Fully data-driven convolutional filters with deep learning models for epileptic spike detection
K Fukumori, HTT Nguyen, N Yoshida… - ICASSP 2019-2019 …, 2019 - ieeexplore.ieee.org
Epilepsy is a chronic disorder that causes unprovoked, recurrent-seizures. Characteristic
spikes are often observed in the electroencephalogram (EEG) of epileptic patients in order …
spikes are often observed in the electroencephalogram (EEG) of epileptic patients in order …
Epileptic seizure detection using multi-channel EEG wavelet power spectra and 1-D convolutional neural networks
RV Sharan, S Berkovsky - … of the IEEE Engineering in Medicine …, 2020 - ieeexplore.ieee.org
The use of feature extraction and selection from EEG signals has shown to be useful in the
detection of epileptic seizure segments. However, these traditional methods have more …
detection of epileptic seizure segments. However, these traditional methods have more …
Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks
Advances in deep learning methods present new opportunities for fixing complex problems
for an end to end learning. In terms of optimal design, seizure detection from EEG data has …
for an end to end learning. In terms of optimal design, seizure detection from EEG data has …
Eeg-based seizure detection using variable-frequency complex demodulation and convolutional neural networks
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable
seizures that affect millions of people around the world. Early and accurate epilepsy …
seizures that affect millions of people around the world. Early and accurate epilepsy …
Seizure detection using least EEG channels by deep convolutional neural network
This work aims to develop an end-to-end solution for seizure onset detection. We design the
SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with …
SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with …
A deep learning-based method for automatic detection of epileptic seizure in a dataset with both generalized and focal seizure types
Epilepsy is the second most popular neurological disorder affecting 65 million people
around the world. Seizures are classified into two kinds; focal and generalized ictal activities …
around the world. Seizures are classified into two kinds; focal and generalized ictal activities …
Epileptic seizure detection: A deep learning approach
Epilepsy is the second most common brain disorder after migraine. Automatic detection of
epileptic seizures can considerably improve the patients' quality of life. Current …
epileptic seizures can considerably improve the patients' quality of life. Current …
A comparison of deep neural networks for seizure detection in EEG signals
This paper aims to apply machine learning techniques to an automated epileptic seizure
detection using EEG signals to help neurologists in a time-consuming diagnostic process …
detection using EEG signals to help neurologists in a time-consuming diagnostic process …
A deep learning scheme for automatic seizure detection from long-term scalp EEG
Epilepsy is a chronic brain disorder that is expressed by seizures. Monitoring brain activity
via electroencephalogram (EEG) is an established method for epilepsy diagnosis and for …
via electroencephalogram (EEG) is an established method for epilepsy diagnosis and for …
Epileptiform spike detection via convolutional neural networks
AR Johansen, J Jin, T Maszczyk… - … , Speech and Signal …, 2016 - ieeexplore.ieee.org
The EEG of epileptic patients often contains sharp waveforms called" spikes", occurring
between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we …
between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we …