Discrimination of scalp EEG signals in wavelet transform domain and channel selection for the patient-invariant seizure detection

2015 ◽  
Author(s):  
Anindya Bijoy Das ◽  
Md. Jubaer Hossain Pantho ◽  
Mohammed Imamul Hassan Bhuiyan
Keyword(s):  
Wavelet Transform ◽  
Seizure Detection ◽  
Channel Selection ◽  
Transform Domain ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Selection For

scholarly journals Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Qi ◽  
Yueming Wang ◽  
Jianmin Zhang ◽  
Junming Zhu ◽  
Xiaoxiang Zheng
Keyword(s):  
Seizure Detection ◽  
Simultaneous Optimization ◽  
Eeg Signals ◽  
Deep Network ◽  
Scalp Eeg ◽  
Impulsive Noises ◽  
Eeg Data ◽  
The Mean ◽  
Maximum Correntropy Criterion

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

scholarly journals Automatic seizure detection based on imaged-EEG signals through fully convolutional networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Catalina Gómez ◽  
Pablo Arbeláez ◽  
Miguel Navarrete ◽  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
Seizure Detection ◽  
False Alarms ◽  
Eeg Signals ◽  
Convolutional Networks ◽  
Scalp Eeg ◽  
Number Of False Alarms ◽  
Fully Convolutional Networks ◽  
Manual Intervention ◽  
Average Accuracy

AbstractSeizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.

scholarly journals A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals

2019 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Krisnachai Chomtho ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Networks ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Diagnostic Process ◽  
Eeg Signals ◽  
Time Frequency ◽  
Scalp Eeg ◽  
Learning Techniques ◽  
Frequency Domains ◽  
2D Filter

AbstractThis 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. We employ two approaches based on convolution neural networks (CNNs) and artificial neural networks (ANNs) to provide a probability of seizure occurrence in a windowed EEG recording of 18 channels. In order to extract relevant features based on time, frequency, and time-frequency domains for these networks, we consider an improvement of the Bayesian error rate from a baseline. Features of which the improvement rates are higher than the significant level are considered. These dominant features extracted from all EEG channels are concatenated as the input for ANN with 7 hidden layers, while the input of CNN is taken as raw multi-channel EEG signals. Using multi-concept of deep CNN in image processing, we exploit 2D-filter decomposition to handle the signal in spatial and temporal domains. Our experiments based on CHB-MIT Scalp EEG Database showed that both ANN and CNN were able to perform with the overall accuracy of up to 99.07% and F1-score of up to 77.04%. ANN with dominant features is more capable of detecting seizure events than CNN whereas CNN requiring no feature extraction is slightly better than ANN in classification accuracy.

Sign in / Sign up

Export Citation Format

Share Document