scholarly journals Artificial Neural Network analysis of EEG waves in complex partial seizure patients

2021 ◽  
Vol 18 (1) ◽  
pp. 15-21
Author(s):  
Shikha Saxena ◽  
Kamal Kant Gupta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temporal Lobe ◽  
Partial Seizure ◽  
Test Performance ◽  
Complex Partial Seizure ◽  
Parametric Representation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Automated Algorithms

Background: Brain dynamics associated with epilepsy remains limited. EEG-based epilepsy diagnosis and seizure detection is still in its infancy. The problem is further amplified for the design and development of automated algorithms, which requires a quantitative parametric representation of the qualitative or visual aspect of the markers. This study proposes an automatic classification system for epilepsy based on neural networks and EEG signals. Material and Method: The present study made use of EEG data from 16 controls and 16 temporal lobe epilepsy (TLE) patients in order to comparatively assess neural dynamics in normal healthy young adults and epileptic patients treated with anti-epileptic drugs in the context of resting state during eye close session. Such tangible differences could be appreciated through artificial neural network (ANN) classifiers. Results: During eye closed session of EEG in order to diagnose temporal lobe epileptic patient, the extracted features of EEG activity are given to the classifier algorithm for training and test performance. Artificial Neural Network (ANN) classifier was used for the diagnosis task. Fractal dimension (Katz, Higuchi and Permission entropy) were analyzed, in which the best results was observed in trained set of data of Katz (93.18%).   Conclusion:  Non-linear analysis plays an important role in prediction of complex partial seizure during interictal period.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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