Bandpass Filtering and Convolutional Neural Network
The paper proposes a generalized approach for epileptic seizure prediction rather than a patient-specific approach. The early diagnosis of seizures...
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Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder
Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of...
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Efficient Seizure Prediction from Images of EEG Signals Using Convolutional Neural Network
Epileptic seizures are abnormal electrical activities in the brains of epilepsy patients. Seizure causes life threats due to sudden unconsciousness....
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Transfer Learning Based Seizure Detection: A Review
Seizure detection automatically recognizes Electroencephalogram (EEG) signals in epileptic seizure states through machine learning, time-frequency...
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Epileptic Seizure Prediction Using Geometrical Features Extracted from HRV Signal
The prediction of epileptic seizures in patients can help prevent many unwanted risks and excessive suffering. In this research, electrocardiography...
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Hybrid approach for the detection of epileptic seizure using electroencephalography input
In the early days, it was difficult to study bio-electric signals, but now a days these problems have been solved by many hardware devices which are...
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Epileptic seizure detection using scalogram-based hybrid CNN model on EEG signals
Epilepsy is one of the most usual neurological diseases characterized by abnormal brain activity, resulting in seizures or strange behavior,...
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A novel multivariate approach for the detection of epileptic seizure using BCS-WELM
This paper proposes a novel weighted extreme learning machine (WELM) classifier using binary cuckoo search (BCS) optimization algorithm for a fast...
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Optimizing epileptic seizure recognition performance with feature scaling and dropout layers
Epilepsy is a widespread neurological disorder characterized by recurring seizures that have a significant impact on individuals' lives. Accurately...
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eSeiz 2.0: An Optimized Pulse Exclusion Mechanism for Accurate and Energy-Efficient Seizure Detection in the IoMT
Approximately, 50 million people worldwide are impacted by epilepsy, necessitating the development of a seizure detection system that is low power,...
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Epileptic seizure detection using posterior probability-based convolutional neural network classifier
Epilepsy is the most common neurological disorders affecting 70 million people worldwide. Nowadays, the advanced Epileptic Seizure (ES) detection...
