An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Flexible Dry Electrode Based on a Wrinkled Surface That Uses Carbon Nanotube/Polymer Composites for Recording Electroencephalograms

Electroencephalography (EEG) captures minute electrical signals emanating from the brain. These signals are vulnerable to interference from external noise and dynamic artifacts; hence, accurately recording such signals is challenging. Although dry electrodes are convenient, their signals are of limited quality; consequently, wet electrodes are predominantly used in EEG. Therefore, developing dry electrodes for accurately and stably recording EEG signals is crucial. In this study, we developed flexible dry electrodes using polydimethylsiloxane (PDMS)/carbon-nanotube (CNT) composites with isotropically wrinkled surfaces that effectively combine the advantages of wet and dry electrodes. Adjusting the PDMS crosslinker ratio led to good adhesion, resulting in a highly adhesive CNT/PDMS composite with a low Young's modulus that exhibited excellent electrical and mechanical properties owing to its ability to conformally contact skin. The isotropically wrinkled surface also effectively controls dynamic artifacts during EEG signal detection and ensures accurate signal analysis. The results of this study demonstrate that dry electrodes based on flexible CNT/PDMS composites and corrugated structures can outperform wet electrodes. The introduction of such electrodes is expected to enable the accurate analysis and monitoring of EEG signals in various scenarios, including clinical trials.

Efficient novel network and index for alcoholism detection from EEGs

Background

Alcoholism is a catastrophic condition that causes brain damage as well as neurological, social, and behavioral difficulties.

Limitations

This illness is often assessed using the Cut down, Annoyed, Guilty, and Eye-opener examination technique, which assesses the intensity of an alcohol problem. This technique is protracted, arduous, error-prone, and errant.

Method

As a result, the intention of this paper is to design a cutting-edge system for automatically identifying alcoholism utilizing electroencephalography (EEG) signals, that can alleviate these problems and aid practitioners and investigators. First, we investigate the feasibility of using the Fast Walsh-Hadamard transform of EEG signals to explore the unpredictable essence and variability of EEG indicators in the suggested framework. Second, thirty-six linear and nonlinear features for deciphering the dynamic pattern of healthy and alcoholic EEG signals are discovered. Subsequently, we suggested a strategy for selecting powerful features. Finally, nineteen machine learning algorithms and five neural network classifiers are used to assess the overall performance of selected attributes.

Results

The extensive experiments show that the suggested method provides the best classification efficiency, with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the features chosen using the correlation-based FS approach with Recurrent Neural Networks. With recently introduced matrix determinant features, a classification accuracy of 93.3% is also attained. Moreover, we developed a novel index that uses clinically meaningful features to differentiate between healthy and alcoholic categories with a unique integer. This index can assist health care workers, commercial companies, and design engineers in developing a real-time system with 100% classification results for the computerized framework.

EEG Classification of Normal and Alcoholic by Deep Learning

Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol's EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG's features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms.

An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis

Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism.

Hybrid classification model for eye state detection using electroencephalogram signals

The electroencephalography (EEG) signal is an essential source of Brain-Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5-40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN-RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN-RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN-RNN classification procedure. The results revealed that the proposed CNN-RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

Classification of alcoholic EEG signals using wavelet scattering transform-based features

Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks. With data from a publicly-available UCI database, we investigated the ability of WST-based features extracted from multichannel electroencephalogram (EEG) signals to discriminate 1.0-s EEG records of 20 male subjects with alcoholism and 20 male healthy subjects. Using record-wise 10-fold cross-validation, we found that WST-based features, inputted to a support vector machine (SVM) classifier, were able to correctly classify all alcoholic and normal EEG records. Similar performances were achieved with 1D CNN. In contrast, the highest independent-subject-wise mean 10-fold cross-validation performance was achieved with WST-based features fed to a linear discriminant (LDA) classifier. The results achieved with two 10-fold cross-validation approaches suggest that the WST together with a conventional classifier is an alternative to CNN for classification of alcoholic and normal EEGs. WST-based features from occipital and parietal regions were the most informative at discriminating between alcoholic and normal EEG records.

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques

Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values ​​obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.

Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient's sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.

Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain

A widespread brain disorder of present days is depression which influences 264 million of the world's population. Depression may cause diverse undesirable consequences, including poor physical health, suicide, and self-harm if left untreated. Depression may have adverse effects on the personal, social, and professional lives of individuals. Both neurologists and researchers are trying to detect depression by challenging brain signals of Electroencephalogram (EEG) with chaotic and non-stationary characteristics. It is essential to detect early-stage depression to help patients obtain the best treatment promptly to prevent harmful consequences. In this paper, we proposed a new method based on centered correntropy (CC) and empirical wavelet transform (EWT) for the classification of normal and depressed EEG signals. The EEG signals are decomposed to rhythms by EWT and then CC of rhythms is computed as the discrimination feature and fed to K-nearest neighbor and support vector machine (SVM) classifiers. The proposed method was evaluated using EEG signals recorded from 22 depression and 22 normal subjects. We achieved 98.76%, 98.47%, and 99.05% average classification accuracy (ACC), sensitivity, and specificity in a 10-fold cross-validation strategy by using an SVM classifier. Such efficient results conclude that the method proposed can be used as a fast and accurate computer-aided detection system for the diagnosis of patients with depression in clinics and hospitals.

Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms

Surgery is recommended for epilepsy diagnosis in cases where patients do not respond well to anti-epilepsy medications. Successful surgery is essentially dependent on the area suffered from epilepsy, i.e., focal area. Electroencephalogram (EEG) signals are considered a powerful tool to identify focal or non-focal (normal) areas. In this work, we propose an automated method for focal and non-focal EEG signal identification, taking into account non-linear features derived from rhythms in the empirical wavelet transform (EWT) domain. The research paradigm is related to the decomposition of EEG signals into the delta, theta, alpha, beta, and gamma rhythms through the development of the EWT. Specifically, various non-linear features are extracted from rhythms composed of Stein's unbiased risk estimation entropy, threshold entropy, centered correntropy, and information potential. From a statistical point of view, Kruskal-Wallis (KW) statistical test is then used to identify the significant features. The significant features obtained from the KW test are fed to support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. The SURE entropy provides an average classification accuracy of 93% and 82.6% for small and entire datasets by utilizing SVM and KNN classifiers with a tenfold cross-validation method, respectively. It is observed that the proposed method is better and competitive in comparison with other studies for small and large data, respectively. The obtained outcome concludes that the proposed framework could be used for people with epilepsy and can help the physicians to validate the assessment.

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

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.

A novel approach for automated alcoholism detection using Fourier decomposition method

BACKGROUND

The identification of alcoholism is of prime importance because of its adverse effects on the central nervous system. Moreover, people suffering from alcoholism are susceptible to various health problems such as cardiomyopathy, immune system disorder, high blood pressure, cirrhosis, brain anomalies, and heart problems.

NEW METHOD

This study presents a novel approach, based on Fourier theory, known as Fourier decomposition method (FDM) for automatic identification of alcoholism using electroencephalogram (EEG) signals. The FDM approach is employed to decompose EEG signals into a set of desired orthogonal components, commonly referred as Fourier intrinsic band functions (FIBFs), obtained by dividing the complete bandwidth of EEG signal under analysis into equal frequency bands. Time-domain features such as Hjorth parameters, kurtosis, inter-quartile range, and median frequency are extracted from FIBFs. To reduce the complexity, Kruskal-Wallis (KW) statistical test, is performed to adopt the most significant features.

RESULTS

Simulation results are obtained using different classification methods, namely, k-nearest neighbor (kNN), support vector machine (SVM), and linear discriminant analysis (LDA). The proposed approach with the SVM classifier using radial basis function provides average accuracy of 99.98%, sensitivity of 99.99% and specificity of 99.97%. Performance is also tested in the presence of noise.

COMPARISON WITH EXISTING METHOD(S)

Classification results highlight the superior performance of our method in comparison to existing works.

CONCLUSIONS

The proposed scheme provides an efficient approach and can be employed in real-time alcoholism detection.

Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.

Estimation of Parkinson's disease severity using speech features and extreme gradient boosting

In recent years, there is an increasing interest in building e-health systems. The systems built to deliver the health services with the use of internet and communication technologies aim to reduce the costs arising from outpatient visits of patients. Some of the related recent studies propose machine learning-based telediagnosis and telemonitoring systems for Parkinson's disease (PD). Motivated from the studies showing the potential of speech disorders in PD telemonitoring systems, in this study, we aim to estimate the severity of PD from voice recordings of the patients using motor Unified Parkinson's Disease Rating Scale (UPDRS) as the evaluation metric. For this purpose, we apply various speech processing algorithms to the voice signals of the patients and then use these features as input to a two-stage estimation model. The first step is to apply a wrapper-based feature selection algorithm, called Boruta, and select the most informative speech features. The second step is to feed the selected set of features to a decision tree-based boosting algorithm, extreme gradient boosting, which has been recently applied successfully in many machine learning tasks due to its generalization ability and speed. The feature selection analysis showed that the vibration pattern of the vocal fold is an important indicator of PD severity. Besides, we also investigate the effectiveness of using age and years passed since diagnosis as covariates together with speech features. The lowest mean absolute error with 3.87 was obtained by combining these covariates and speech features with prediction level fusion. Graphical Abstract Framework for the proposed UPDRS estimation model.

Profiling of pornography addiction among children using EEG signals: A systematic literature review

Nowadays human behavior has been affected with the advent of new digital technologies. Due to the rampant use of the Internet by children, many have been addicted to pornography. This addiction has negatively affected the behaviors of children including increased impulsiveness, learning ability to attention, poor decision-making, memory problems, and deficit in emotion regulation. The children with porn addiction can be identified by parents and medical practitioners as third-party observers. This systematic literature review (SLR) is conducted to increase the understanding of porn addiction using electroencephalogram (EEG) signals. We have searched five different databases namely IEEE, ACM, Science Direct, Springer and National Center for Biotechnology Information (NCBI) using addiction, porn, and EEG as keywords along with 'OR 'operation in between the expressions. We have selected 46 studies in this work by screening 815,554 papers from five databases. Our results show that it is possible to identify children with porn addiction using EEG signals.

Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks

Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.

Multiclass covert speech classification using extreme learning machine

The objective of the proposed research is to classify electroencephalography (EEG) data of covert speech words. Six subjects were asked to perform covert speech tasks i.e mental repetition of four different words i.e 'left', 'right', 'up' and 'down'. Fifty trials for each word recorded for every subject. Kernel-based Extreme Learning Machine (kernel ELM) was used for multiclass and binary classification of EEG signals of covert speech words. We achieved a maximum multiclass and binary classification accuracy of (49.77%) and (85.57%) respectively. The kernel ELM achieves significantly higher accuracy compared to some of the most commonly used classification algorithms in Brain-Computer Interfaces (BCIs). Our findings suggested that covert speech EEG signals could be successfully classified using kernel ELM. This research involving the classification of covert speech words potentially leading to real-time silent speech BCI research.

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject's influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject's influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

Comparative analysis of different characteristics of automatic sleep stages

BACKGROUND AND OBJECTIVE

With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status.

METHODS

This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA.

RESULTS

By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging.

CONCLUSION

In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.

A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification

BACKGROUND

Electroencephalogram (EEG) signals are important for brain health monitoring applications. Characteristics of EEG signals are complex, being non-stationarity, aperiodic and nonlinear in nature. EEG signals are a combination of sustained oscillation and non-oscillation transients that are challenging to deal with using linear approaches.

METHOD

This research proposes a new scheme based on a tunable Q-factor wavelet transform (TQWT) and a statistical approach to analyse various EEG recordings. Firstly, the proposed method decompose EEG signals into different sub-bands using the TQWT method, which is parameterized by its Q-factor and redundancy. This method depends on the resonance of a signal, instead of frequency or scaling as in the Fourier and wavelet transforms. Secondly, using a statistical feature extraction on the sub-bands to divide each sub-band into n windows, and then extract several statistical features from each window. Finally, the extracted features are forwarded to a bagging tree (BT), k nearest neighbor (k-NN), and support vector machine (SVM) as classifiers to evaluate the performance of the proposed feature extraction technique.

RESULTS

The proposed method is tested on two different EEG databases: Bonn University database and Born University database. The experimental results demonstrate that the proposed feature extraction algorithm with thek-NN classifier produces the best performance compared with the other two classifiers. Comparison with existing methods: In order to further evaluate the performances, the proposed scheme is compared with the other existing methods in terms of accuracy. The results prove that the proposed TQWT based feature extraction method has great potential to extract discriminative information from brain signals.

CONCLUSION

The outcomes of the proposed technique can assist doctors and other health experts to identify diversified EEG categories.

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.

CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING FEATURES DERIVED FROM FOURIER-BASED RHYTHMS

We propose a new technique for the automated classification of focal and nonfocal electroencephalogram (EEG) signals using Fourier-based rhythms in this paper. The EEG rhythms, namely, delta, theta, alpha, beta and gamma, are obtained using the discrete Fourier transform (DFT)-based filter bank applied on EEG signals. The mean-frequency (MF) and root-mean-square (RMS) bandwidth features are derived using DFT-based computation on rhythms of EEG signals and their envelopes. These derived features, namely, MF and RMS bandwidths have been provided as an input feature set for the classification of focal and nonfocal EEG signals using a least-squares support vector machine (LS-SVM) classifier. We present experimental results obtained from the publicly available database in order to demonstrate the effectiveness of the proposed feature sets for the automated classification of the focal and nonfocal classes of EEG signals. The obtained classification accuracy in this dataset for the automated classification of focal and nonfocal 50 pairs and 750 pairs of EEG signals are 89.7% and 89.52%, respectively.

Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems

Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.