Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT

2018 ◽  
pp. 209-221 ◽  
Author(s):  
Abhijit Bhattacharyya ◽  
Lokesh Singh ◽  
Ram Bilas Pachori
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Scalp Eeg

A novel wavelet-based index to detect epileptic seizures using scalp EEG signals

2008 ◽  
Author(s):  
Ali Shahidi Zandi ◽  
Guy A. Dumont ◽  
Manouchehr Javidan ◽  
Reza Tafreshi ◽  
Bernard A. MacLeod ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Scalp Eeg

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.

Early Detection of Epileptic Seizures Based on Scalp EEG Signals

2019 ◽  
pp. 42-61 ◽  
Author(s):  
Abhishek Agrawal ◽  
Lalit Garg ◽  
Eliazar Elisha Audu ◽  
Ram Bilas Pachori ◽  
Justin H.G. Dauwels
Keyword(s):  
Early Detection ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Scalp Eeg

scholarly journals Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies

Seizure ◽  
2019 ◽  
Vol 71 ◽  
pp. 258-269 ◽  
Author(s):  
Syed Muhammad Usman ◽  
Shehzad Khalid ◽  
Rizwan Akhtar ◽  
Zuner Bortolotto ◽  
Zafar Bashir ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Intracranial Eeg ◽  
Scalp Eeg

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

Enhancing the reliability of epileptic seizure alarms for scalp EEG signals

2015 ◽  
Author(s):  
Muhammad Imran Khalid ◽  
Saeed Abdullah Aldosari ◽  
Saleh A. Alshebeili ◽  
Turky Alotaiby
Keyword(s):  
Epileptic Seizure ◽  
Eeg Signals ◽  
Scalp Eeg

scholarly journals An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed I. Sharaf ◽  
Mohamed Abu El-Soud ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Feature Selection ◽  
Correlation Energy ◽  
Epileptic Seizures ◽  
Compact Set ◽  
Eeg Signal ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Computer Aided ◽  
Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

Sign in / Sign up

Export Citation Format

Share Document