scholarly journals Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2499 ◽  
Author(s):  
Yue Gu ◽  
Zhenhu Liang ◽  
Satoshi Hagihira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Activity ◽  
Permutation Entropy ◽  
Depth Of Anesthesia ◽  
Spectral Edge Frequency ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( p < 0.001 ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.

scholarly journals Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)

2019 ◽  
Vol 6 (2) ◽  
pp. 46 ◽  
Author(s):  
Yar Muhammad ◽  
Daniil Vaino
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Brain Activity ◽  
Electronic Devices ◽  
Testing Dataset ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg–Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg–Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset.

scholarly journals Artificial Neural Network Model Adopting Combinatorial Inhibition Process in Multiple Solution Problems

2018 ◽  
Vol 7 (3.4) ◽  
pp. 167
Author(s):  
K Shyamala ◽  
P Chanthini ◽  
R Krishnan ◽  
A Murugan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Problem Solving ◽  
Research Work ◽  
Neural Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sequential Firing ◽  
The Brain ◽  
Preliminary Phase

Exploration of Artificial Neural Network (ANN) research continually opens rooms for improvement and implementation of mathematical models to solve various problems. This research work was not only to direct on the objective of problem-solving, instead the goal is to mimic basic biological functions of the brain in problem-solving situations. The basic biological theories of “Selection”, “Combination” and “Inhibition” were successfully implemented in the earlier works. This work conceived another biological theory named “Sequential Firing” of neuron in solving complex problems like sum-of-subset problems. The non-modular combinatorial inhibition neural model has been proposed and implemented successfully using the time delayed sequential firing between neurons. As per the biological theories knowledge representation is a preliminary phase of learning. This work not only illustrates the sequential process of firing between neurons, it paves the way to utilize this neural model for the learning process.  

Definition of Artificial Neural Network

2014 ◽  
pp. 44-57
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Unit ◽  
Processing Elements ◽  
Processing Information ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Definition Of ◽  
External Stimuli ◽  
The Brain

In living creatures, the brain is the control unit and it can be divided in different anatomic and functional sub-units. An artificial neural network is a computational system for processing information as a response to external stimuli, which consists of a set of highly interconnected processing elements called neurons. It is very useful to have some knowledge of the way the biological nervous system is organized, since the artificial neural network is an inspiration of the biological neural networks. This chapter is an explanation of the Artificial Neural Network (ANN). The biological and mathematical definition of a neural network is provided and the activation functions effective for processing are listed. Some figures are collected for better understanding.

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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