E. Juarez-Guerra, V. Alarcon-Aquino, and P. Gomez-Gil, "Epilepsy seizure detection in EEG signals using wavelet transforms and neural networks," in New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering, ed: Springer, 2015, pp. 261-269....
SeizureIctalInterictal1D-CNNEpilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. ...
This field has been moving forward by assuming the existence of a preictal period that Electroencephalogram (EEG) signals can capture3. The preictal is considered a transitional period that precedes the seizure. Beyond this period, the EEG signal can be divided into three more stages: ictal (th...
Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier Torikul Islam , Redwanul Islam & Sk. Rahat Ali Article 20 April 2024 | Open Access Stability of transcranial magnetic stimulation electroencephalogram evoked potentials in pediatric epilepsy...
The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, ...
A variety of algorithms of different biometric signals can do this even prior to clinical onset of a seizure (Table 1). All seizure detection algorithms involve two main steps. First, appropriate quantitative values or features, such as EEG features, movements, or other biomarkers, must be ...
The automation of epilepsy detection using signal processing techniques such as wavelet transform and entropies may optimise the performance of the system. Many algorithms have been developed to diagnose the presence of seizure in the EEG signals. The entropy is a nonlinear parameter that reflects ...
Epilepsy can be defined as a disorder of the central nervous system that results in recurrent seizures due to chronic abnormal bursts of electrical discharge in the brain. This paper reviews the different methods proposed by the researchers for detection of Epileptic activity using EEG....
A framework is proposed to evaluate the EEG signal classification algorithms for seizure detection. It is used to assess the performance of the algorithms in classifying EEG signals into two, three, and five separate groups. Three stages are involved in the proposed detection framework. The first ...
EEG signals vary among various patients owing to the variation identified in seizure location and type, where the prediction methods are more patient-centric. Some prediction methods adopt a supervised learning model for feature learning and classification process among two stages (preictal and inter...