An Evaluation of the Channel Effect on Detecting the Preictal Stage in Patients With Epilepsy

2020 ◽  
pp. 155005942096643
Author(s):  
Erhan Bergil ◽  
Mehmet Recep Bozkurt ◽  
Canan Oral
Keyword(s):  
Principal Component ◽  
Epileptic Seizures ◽  
Training Data ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Testing Data ◽  
Patients With Epilepsy ◽  
Electrode Selection ◽  
And Training ◽  
Selection Of

Decreasing the processor load to an acceptable level challenges researchers as an important threshold in the study of real-time detection and the prediction of epileptic seizures. The main methods in overcoming this problem are feature selection, dimension reduction, and electrode selection. This study is an evaluation of the performances of EEG signals, obtained from different channels in the detection processes of epileptic stages, in epileptic individuals. In particular, it aimed to analyze the separation levels of preictal periods from other periods and to evaluate the effects of the electrode selection on seizure prediction studies. The EEG signals belong to 14 epileptic patients. A feature set was formed for each patient using 20 features widely used in epilepsy studies. The number of features was decreased to 8 using principal component analysis. The reduced feature set was divided into testing and training data, using the cross-validation method. The testing data were classified with linear discriminant analysis and the results of the classification were evaluated individually for each patient and channel. Variability of up to 29.48 % was observed in the average of classification accuracy due to the selection of channels.

Selection of optimal external filter for colorimetric camera

2021 ◽  
Vol 2021 (29) ◽  
pp. 141-147
Author(s):  
Michael J. Vrhel ◽  
H. Joel Trussell
Keyword(s):  
Figure Of Merit ◽  
Band System ◽  
Image Data ◽  
Optimal Filter ◽  
Training Data ◽  
Noise Model ◽  
Data Set ◽  
Testing Data ◽  
And Training ◽  
Selection Of

A database of realizable filters is created and searched to obtain the best filter that, when placed in front of an existing camera, results in improved colorimetric capabilities for the system. The image data with the external filter is combined with image data without the filter to provide a six-band system. The colorimetric accuracy of the system is quantified using simulations that include a realistic signal-dependent noise model. Using a training data set, we selected the optimal filter based on four criteria: Vora Value, Figure of Merit, training average ΔE, and training maximum ΔE. Each selected filter was used on testing data. The filters chosen using the training ΔE criteria consistently outperformed the theoretical criteria.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Classification Method of Hand Gestures Based on Support Vector Machine

2018 ◽  
Vol 7 (3) ◽  
Author(s):  
Wahyu Caesarendra
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Time Domain ◽  
Principal Component ◽  
Training Data ◽  
Signal Classification ◽  
Support Vector ◽  
Hand Gestures ◽  
Emg Signal ◽  
Testing Data

This paper presents the EMG signal classification based on PCA and SVM method. The data is acquired from the 5 subjects and each subject perform 7 hand gestures includes the tripod, power, precision closed, finger point, mouse, hand open, and hand close. Each gesture is repeated 10 times (5 data as training data and the 5 remaining data as testing data). Each of training and testing data are processed using 16 features extraction in time–domain and reduced using principal component analysis (PCA) to obtain new set of features. Features classification using support vector machine classify new set of features from each subject result 85% - 89% percentage of training classification. Training data classification is tested using testing data of EMG signals and giving accuracy reach 80% - 86%.

MONOTONOUS TASKS AND ALCOHOL CONSUMPTION EFFECTS ON THE BRAIN BY EEG ANALYSIS USING NEURAL NETWORKS

2012 ◽  
Vol 11 (03) ◽  
pp. 1250015 ◽  
Author(s):  
STEPHEN KARUNGARU ◽  
TOSHIHIRO YOSHIDA ◽  
TORU SEO ◽  
MINORU FUKUMI ◽  
KENJI TERADA
Keyword(s):  
Neural Networks ◽  
Mental Stress ◽  
Brain Activity ◽  
Principal Component ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Eeg Signal Analysis

An analysis of the Electroencephalogram (EEG) signals while performing a monotonous task and drinking alcohol using principal component analysis (PCA), linear discriminant analysis (LDA) for feature extraction and Neural Networks (NNs) for classification is proposed. The EEG is captured while performing a monotonous task that can adversely affect the brain and possibly cause stress. Moreover, we investigate the effects of alcohol on the brain by capturing the data continuously after consumption of equal amounts of alcohol. We hope that our work will shed more light on the relationship between such actions and EEG, and investigate if there is any relation between the tasks and mental stress. EEG signals offers a rare look at brain activity, while, monotonous activities are well known to cause irritation which may contribute to mental stress. We apply PCA and LDA to characterize the change in each component, extract it and discriminate using a NN. After experiments, it was found that PCA and LDA are effective analysis methods in EEG signal analysis.

scholarly journals Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Roozbeh Zarei ◽  
Jing He ◽  
Siuly Siuly ◽  
Guangyan Huang ◽  
Yanchun Zhang
Keyword(s):  
Principal Component ◽  
Epileptic Seizures ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Eeg Signals ◽  
Decision Tree Classifier ◽  
Efficient Detection ◽  
Eeg Data ◽  
Tree Classifier ◽  
Representative Samples

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k-nearest neighbour algorithm (k-NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy (OCA). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.

scholarly journals Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs

2019 ◽  
Vol 6 (1) ◽  
pp. 116-124 ◽  
Author(s):  
Anna Adi Perbawawati ◽  
Endang Sugiharti ◽  
Much Aziz Muslim
Keyword(s):  
Expert System ◽  
Computer Science ◽  
Medical Record ◽  
Bayes Theorem ◽  
Training Data ◽  
Drug Selection ◽  
Forward Chaining ◽  
Testing Data ◽  
Human Thinking ◽  
Selection Of

The development of technology capable to imitating the process of human thinking  and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs.  Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy

scholarly journals Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Bhojraj Thapa
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Epileptic Seizure ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.

scholarly journals Machine learning and feature engineering for predicting pulse presence during chest compressions

2021 ◽  
Vol 8 (11) ◽  
Author(s):  
Diya Sashidhar ◽  
Heemun Kwok ◽  
Jason Coult ◽  
Jennifer Blackwood ◽  
Peter J. Kudenchuk ◽  
...  
Keyword(s):  
Patient Outcomes ◽  
Principal Component ◽  
Training Data ◽  
Feature Engineering ◽  
Chest Compressions ◽  
Linear Discriminant ◽  
Discriminant Model ◽  
Audio Recordings ◽  
Hospital Cardiac Arrest ◽  
Pulse Check

Current resuscitation protocols require pausing chest compressions during cardiopulmonary resuscitation (CPR) to check for a pulse. However, pausing CPR when a patient is pulseless can worsen patient outcomes. Our objective was to design and evaluate an ECG-based algorithm that predicts pulse presence with or without CPR. We evaluated 383 patients being treated for out-of-hospital cardiac arrest with real-time ECG, impedance and audio recordings. Paired ECG segments having an organized rhythm immediately preceding a pulse check (during CPR) and during the pulse check (without CPR) were extracted. Patients were randomly divided into 60% training and 40% test groups. From training data, we developed an algorithm to predict the clinical pulse presence based on the wavelet transform of the bandpass-filtered ECG. Principal component analysis was used to reduce dimensionality, and we then trained a linear discriminant model using three principal component modes as input features. Overall, 38% (351/912) of checks had a spontaneous pulse. AUCs for predicting pulse presence with and without CPR on test data were 0.84 (95% CI (0.80, 0.88)) and 0.89 (95% CI (0.86, 0.92)), respectively. This ECG-based algorithm demonstrates potential to improve resuscitation by predicting the presence of a spontaneous pulse without pausing CPR with moderate accuracy.

scholarly journals Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1481 ◽  
Author(s):  
Samed Jukic ◽  
Muzafer Saracevic ◽  
Abdulhamit Subasi ◽  
Jasmin Kevric
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Epileptic Focus ◽  
Eeg Signals ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

Utility of Projection Network for Diagnosis of High Pressure Air Compressor Faults

Manufacturing ◽  
2003 ◽  
Author(s):  
C. Jansuwan ◽  
C. James Li
Keyword(s):  
High Pressure ◽  
Training Data ◽  
Training Methods ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Air Compressor ◽  
Testing Data ◽  
And Training ◽  
Projection Network

The utility of a dynamic neural network, i.e., projection network, was established to diagnose the condition of a 4-stage high pressure air compressor. Network structure and parameter initialization and training methods were developed. Using measurements of the compressor’s four stages’ discharge temperatures and pressures collected under different baseline conditons, 3rd stage suction and exhaust value faults, and an unanticipated 3rd stage cylinder pittings as training data, a 99+% of correct classification rate was accomplished with testing data.

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