AUTOMATED DIAGNOSIS OF DIABETES USING ENTROPIES AND DIABETIC INDEX

2016 ◽  
Vol 16 (01) ◽  
pp. 1640008 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
HAMIDO FUJITA ◽  
SHREYA BHAT ◽  
JOEL EW KOH ◽  
MUHAMMAD ADAM ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Characteristic Curve ◽  
Gaussian Mixture ◽  
The Body ◽  
Rank Test ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Bhattacharyya Distance ◽  
Noninvasive Technique ◽  
Distance Information

Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240028 ◽  
Author(s):  
EE PING NG ◽  
TEIK-CHENG LIM ◽  
SUBHAGATA CHATTOPADHYAY ◽  
MURALIDHAR BAIRY
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Recurrence Quantification Analysis ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

2011 ◽  
Vol 21 (03) ◽  
pp. 199-211 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
SUBHAGATA CHATTOPADHYAY ◽  
WENWEI YU ◽  
PENG CHUAN ALVIN ANG
Keyword(s):  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Recurrence Quantification Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

DEPRESSION DIAGNOSIS SUPPORT SYSTEM BASED ON EEG SIGNAL ENTROPIES

2014 ◽  
Vol 14 (03) ◽  
pp. 1450035 ◽  
Author(s):  
OLIVER FAUST ◽  
PENG CHUAN ALVIN ANG ◽  
SUBHA D. PUTHANKATTIL ◽  
PAUL K. JOSEPH
Keyword(s):  
Support System ◽  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Approximate Entropy ◽  
Gaussian Mixture ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Non Linear

Electroencephalography (EEG) is a measure which represents the functional activity of the brain. We show that a detailed analysis of EEG measurements provides highly discriminant features which indicate the mental state of patients with clinical depression. Our feature extraction method revolves around a novel processing structure that combines wavelet packet decomposition (WPD) and non-linear algorithms. WPD was used to select appropriate EEG frequency bands. The resulting signals were processed with the non-linear measures of approximate entropy (ApEn), sample entropy (SampEn), renyi entropy (REN) and bispectral phase entropy ( P h). The features were selected using t-test and only discriminative features were fed to various classifiers, namely probabilistic neural network (PNN), support vector machine (SVM), decision tree (DT), k-nearest neighbor algorithm (k-NN), naive bayes classification (NBC), Gaussian mixture model (GMM) and Fuzzy Sugeno Classifier (FSC). Our classification results show that, with a classification accuracy of 99.5%, the PNN classifier performed better than the rest of classifiers in discriminating between normal and depression EEG signals. Hence, the proposed decision support system can be used to diagnose, and monitor the treatment of patients suffering from depression.

APPLICATION OF NON-LINEAR AND WAVELET BASED FEATURES FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

2012 ◽  
Vol 22 (02) ◽  
pp. 1250002 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
PENG CHUAN ALVIN ANG ◽  
RATNA YANTI ◽  
JASJIT S. SURI
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Automatic Monitoring ◽  
Gaussian Mixture ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Fuzzy Classifier ◽  
Linear Features ◽  
Non Linear

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

COMPUTER-BASED IDENTIFICATION OF NORMAL AND ALCOHOLIC EEG SIGNALS USING WAVELET PACKETS AND ENERGY MEASURES

2013 ◽  
Vol 13 (03) ◽  
pp. 1350033 ◽  
Author(s):  
OLIVER FAUST ◽  
WENWEI YU ◽  
NAHRIZUL ADIB KADRI
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Test Method ◽  
Identification System ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Computer Based

This paper describes a computer-based identification system of normal and alcoholic Electroencephalography (EEG) signals. The identification system was constructed from feature extraction and classification algorithms. The feature extraction was based on wavelet packet decomposition (WPD) and energy measures. Feature fitness was established through the statistical t-test method. The extracted features were used as training and test data for a competitive 10-fold cross-validated analysis of six classification algorithms. This analysis showed that, with an accuracy of 95.8%, the k-nearest neighbor (k-NN) algorithm outperforms naïve Bayes classification (NBC), fuzzy Sugeno classifier (FSC), probabilistic neural network (PNN), Gaussian mixture model (GMM), and decision tree (DT). The 10-fold stratified cross-validation instilled reliability in the result, therefore we are confident when we state that EEG signals can be used to automate both diagnosis and treatment monitoring of alcoholic patients. Such an automatization can lead to cost reduction by relieving medical experts from routine and administrative tasks.

A New Algorithm for Analysis of MiRNA Expression Profiles—SVM-RFE-FKNN

2021 ◽  
Author(s):  
Duan Mei ◽  
Qiang Liu
Keyword(s):  
Mirna Expression ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Binary Classification ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Mirna Expression Profiles

Based on MicroRNA (miRNA) expression profiles, this article proposes a new algorithm—SVM-RFE-FKNN, which combines the support vector machine-recursive feature elimination (SVM-RFE) algorithm and the fuzzy K -nearest neighbor (FKNN) algorithm, to realize binary classification of tumors. First, the SVM-RFE algorithm was used to select features from the miRNA expression profile dataset to constitute feature subsets and to determine the maximum number of support vectors. Next, this maximum number was regarded as the upper limit of the parameter K in the FKNN algorithm that was then used to classify the samples to be tested. Finally, the leave-one-out cross-validation method was adopted to assess the classification performance of the proposed algorithm. Through experiments, our proposed algorithm was compared with other twelve classification methods, and the result shows that our algorithm had better classification performance. Specifically, with only a few miRNA biomarkers, the proposed algorithm could reach an accuracy of 99.46% and an area under the receiver operating characteristic curve (AUC) of 0.9874.

scholarly journals A machine-learning approach to predict postprandial hypoglycemia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wonju Seo ◽  
You-Bin Lee ◽  
Seunghyun Lee ◽  
Sang-Man Jin ◽  
Sung-Min Park
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Artificial Pancreas ◽  
Individual Performance ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Support Vector ◽  
K Nearest Neighbor

Abstract Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

scholarly journals Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiaqi Wang ◽  
Yingying Ma ◽  
Xianling Yang ◽  
Teng Li ◽  
Haoxi Wei
Keyword(s):  
Prediction Model ◽  
Short Term Memory ◽  
Gaussian Mixture ◽  
Traffic Prediction ◽  
Collaborative Management ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Temporal Characteristics ◽  
Leibler Divergence

This paper presents a short-term traffic prediction method, which takes the historical data of upstream points and prediction point itself and their spatial-temporal characteristics into consideration. First, the Gaussian mixture model (GMM) based on Kullback–Leibler divergence and Grey relation analysis coefficient calculated by the data in the corresponding period is proposed. It can select upstream points that have a great impact on prediction point to reduce computation and increase accuracy in the next prediction work. Second, the hybrid model constructed by long short-term memory and K-nearest neighbor (LSTM-KNN) algorithm using transformed grey wolf optimization is discussed. Parallel computing is used in this part to reduce complexity. Third, some meaningful experiments are carried out using real data with different upstream points, time steps, and prediction model structures. The results show that GMM can improve the accuracy of the multifactor models, such as the support vector machines, the KNN, and the multi-LSTM. Compared with other conventional models, the TGWO-LSTM-KNN prediction model has better accuracy and stability. Since the proposed method is able to export the prediction dataset of upstream and prediction points simultaneously, it can be applied to collaborative management and also has good potential prospects for application in freeway networks.

AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350009 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
RATNA YANTI ◽  
JIA WEI ZHENG ◽  
M MUTHU RAMA KRISHNAN ◽  
JEN HONG TAN ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Texture Features ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Brain Disorder

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

scholarly journals Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3670 ◽  
Author(s):  
Krzysztof Rzecki ◽  
Tomasz Sośnicki ◽  
Mateusz Baran ◽  
Michał Niedźwiecki ◽  
Małgorzata Król ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Computational Intelligence ◽  
Probabilistic Neural Network ◽  
Spectral Lines ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
K Nearest Neighbor ◽  
Computational Intelligence Methods

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

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