A need for automatic seizure detection system is pressing, as more than 65 million people worldwide suffer from Epileptic Seizures, which need to be treated and better cared for. Medical events, such as Epileptic Seizures, have special features in their Electroencephalography (EEG) that allow researchers to have a lot...
These findings demonstrate that recent advances in ML and DL enable reliable prediction of preictal states using ECG alone and indicate that training models on long-term ECG recordings containing multiple seizures from the same individual yields a more dependable solution for seizure prediction than generalized approac...
Multiclass epileptic seizure detection from Electroencephalogram (EEG) signals is difficult because seizure-related patterns are nonstationary, patient-dependent, and distributed across redundant EEG channels. This study develops a detection framework that combines Coefficient of Variation (CV)-based channel selection,...
A proposed hybrid framework that combines a convolutional neural network (CNN) with a support vector machine (SVM) with a support vector machine (SVM) for ECG signal classification outperformed existing techniques and significantly improved accuracy.
The use of pre-trained models reduced the training time and resources required, and the unique application of the ensemble learning approach produces more robust and reliable results compared to individual deep learning models.
A novel hybrid deep learning framework that integrates one-dimensional Convolutional Neural Networks (1D-CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to simultaneously learn discriminative spatial features and complex temporal dependencies inherent in EEG signals is proposed.
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