scholarly journals AUTOMATIC DETECTION OF EPILEPSY EEG USING NEURAL NETWORKS

2012 ◽  
pp. 241-245
Author(s):  
SATYANARAYANA VOLLALA ◽  
KARNAKAR GULLA
Keyword(s):  
Neural Networks ◽  
Detection System ◽  
Statistical Parameter ◽  
Approximate Entropy ◽  
Epileptic Activity ◽  
Diagnostic Systems ◽  
Physiological Signal ◽  
Eeg Data ◽  
Eeg Recordings ◽  
First Time

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy has emerged in recent years.This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system.Two different types of neural networks, namely, Elman and probabilistic neural networks are considered. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.

Correlated EEG Signals Simulation Based on Artificial Neural Networks

2017 ◽  
Vol 27 (05) ◽  
pp. 1750008 ◽  
Author(s):  
Nikola M. Tomasevic ◽  
Aleksandar M. Neskovic ◽  
Natasa J. Neskovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Characteristics ◽  
Measurement Data ◽  
Eeg Signals ◽  
Mental Load ◽  
Novel Approach ◽  
Eeg Data ◽  
Artificial Neural ◽  
Eeg Recordings

In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

An Efficient Hybrid Self-Learning Intrusion Detection System Based on Neural Networks

2019 ◽  
Vol 18 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Fatemeh Amiri
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Imperialist Competitive Algorithm ◽  
Hybrid Architecture ◽  
Self Organizing Map ◽  
First Time ◽  
Self Learning

An intrusion detection system (IDS) is an immunizing system that identifies the hostile activities in a network, and alerts the network administrator in case of detecting suspicious behaviors. Signature-based systems are the most common methods for intrusion detection, but however, they are not able to detect new attacks on the network. The main problem of these systems is to keep up to date the database of already containing known attack signatures. Neural networks have a high ability to learn and are generalizable. This study present as follow: A new intrusion detection system that is a hybrid of self-organizing map algorithm (SOM), radial basis function (RBF) and perceptron networks is proposed to solve this problem. For the first time, The Imperialist Competitive Algorithm is used to calculate the parameters of the Perceptron neural network. The proposed approach uses a hybrid architecture that tries to increase the quality of warnings. Signature-based systems using this method can detect new attacks as a self-learner. The results indicated better performance of the proposed hybrid algorithm compared to earlier methods.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

Raman-Spektren von SnJ4, GeJ4, TiBr4 und TiJ4 in flüssiger und kristalliner Phase / Raman Spectra of SnJ4, GeJ4, TiBr4, and TiJ4 in the Liquid and Crystalline Phase

1970 ◽  
Vol 25 (12) ◽  
pp. 1374-1381 ◽  
Author(s):  
W. Kiefer ◽  
H. W. Schrötter
Keyword(s):  
Raman Spectra ◽  
Ruby Laser ◽  
Detection System ◽  
Point Group ◽  
Visible Region ◽  
Group Symmetry ◽  
Pure Liquid ◽  
Electronic Detection ◽  
Polarization Studies ◽  
First Time

The Raman spectra of four molecules absorbing in the visible region (SnJ4, GeJ4, TiBr4, and TiJ4) are presented. They were excited with a quasi-continuous ruby laser and recorded with a special electronic detection system. Except for TiJ4, complete Raman spectra of crystal powder pellets could be obtained for the first time. The assignment reported by previous authors was confirmed by accurate polarization studies of solutions or pure liquid. The assignment is also in the solid state possible on the basis of Td point group symmetry. The fundamental vibrations of TiJ4 in solutions are: ν1 (A1) =162, ν2 (E) =51, ν3 (F2) =319 and ν4 (F2) Y = 67 cm-1

A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

2006 ◽  
Vol 45 (06) ◽  
pp. 610-621 ◽  
Author(s):  
A. T. Tzallas ◽  
P. S. Karvelis ◽  
C. D. Katsis ◽  
S. Giannopoulos ◽  
S. Konitsiotis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Epileptic Activity ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Epilepsy Diagnosis ◽  
Transient Events ◽  
Artificial Neural ◽  
Eeg Recordings

Summary Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Intelligent garbage detection system based on neural networks

2021 ◽  
Author(s):  
Can Zhang ◽  
Xu Zhang ◽  
Dawei Tu ◽  
Ying Wang
Keyword(s):  
Neural Networks ◽  
Detection System
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