ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

Epilepsy is a persistent neurological condition of the brain in which the activity of the brain goes out of normal state. Classification and Analysis of EEG signal is the early approach for epilepsy diagnosis. During this paper, we have a tendency to propose an EEG signal classification approach based on Support Vector Machine (SVM) classifier. In extracting features from the raw EEG data we applied the Hilbert Transform method and used its coefficients. Then, after the PCA dimension reduction a two-class SVM classifier is used for EEG signals automatic classification, one class for healthy subjects and another for subjects with epilepsy. In SVM classifier we need to divide the EEG signals into a training dataset and testing dataset for classification. We have used five sets of EEG signals which are publicly accessible on EEG time series database. The evaluation and comparison of SVM based classifier with two other classification methods such as KNN and LVQ based classifier was done. The average classification accuracy of our study is 95.33%.

Performance analysis of SAR filtering techniques using SVM and Wishart Classifier

Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.

Currently, pain assessment using electroencephalogram signals and machine learning methods in clinical studies is of great importance, especially for those who cannot express their pain. Since newborns are among the high-risk group and always experience pain at the beginning of birth, in this research, the severity of newborns has been investigated and evaluated. Other studies related to the annoyance of newborns have used the EEG signal of newborns alone; therefore, in this study, the intensity of newborn pain was measured using the electroencephalogram signal of 107 infants who were stimulated by the heel lance in three levels: no pain, low pain and moderate pain were recorded as a single trial and evaluated. The support vector machine (SVM), K-Nearest Neighbors (KNN) and Ensemble bagging classifiers were trained using the K-fold cross-validation method and features of the brain’s time-frequency domain. The results were obtained with accuracies of 72.8 ± 2, 84.4 ± 1.3 and 82.9 ± 1.6%, respectively. Also, in examining the problem of distinguishing pain and no pain, the electroencephalogram signal of 74 infants was evaluated, and similar to the three-class mode, with the 10-fold validation method, we reached the highest accuracy of 100% in Bagging classifier and 98.6 ± 0.1 accuracy in KNN and SVM classifiers.

The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recording the EEG signals, the activities of the neurons are recorded from various scalp regions which has varied characteristics and has a very low magnitude. Therefore, human interpretation of such signals is very difficult and consumes a lot of time. Hence, with the advent of Computer Aided Diagnosis (CAD) Techniques, identifying the normal versus alcoholic EEG signals has been of great utility in the medical field. In this work, we perform the initial clustering of the alcoholic EEG signals by means of using Correlation Dimension (CD) for easy feature extraction and then the suitable features are selected in it by means of employing various distance metrics like correlation distance, city block distance, cosine distance and chebyshev distance. Proceeding in such a methodology aids and assures that a good discrimination could be achieved between normal and alcoholic EEG signals using non-linear features. Finally, classification is then carried out with the suitable classifiers chosen such as Adaboost.RT classifier, the proposed Modified Adaboost.RT classifier by means of introducing Ridge and Lasso based soft thresholding technique, Random Forest with bootstrap resampling technique, Artificial Neural Networks (ANN) such as Radial Basis Functions (RBF) and Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) with Linear, Polynomial and RBF Kernel, Naïve Bayesian Classifier (NBC), K-means classifier, and K Nearest Neighbor (KNN) Classifier and the results are analyzed. Results report a comparatively high classification accuracy of about 98.99% when correlation distance metrics are utilized with CD and the proposed Modified Adaboost.RT classifier using Ridge based soft thresholding technique.

Timely identification of sugarcane cultivation is essential for precision agriculture, policy formulation, and sustainable land management. Our study presents a multimodal framework that integrates spectral, morphological, and textural features from Sentinel-2 imagery with hybrid deep learning techniques for sugarcane mapping. A comprehensive feature space combining NDVI, geometrical descriptors, and GLCM-based texture metrics was used to train Random Forest (RF) and Support Vector Machine (SVM) classifiers. A hybrid convolutional neural network (CNN+MLP) architecture further merged visual embeddings from image patches with tabular descriptors. The RF model achieved a macro F1-score of 0.91, a Kappa coefficient of 0.86, and a macro-AUC of 0.94 across agricultural regions in Brazil. DBSCAN clustering was applied to filter noisy labels and improve the quality of the training dataset. A domain-specific confusion cost matrix illustrated the economic impact of misclassification, showing that the RF and CNN+MLP models improved accuracy and financial efficiency by reducing crop identification errors. Although the analysis was limited to São Paulo and Mato Grosso do Sul, the framework showed consistent and robust performance within these regions. Overall, it highlights the benefits of combining morphospectral, textural, and deep features for crop classification, providing a scalable foundation for time-series modeling and real-time land-use analytics. • Sugarcane regions were accurately mapped using Sentinel-2 satellite imagery across different agricultural zones in Brazil. • A hybrid CNN+MLP model combined visual embeddings and tabular features to improve classification. • Morphological, spectral (NDVI), and GLCM-based texture features were fused to improve crop discrimination. • Random Forest and Support Vector Machine classifiers were benchmarked and compared with the deep learning approach. • Unsupervised DBSCAN clustering was applied to detect and remove noisy samples, improving training set quality.

This paper presents a preliminary exploration showing the surprising effect of extreme parameter values used by Support Vector Machine (SVM) classifiers for identifying objects in images. The Radial Basis Function (RBF) kernel used with SVM classifiers is considered to be a state-of-the-art approach in visual object classification. Standard tuning approaches apply a relative narrow window of values when determining the main parameters for kernel size. We evaluated the effect of setting an extremely small kernel size and discovered that, contrary to expectations, in the context of visual object classification for some object and feature combinations these small kernels can demonstrate good classification performance. The evaluation is based on experiments on the TRECVid 2013 Semantic INdexing (SIN) training dataset and provides initial indications that can be used to better understand the optimisation of RBF kernel parameters.

This paper deals with feature selection for electroencephalogram (EEG) signal classification. The wavelet packet transform (WPT) technique is used to decompose EEG signals. Then, features are computed and extracted from different wavelet packet coefficients for each frequency sub-band. Then, principal components analysis (PCA) is used for feature dimension reduction. The features are used as inputs for support vector machine (SVM) classifier. The SVM classify EEG signals into two categories: Epileptic or Healthy. To select the best feature basis, a feature ranking method based on SVM recursive feature elimination technique (SVM-RFE) is used. Best results are obtained for a combination of three features, namely: range, maximum and energy values of wavelet coefficients in the sub-bands. The classifier was tested against data obtained from three different datasets. Data length is approximately 120 hours. Obtained results were compared to those obtained in state of the art papers. The classification accuracy reached 99.91% for the first dataset (around 1 hour 20 minutes data) and reached 100% for the second dataset (around 20 hours data) and reached 99.78% for the third dataset (around 98 hours data). Those results are among the best classification results reported so far.

Depression is a mental disorder that affects emotional and physical state of a person. It is a state of extreme sadness and dejection. The electroencephalographic (EEG) signals can be used to detect the alterations in the brain’s electrochemical potential. The highly irregular and complex EEG signal variations can be determined by different processing tools. The present work is based on the automated classification of the normal and depression EEG signals. The discrete cosine transform (DCT) decomposes the normal and depression EEG signals into different frequency sub-bands. Nonlinear methods such as sample entropy, correlation dimension, fractal dimension, largest Lyapunov exponent, Hurst exponent and detrended fluctuation analysis are applied to the DCT coefficients and the extracted characteristic features are ranked using t-value. These significant features are fed to decision tree (DT), support vector machine (SVM), k-nearest neighbor (kNN) and naive Bayes (NB) classifiers. Five significant features are selected and the SVM classifier with radial basis function (RBF) results in a classification accuracy of 93.8%, sensitivity of 92% and specificity of 95.8%.

Alzheimer’s disease (AD) is a neurological disorder that causes memory decline and loss of cognitiveabilities. AD directly impacts the brain activity of affected individuals, which can be reflected inelectroencephalogram (EEG) signals. Previous studies have typically relied on statistical, spectral, and waveletfeatures in order detect AD using EEG signals. Recurrence Quantification Analysis (RQA) is a non-lineartechnique that has been successfully used to analyze EEG signals in several other domains including emotionrecognition and autism detection. However, RQA features have not yet been fully investigated for AD diagnosis.The aim of this work is to thoroughly investigate the usefulness of RQA features for AD diagnosis. Fifteen RQAfeatures were computed along with statistical measures, Hjorth parameters, and relative power in order to comparethe performance of the RQA features to other commonly utilized EEG features. All features were computed fromthe different brain regions. Experimental results indicated that RQA features outperformed all other feature groupsregardless of the considered brain region. RQA features achieved accuracies ranging from 89.6% to 98.2% using asupport machine vector (SVM) classifier with leave-one-subject-out (LOSO) cross-validation. These results arebetween 25% to 40% higher than the three other considered feature groups. Feature ranking was performed to findthe most relevant RQA features, identifying seven of the fifteen computed RQA features. This work sheds light onthe potential of RQA features for reliable AD diagnosis and paves the way for their integration in computer-aidedAD diagnostic tools using EEG signals.

The electroencephalogram (EEG) signal is an electrical representation of brain's working that reflects various physiological and pathological activities such as alcoholism. Alcohol can affect whole parts of the body but, it particularly affects the brain, heart, liver, and the immune system; its effects on the brain are called brain disorders. Nowadays, automatic identification of alcoholic subjects based on EEG signals has become one of the challenging problems in biomedical research. In this study, an automatic classification method for classifying alcoholic and normal EEG signals, based on empirical mode decomposition (EMD), is proposed. The uniqueness of EMD method is to decompose non‐stationary and non‐linear signals into a set of stationary intrinsic mode functions (IMFs) that are band limited signals. These IMFs are transformed into analytic representations by applying the Hilbert transform. From these analytic IMFs, various features namely mean, kurtosis, skewness, entropy, and negentropy are extracted; these features are used as input to least squares support vector machines (LS‐SVMs) classifier with radial basis function (RBF) kernel and polynomial kernel. The accuracy results achieved for LS‐SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92%, respectively, which are found to be better as compared with other state‐of‐the‐art methods.

EEG-based human recognition is increasingly becoming a popular modality for biometric authentication. Two important features of EEG signals are liveliness and the robustness against falsification. However, a comprehensive study on human authentication using EEG signal is still remains. On the other hand, low-cost wireless EEG recording devices are now growing in the market places. Although these devices have the potential to many applications, researches have yet to be done to find the feasibility of these devices. In this study, we propose a method for human identification using EEG signals obtained from such low-cost devices. EEG signal is first preprocessed to remove noise and artifacts using Bandpass FIR filter. These signals are then divided into disjoint segments. Three feature extraction methods, namely multiscale shape description (MSD), multiscale wavelet packet statistics (WPS) and multiscale wavelet packet energy statistics (WPES) are then applied. These features are finally used to train a supervised error-correcting output code multiclass model (ECOC) using support vector machine (SVM) classifier, which ultimately can recognize humans from test EEG signals. A preliminary experiment with 9 EEG records from 9 subjects shows the true positive rate of 94.44% of the proposed method.

Parkinson's disease (PD) is a neurological disorder which is progressive in nature. Although there is no cure to this disease, symptomatic treatments are available. These treatments can slow the progressive development of the symptoms. Medications can treat some of the symptoms of the PD up to a great extent that in turn may help the patients to live a normal life. Besides these medications, the patients can also be provided with various therapies based on the types of their symptoms. But for providing any treatment, detection of its symptoms at an early stage is very important. This can reduce its future complexities considerably. Early diagnosis along with proper medications may treat the symptoms of PD significantly. This motivates to propose a new and effective methodology for detection and analysis of PD. In this work, an approach has been proposed for identification of PD patients by using Electroencephalogram (EEG) signals. Here, the EEG signals of normal persons and PD patients are processed in three stages. First, the raw EEG signals are pre-processed for removal of noises and artefacts present. Out of various techniques, Wavelet transform is used for this purpose. In the MATLAB environment, de-noising can be executed by using the in-built functions. Performances of the de-noising techniques are examined with the performance parameters namely Root Mean Square Error (RMSE) as well as Signal to Noise Ratio (SNR). In the second stage, statistical features are extracted from the pre-processed EEG signals. In this work, five statistical features are considered for performing the classification. In the final stage, the extracted features are classified using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. ANN is an efficient classifier that predicts the human brain's working manners. SVM on the other hand has been proven as one of the most prevailing classification algorithms that gives highly accurate and robust results. This is a novel approach of analyzing the performances of the classification techniques by evaluating the best performing feature. In both the classifiers accuracy, precision, sensitivity and specificity are calculated from the confusion matrix evaluated from the values of the statistical features. In ANN, results using six different training algorithms at different hidden layers are calculated and compared. This proves the training algorithm Levenberg-Marquardt back-propagation with hidden layer 20 as the best combination for performing the classification. From the results it is seen that both ANN and SVM classifiers provide significant classification accuracies of 94.7% and 96.5% respectively. Amongst the five considered features, Mean performs the best in terms of classification accuracy.