Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

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.

Instance-based learning techniques use a set of stored training instances to classify new examples. The most common such learning technique is the nearest neighbor method, in which new instances are classified according to the closest training instance. A critical element of any such method is the metric used to determine distance between instances. Euclidean distance is by far the most commonly used metric; no one, however, has systematically considered whether a different metric, such as Manhattan distance, might perform equally well on naturally occurring data sets. Some evidence from psychological research indicates that Manhattan distance might be preferable in some circumstances. This paper examines three different distance metrics and presents experimental comparisons using data from three domains: malignant cancer classification, heart disease diagnosis, and diabetes prediction. The results of these studies indicate that the Manhattan distance metric works works quite well, although not better than the Euclidean metric that has become a standard for machine learning experiments. Because the nearest neighbor technique provides a good benchmark for comparisons with other learning algorithms, the results below include a number of such comparisons, which show that nearest neighbor, using any distance metric, compares quite well to other machine learning techniques.

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%.

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.

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.

Alcoholic EEG signals recognition based on phase space dynamic and geometrical features

Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

The machine learning algorithms can be applied in many classification problems including the clinical studies. In the stroke emotion analysis, the machine learning is used to analyze the emotion of stroke patients and normal people. The K-Nearest Neighbor (KNN) classifier relies on the distance metric to calculate the nearest class for classification. The aim of this study is to compare the performances of different distance metrics apply on the classification of emotional electroencephalogram (EEG) between stroke and normal people. The Detrended Fluctuation Analysis (DFA) feature was extracted from the EEG signal of both classes and KNN was applied with different distance metrics for comparison. The results showed that the City Block distance metric performs the best among all. Moreover, the lower values of nearest neighbor were required to provide high classification result. The results from this study showed that the performance of the KNN classification was affected by the distance metric used.

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.

This paper studied the epilepsy signal in different frequency bands based on the average phase-synchronization time matrix (APSTM). APSTM algorithm can probe the change in the synchrony of the underlying system with high sensitivity, and be applied to multichannel, nonlinear time series in noisy. This paper uses the EEG filter provided by the EEGLAB to extract the four common waves : theta, alpha, beta and gamma, which are from the normal and epilepsy EEG signals. The four frequency bands of EEG signals are analysed based on the APSTM algorithm, and the results show that the APSTM determinant can distinguish the epilepsy from the normal EEG signals clearly. And when APSTM determinant detects epileptic discharge ,the theta wave is the most significant, and the beta wave can only generally detect epileptic discharge, while alpha and gamma waves are ineffective.

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances (Euclidean, standardized Euclidean, Mahalanobis, city block, cosine and correlation distances) combining four commonly used hierarchical cluster methods (single distance, complete distance, unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling (LDSS) method for constructing different core subsets. The analyses of variance (ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean, standardized Euclidean, Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean, Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages, which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

An alternative SMOTE oversampling strategy for high-dimensional datasets

The upland mesic rainforests of eastern Australia have been described as a "mesothermal archipelago" where a chain of cool mountain "islands" arise from a warm "sea" of tropical and subtropical lowlands. An endemic freshwater crayfish belonging to the genus Euastacus is found on each of these mountain "islands." The Euastacus are particularly suitable for the study of evolution because each mountain harbors a unique species, there are many taxa present providing replication within the group and, most importantly, their distribution is linear, extending along a south-north axis. This group could have evolved by "simultaneous vicariance" where there was one vicariant separation event of a widespread ancestor, or by "south to north stepping stone dispersal" where there were long distance dispersal events from neighboring mountain islands, starting in the south and proceeding north in a dispersal-colonization wave. We used pairwise genetic distances between nearest geographic neighbors as a novel way to test the two hypotheses. If diversification was due to "south to north stepping stone dispersal," then pairwise genetic distances between nearest geographic neighbors should decrease progressively the farther north the taxon pairs are found, reflecting the decreasing periods of isolation. In this case there should be a negative correlation between the south to north rank order of nearest neighbors and pairwise genetic distances. A Spearman's correlation on 16S mtDNA pairwise genetic distances and geographic rank order was not significant, indicating there was no support for the south to north stepping stone dispersal hypothesis. If simultaneous vicariance was responsible for diversification then all nearest geographic neighbor taxon pairs should have similar genetic distances and, therefore, the variance in nearest neighbor distances should be zero, or close to it. To test if the observed variance was tending towards zero we developed a randomization test where nearest neighbor taxon pairs were assigned random genetic distances and the variances calculated. The observed variance lay in the < 0.05 range of the simulated variances, providing support for the simultaneous vicariance hypothesis. The data also suggest there was simultaneous vicariance of at least two ancestral Queensland lineages. The timing of this vicariant event was probably in the Pliocene, which is consistent with the divergence times reported for other Australian mesic rainforest restricted taxa.

The upland mesic rainforests of eastern Australia have been described as a “mesothermal archipelago” where a chain of cool mountain “islands” arise from a warm “sea” of tropical and subtropical lowlands. An endemic freshwater crayfish belonging to the genus Euastacus is found on each of these mountain “islands.” The Euastacus are particularly suitable for the study of evolution because each mountain harbors a unique species, there are many taxa present providing replication within the group and, most importantly, their distribution is linear, extending along a south-north axis. This group could have evolved by “simultaneous vicariance” where there was one vicariant separation event of a widespread ancestor, or by “south to north stepping stone dispersal” where there were long distance dispersal events from neighboring mountain islands, starting in the south and proceeding north in a dispersal-colonization wave. We used pairwise genetic distances between nearest geographic neighbors as a novel way to test the two hypotheses. If diversification was due to “south to north stepping stone dispersal,” then pairwise genetic distances between nearest geographic neighbors should decrease progressively the farther north the taxon pairs are found, reflecting the decreasing periods of isolation. In this case there should be a negative correlation between the south to north rank order of nearest neighbors and pairwise genetic distances. A Spearman's correlation on 16S mtDNA pairwise genetic distances and geographic rank order was not significant, indicating there was no support for the south to north stepping stone dispersal hypothesis. If simultaneous vicariance was responsible for diversification then all nearest geographic neighbor taxon pairs should have similar genetic distances and, therefore, the variance in nearest neighbor distances should be zero, or close to it. To test if the observed variance was tending towards zero we developed a randomization test where nearest neighbor taxon pairs were assigned random genetic distances and the variances calculated. The observed variance lay in the < 0.05 range of the simulated variances, providing support for the simultaneous vicariance hypothesis. The data also suggest there was simultaneous vicariance of at least two ancestral Queensland lineages. The timing of this vicariant event was probably in the Pliocene, which is consistent with the divergence times reported for other Australian mesic rainforest restricted taxa.

Shows that systems built on a simple statistical technique and a large training database can be automatically optimized to produce classification accuracies of 99% in the domain of handwritten digits. It is also shown that the performance of these systems scale consistently with the size of the training database, where the error rate is cut by more than half for every tenfold increase in the size of the training set from 10 to 100,000 examples. Three distance metrics for the standard nearest neighbor classification system are investigated: a simple Hamming distance metric, a pixel distance metric, and a metric based on the extraction of penstroke features. Systems employing these metrics were trained and tested on a standard, publicly available, database of nearly 225,000 digits provided by the National Institute of Standards and Technology. Additionally, a confidence metric is both introduced by the authors and also discovered and optimized by the system. The new confidence measure proves to be superior to the commonly used nearest neighbor distance. >