Search

Colon cancer (CC) is a leading cause of mortality rate across worldwide. Early diagnosis of colon cancer prolongs human life and is also helpful in preventing the disease. Histopathological inspection is a frequently used technique to detect and diagnose it. Visual inspection of histopathological analysis needs more inspection time and the decision is based on clinicians' subjective perceptions. Typically, machine learning techniques depend on conventional feature extraction which is time-consuming and laborious, and may not be suitable for a large amount of data. This work proposed a lightweight multi-headed convolutional neural network (MHCNN) for the classification of colon tissue using histopathological images. Authors have employed the first-time Tikhonov-based unsharp masking on colon tissue histopathological images. Tikhonov-based unsharp masking is used to highlight the high-frequency details (such as edges, corners, contours, etc.) of histopathological images. Then, the obtained unsharp mask-based histopathological images are given as input to the proposed MHCNN. Further, quantized aware training is applied to the proposed model to reduce the model size for efficient storage and speed up the training and testing time while maintaining high accuracy. The effectiveness of the developed model is compared with the other existing state-of-the-art methods. The proposed MHCNN achieved an average classification accuracy of 96.62%, precision of 97.48%, specificity of 97.46%, f1 score of 0.9664, and area under the curve of 0.9828. The quantized accuracy of 96.10% is achieved by the proposed network. Clinicians may install the developed network to validate the diagnosis in the hospitals. [ABSTRACT FROM AUTHOR]

Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain–computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) or diabetes is an incurable, chronic, and genetic link health problem that occurs due to the higher glucose level in the blood. Continuous monitoring and automatic screening of diabetic patients will significantly improve the quality of the medical management system. DM induces cardiovascular autonomic neuropathy, which alters the morphology of electrocardiogram (ECG) signals. Hence, in this experiment, a new single-lead ECG signal database of 86 subjects (35 diabetic and 51 normal) is recorded. For the automatic screening of DM, an intrinsic time-scale decomposition (ITD) and machine learning-based framework is developed. In the first stage, denoised recorded signals are segmented into fragments of 5-s and decomposed into rotational components using the ITD algorithm. In the second stage, four features, namely, Hjorth complexity, Shannon entropy, log energy entropy, and log energy, are extracted from the ITD components. In the third stage, the Kruskal–Wallis (K–W) test is applied to select the most distinguishable features and fed to a decision tree classifier (DTC) with three different kernel functions for the automatic detection of diabetic patients. The fine tree (FT) kernel function provides the highest classification accuracy (ACC) of 86.9%. The proposed framework is developed using a 10-fold validation strategy. The developed framework is patient-centered suitable for screening in resource-limited environments and ready to be tested with more databases. [ABSTRACT FROM AUTHOR]

Objective: Bundle Branch Block (BBB) is the most serious asymptomatic, cardiovascular complication caused due to obstruction or delay across the pathways of electrical signals. Vectorcardiography (VCG) is the most useful method for measuring heart activities, containing information about the different physiological states of the heart. It is a powerful tool for detecting blockage inside the lower chamber of the heart. Clinically, posterior sensing electrodes are used to record VCG signals. Manual inspection of BBB-induced VCG changes is a time-consuming, labor-intensive, and error-prone procedure. This is the first work that integrates a deep learning technique with S-T spectrograms of smoothed VCG signals for the accurate diagnosis of BBB. A novel filter-fusion technique has also been proposed, to remove the various artifacts from the signals, which considerably increased the BBB diagnosis performance. Methods: VCG signals are divided into fragments of 4- ${s}$. A newly introduced filter-fusion technique is utilized to remove baseline wander and smooth the signal. Stock-well transform (S-T) is applied to extract spectrograms from de-noised smoothed VCG fragments. Obtained S-T spectrograms are fed to pre-trained Alex-Net, Squeeze-Net, and a newly developed less complex deep learning model with data augmentation technique utilizing a 10-fold cross-validation scheme. Results: Proposed improved deep learning model (IDLM) yielded the highest detection accuracy of 98.80%, true positive rate of 98.74%, and true negative rate of 98.80%. Conclusion: The findings show that developed DLM is computationally fast and efficient than benchmark models. The proposed IDLM is available for testing with other datasets. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is a neurodegenerative ailment which causes changes in the neuronal, behavioral, and physiological structures. During the early stages of PD, these changes are very subtle and hence accurate diagnosis is challenging. Pathological and neurological experts assess the PD patients by examining their drawing, writing, walking, tremor, facial expressions, and speech. The manual analysis performed by specialists is time-consuming and prone to errors. Electroencephalogram (EEG) signals represent changes in the brain activities, but it is difficult to manually analyze these signals due to their non-linear, non-stationary, and complex nature. Traditional machine learning methods require several manual steps, such as decomposition, extraction of features, and classification. To overcome these limitations, automated PD detection using smoothed pseudo-Wigner Ville distribution (SPWVD) coupled with convolutional neural networks (CNN) called Parkinson’s disease CNN (PDCNNet) is proposed. First the EEG signals are subjected to SPWVD to obtain time-frequency representation (TFR). Then these two-dimensional plots are fed to an CNN. The proposed model is developed using two public databases. We have obtained an accuracy of 100% and 99.97% for dataset 1 and 2, respectively in detecting PD automatically using our proposed PDCNNet model. Our developed prototype has outperformed all existing state-of-the-art techniques and ready to be validated with more diverse datasets. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions, hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis of SZ by visual inspection is subjective, difficult, and time-consuming. Electroencephalogram (EEG) signals are widely used to detect the SZ as they reflect the conditions of the brain. Conventional machine learning methods involve many lengthy manual steps, such as decomposition, feature extraction, feature selection, and classification. In this article, automated identification of SZ is proposed using a combination of time–frequency analysis and convolutional neural network (CNN) to overcome the limitations of feature extraction-based methods. Three press button tasks are analyzed to segregate normal subjects and SZ patients. The EEG signals are subjected to continuous wavelet transform, short-time Fourier transform, and smoothed pseudo-Wigner–Ville distribution (SPWVD) techniques to obtain scalogram, spectrogram, and SPWVD-based time–frequency representation (TFR) plots, respectively. These 2-D plots are fed to pretrained AlexNet, VGG16, ResNet50, and CNN. We have obtained an accuracy of 93.36% using the SPWVD-based TFR and CNN model. In comparison to the benchmark AlexNet, ResNet50, and VGG16 networks, the developed CNN model with four convolutional layers not only requires fewer learnable parameters but also is computationally efficient and fast. This clearly indicates that our proposed method combining the SPWVD-CNN model has performed better than the state-of-the-art transfer learning techniques. Our developed model is ready to be tested with more EEG data and can aid psychiatrists in their diagnosis. [ABSTRACT FROM AUTHOR]

Background: Drivers drowsiness is one of the prime reasons for road accidents. Electroencephalogram (EEG) signals provide crucial information regarding drowsy state due to neurological changes in the brain. But the complex nature of EEG signals makes it difficult to study these changes. A detailed analysis of the EEG signal can be done if it is decomposed into multi-modes. Method: In this paper, adaptive variational mode decomposition (AVMD) is used for accurate analysis and synthesis of EEG signals. The number of modes (${J}$) and quadratic penalty factor ($\alpha $) is selected adaptively to find out representative information from EEG signals. Selection of ${J}$ and $\alpha $ is done by minimizing the reconstruction error using the Jaya optimization algorithm. Features are extracted from the adaptively decomposed modes. Entropy-based features selected by statistical analysis are classified with different classification algorithms. Eight performance parameters are evaluated to test the system’s effectiveness. Results: The reconstruction error of $4.035\times 10^{-09}$ and $1.564\times 10^{-09}$ for the alert and drowsy state shows that the proposed method gives a better synthesis of signals. An accuracy, sensitivity, specificity, F-1 score, Kappa, false-positive rate, error, and precision of 97.19%, 97.01%, 97.46%, 0.976, 94.23%, 2.54%, 2.81%, and 98.18% shows that the proposed method provides representative modes for analysis. Conclusion: The comparison shows that AVMD is superior over conventional and existing methods by about 7% and 1%, respectively. The solution provided in this paper takes a step ahead for efficient synthesis and analysis of EEG signals to detect the drowsy state of drivers. [ABSTRACT FROM AUTHOR]

Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain–computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its nonstationary nature. Fixing a factual predefined basis function for efficient decomposition using a tunable $Q$ wavelet transform is an arduous task. In this article, an adaptive tunable $Q$ wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using gray wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands (SBs). The set of time-domain features elicited from the SBs are used as an input to multiclass least-squares support vector machine. Classification accuracy of four basic emotions, namely, happy, fear, sad, and relax, is tested and compared with existing methods. An accuracy of 95.70% is achieved with a radial basis function kernel that is about 5% more than the existing methods using the same data set. This article proposes the development of a nonparameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems. [ABSTRACT FROM AUTHOR]

Cognitive workload assessment plays an important role to examine the mental status of brain for emergency situations faced by air traffic controllers, military personals and many more. It is also significant for brain disorder diagnosis and psychological health monitoring. Mental workload induced by various factors needs to be critically examined for better decisions. This paper presents an automated model for cognitive workload assessment to provide accurate categorization on intensity of workload induced by multitasking situations. Statistical wavelets corresponding to brain frequencies act as features for categorization task. The brain analysis needs technical expertise to identify the relevant information but, due to lack of sufficient number of experts, feature selection model helps to identify the relevant features responsible to categorize cognitive workload. Teaching Learning based Optimization (TLBO) method has been modified using ‘Logical Operators’ for binary feature selection problem in the current study and evaluated based on different parameters. Results on Logical TLBO reached upto 100% for binary and multiclass classification of cognitive workload. The results also show the proposed method is converging in minimum iteration for multiclass cognitive workload. PROMETHEE based multi-criteria decision-making approach has adopted for ranking of algorithm to select the best algorithm when multiple alternatives are available. Based on study of current literature, this approach appears to be first time implemented to provide ranking of optimization algorithms. The PROMETHEE method also suggests that the Logical TLBO is best among all the other compared approaches. [ABSTRACT FROM AUTHOR]

Diagnosis of schizophrenia (SZ) is traditionally performed through patient’s interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifierson a SZ EEG dataset. Among the considered classifiers, theensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ. [ABSTRACT FROM AUTHOR]

Sleep apnea is a sleeping disorder, which adversely affects the health of humans. The diagnosis of sleep apnea is possible by the detection of apnea events using electroencephalogram (EEG) recordings. This paper introduces an adaptive decomposition for the detection of apnea events using EEG signals. In introduced decomposition, the evolutionary techniques (ETs) optimized Hermite functions (HFs) represent the EEG signals. In tested ETs, the artificial bee colony (ABC) algorithm provides the least reconstruction error for the representation of EEG signals. The ABC is considered for Hermite coefficients-based feature extraction. From the extracted features, a highly discriminative feature set is obtained using the Fisher-score ranking test. The apnea detection performance of ranking-based selected features is evaluated using the extreme learning machine and least-squares support vector machine classifiers. The proposed method obtained performance measures, sensitivity 99.47%, specificity 99.58%, and accuracy 99.53%, are better as compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Human physical action classification is an emerging area of research for human-to-machine interaction, which can help to disable people to interact with real world, and robotics application. EMG signals measure the electrical activity muscular systems, which involved in physical action of human. EMG signals provide more information related to physical action. In this paper, we proposed deep transfer learning-based approach of human action classification using surface EMG signals. The surface EMG signals are represented by time–frequency image (TFI) by using short-time Fourier transform. TFI is used as input to pre-trained convolutional neural network models, namely AlexNet and VGG16, for deep feature extraction, and support vector machine (SVM) classifier is used for classification of physical action of EMG signals. Also, the fine-tuning of the pre-trained AlexNet model is also considered. The experimental results show that deep feature extraction and SVM classification method and fine-tuning have obviously improved the classification accuracy when compared with various results from the literature. The 99.04% accuracy score is obtained with AlexNet fc6 + AlexNet fc7 + VGG16 fc6 + VGG16 fc7 deep feature concatenation and SVM classification. 98.65% accuracy score is performed by fine-tuning of the AlexNet model. We also compare the obtained results with some of the existing methods. The comparisons show that the deep feature concatenation and SVM classification method provide better classification accuracy than the compared methods. [ABSTRACT FROM AUTHOR]

Early detection of driver drowsiness and the development of a functioning driver alertness system may support the prevention of numerous vehicular accidents worldwide. Wearable sensors and camera-based systems are generally employed in the driver drowsiness detection. Electroencephalogram (or EEG) is considered another effective option for the driver drowsiness detection. Various EEG-based drowsiness detection systems have been proposed to date. In this paper, EEG signals are also used for the detection of drowsiness, with the proposed method being composed of three main building blocks. Both raw EEG signals and their corresponding spectrograms are used in the proposed building blocks. In the first building block, while energy distribution and zero-crossing distribution features are calculated from the raw EEG signals, spectral entropy and instantaneous frequency features are extracted from the EEG spectrogram images. In the second building block, deep feature extraction is employed directly on the EEG spectrogram images using pre-trained AlexNet and VGGNet. In the third building block, the tunable Q-factor wavelet transform (TQWT) is used to decompose the EEG signals into related sub-bands. The spectrogram images of the obtained sub-bands and statistical features, such as mean and standard deviation of the sub-bands’ instantaneous frequencies, are then calculated. Each feature group from each building block is fed to a long-short term memory (LSTM) network for the purposes of classification. The obtained results from the LSTM networks are then fused with a majority voting layer. The MIT-BIH Polysomnographic database was used in the experimental works. The evaluation of the proposed method was carried out with ten-fold cross validation test and the average accuracy represented accordingly. The obtained average accuracy score was 94.31%. The obtained result was also compared with other results to be found in the literature. The comparison shows that the proposed method’s achievement was found to be better than the compared results. [ABSTRACT FROM AUTHOR]

Breast cancer is a serious disease for women in the world and ranks the second cancer for women in many countries. Computer-aided diagnosis provides a second view to aid for radiologists to detect and diagnose breast cancer. In this paper, we present a novel approach of textural features extraction from mammograms using bi-dimensional empirical mode decomposition (BEMD) method and classification for diagnosis of breast cancer. Preprocessing techniques such as noise removal, artifacts and background suppression and contrast enhancement are performed before features extraction stage. Gray-level co-occurrence matrices-based features are extracted from 2-D intrinsic mode functions obtained by applying BEMD method on mammographic images. Finally, these features are given as an input to least squares support vector machine for classification of mammogram as normal or abnormal. The experimental results show that the proposed method achieves 95% accuracy, which is better than as compared to other published methods in the Mini-MIAS database for diagnosis of breast cancer. The proposed method can be used as automatic, accurate and noninvasive method for breast cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology based on deep convolutional neural networks (DCNN) for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More specifically, the DCNN is used for classification of the right hand and right foot MI-tasks based electroencephalogram (EEG) signals. The proposed method first transforms the input EEG signals into images by applying the time-frequency (T-F) approaches. The used T-F approaches are short-time-Fourier-transform (STFT) and continuous-wavelet-transform (CWT). After T-F transformation the images of MI-tasks EEG signals are applied to the DCNN stage. The pre-trained DCNN model, AlexNet is explored for classification. The efficiency of the proposed method is evaluated on IVa dataset of BCI competition-III. The evaluation metrics such as accuracy, sensitivity, specificity, F1-score, and kappa value are used for measuring the proposed method results quantitatively. The obtained results show that the CWT approach yields better results than the STFT approach. In addition, the proposed method obtained 99.35% accuracy score is the best one among the existing methods accuracy scores. [ABSTRACT FROM AUTHOR]

Emotion is a most instinctive feeling of a human. Emotion classification finds application in brain–computer interface systems for the assistance of disabled persons. To recognise the emotional state, electroencephalogram (EEG) signal plays a vital role because it provides immediate response to every state of change in the human brain. Here, the utility of tunable‐Q wavelet transform (TQWT) is explored for the classification of different emotions EEG signals. TQWT decomposes EEG signal into subbands and time‐domain features are extracted from subbands. The extracted features are used as an input to extreme learning machine classifier for the classification of happy, fear, sad, and relax emotions. Experimental results of the proposed method show better four emotions classification performance when compared with the other existing methods. [ABSTRACT FROM AUTHOR]

Automatic detection of epileptic seizure from brain signal data (e.g. electroencephalogram (EEG)) is very crucial due to dynamic and complex nature of EEG signal (e.g. non‐stationarity, aperiodic and chaotic). Owing to these natures, manual interpretation and detection of epileptic seizure is not reliable and efficient process. Hence, this study is intended to develop a new computer‐aided detection system that can automatically and efficiently identify epileptic seizure from huge amount EEG data. In this study, Hermite Transform is introduced for extracting discriminating information from EEG data for the detection of epileptic seizure. The analysis is performed in three stages: EEG signal transformation into a new form by Hermite Transform; computation of three types of features, namely permutation entropy, histogram feature and statistical feature; and classification of obtained features by least square support vector machine. The classification outcomes reveal the presence of epileptic seizure. The proposed method is evaluated on a benchmark Epileptic EEG database (Bonn University data) and the performance of this method is compared with several state‐of‐art algorithms for the same database. The experimental results demonstrate that the proposed scheme has the ability to efficiently detect epileptic seizure from EEG data outperforming competing techniques in terms of overall classification accuracy. [ABSTRACT FROM AUTHOR]

Automatic drowsiness detection system plays a vital role to prevent the road accidents caused by drowsiness. In this regard, the electroencephalogram (EEG) signal provides valuable information of brain physiology for detection of drowsiness. EEG signals exhibit non-stationary nature which is tough to explore by prior defined and fixed number of basis functions. In this paper, an adaptive Hermite decomposition is proposed for the detection of drowsiness EEG signals. In the proposed decomposition, Hermite functions are used as basis functions, which are adaptively selected for each EEG signal by evolutionary optimization algorithms (EOAs). The mean square error of decomposition is proposed as an objective function to EOAs. The minimum decomposition error provided artificial bee colony EOA is considered to Hermite coefficients-based feature extraction for EEG signals. The extracted features are tested with the extreme learning machine (ELM), decision tree, $k$ -nearest neighbor, least squares support vector machine, artificial neural network, and naive Bayes for detection of alertness and drowsiness EEG signals. The proposed method with ELM classifier obtained 95.45% and 87.92% detection rates for alertness and drowsiness states, respectively, which are better as compared to other existing methods. [ABSTRACT FROM AUTHOR]

Classification of alcoholic electroencephalogram (EEG) signals is a challenging job in biomedical research for diagnosis and treatment of brain diseases of alcoholic people. The aim of this study was to introduce a robust method that can automatically identify alcoholic EEG signals based on time-frequency (T-F) image information as they convey key characteristics of EEG signals. In this paper, we propose a new hybrid method to classify automatically the alcoholic and control EEG signals. The proposed scheme is based on time-frequency images, texture image feature extraction and nonnegative least squares classifier (NNLS). In T-F analysis, the spectrogram of the short-time Fourier transform is considered. The obtained T-F images are then converted into 8-bit grayscale images. Co-occurrence of the histograms of oriented gradients (CoHOG) and Eig(Hess)-CoHOG features are extracted from T-F images. Finally, obtained features are fed into NNLS classifier as input for classify alcoholic and control EEG signals. To verify the effectiveness of the proposed approach, we replace the NNLS classifier by artificial neural networks, k-nearest neighbor, linear discriminant analysis and support vector machine classifier separately, with the same features. Experimental outcomes along with comparative evaluations with the state-of-the-art algorithms manifest that the proposed method outperforms competing algorithms. The experimental outcomes are promising, and it can be anticipated that upon its implementation in clinical practice, the proposed scheme will alleviate the onus of the physicians and expedite neurological diseases diagnosis and research. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) contains five rhythms, which provide details about various activities of brain. These rhythms are separated using Hilbert–Huang transform for classification of focal and non‐focal EEG signals. For this, the EEG signal is disintegrated into narrow bands intrinsic mode functions (IMFs) using empirical mode decomposition, and analytic representation of IMFs is computed by Hilbert transformation that helps to extract instantaneous frequencies of respective IMFs. Frequency bands of EEG signals known as rhythms are separated from analytic IMFs using instantaneous frequencies. Two efficient parameters Pearson product‐moment correlation coefficient and Spearman rank correlation coefficient extracted from the rhythms are used with different kernel functions of least‐squares support vector machine for the classification of focal and non‐focal EEG signals. Thus, obtained results show improved performance of proposed method as compared to other existing methods. [ABSTRACT FROM AUTHOR]

• Investigate the detailed analysis of the SZ and HC with different conditions. • Selection of dominant channel by Fisher score to reduce the burden of computation. • F-TQWT for automatic and meaningful extraction of sub-bands from EEG signals. • Accurate selection of decomposition levels for uniform decomposition. • F-LSSVM for tuning of hyperparameters to improve the separability of SZ and HC. Background: Schizophrenia (SZ) is a type of neurological disorder that is diagnosed by professional psychiatrists based on interviews and manual screening of patients. The procedures are time-consuming, burdensome, and prone to human error. This urgently necessitates the development of an effective and precise computer-aided design for the detection of SZ. One such efficient source for SZ detection is the electroencephalogram (EEG) signals. Because EEG signals are non-stationary, it is challenging to find representative information in its raw form. Decomposing the signals into multi-modes can provide detailed insight information from it. But the choice of uniform decomposition and hyper-parameters leads to information loss affecting system performance drastically. Method: In this paper, automatic signal decomposition and classification methods are proposed for the detection of SZ and healthy control EEG signals. The Fisher score method is used for the selection of the most discriminant channel. Flexible tunable Q wavelet transform (F-TQWT) is developed for efficient decomposition of EEG signals by reducing root mean square error with grey wolf optimization (GWO) algorithm. Five features are extracted from the adaptively generated subbands and selected by the Kruskal Wallis test. The feature matrix is given as an input to the flexible least square support vector machine (F-LSSVM) classifier. The hyper-parameters and kernel of classifier are selected such that the accuracy of each subband is maximized using GWO algorithm. Results: The effectiveness and superiority of the proposed method is tested by evaluating seven performance parameters. An accuracy of 91.39%, sensitivity, specificity, precision, F-1 measure, false positive rate and error of 92.65%, 93.22%, 95.57%, 0.9306, 6.78% and 8.61% is achieved. The results prove superiority of the developed F-TQWT decomposition and F-LSSVM classifier over existing methodologies. Conclusion: The EEG signals of healthy control and SZ subjects performing motor and auditory tasks simultaneously provide higher discrimination ability over the subjects performing auditory and motory tasks separately. The developed model is accurate, robust, and effective as it is developed on a relatively larger data-set, obtained maximum performance, and tested using ten-fold cross-validation technique. This proposed model is ready to be put to test for real-time SZ detection. [ABSTRACT FROM AUTHOR]

Drowsy driving is one of the major causes of road accidents. The road accidents can be avoided by the discrimination of alertness and drowsiness states of the drives. The neurological changes in alertness and drowsiness states can be asses by electroencephalogram (EEG) signals. In this paper, the non-stationary characteristic of the EEG signal is explored by tunable Q-factor wavelet transform (TQWT). TQWT decomposes the EEG signal into sub-bands, which further used for the extraction of features. Statistical features of the Hjorth mobility such as minimum value, maximum value, mean and standard deviation (SD) are used for characterization of the alertness and drowsiness states. Various classifiers such as decision tree, logistic regression, fine Gaussian support vector machine, weighted KNN, ensemble boosted trees and extreme learning machine (ELM) are considered. The alertness and drowsiness EEG signals discriminative performance of TQWT-based features are assessed by the Kruskal-Wallis (KW) test. The results of KW-test show that the proposed features are effectively discriminative of the alertness and drowsiness states. According to the obtained results, the best accuracy score of 91.842% is produced by the ELM classifier. [ABSTRACT FROM AUTHOR]

Classification of electroencephalogram (EEG) signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. Here, we introduce a different multi-category EEG signal processing technique, namely time-frequency (T-F) image representation of Gray Level Co-occurrence Matrix (GLCM) descriptors and Fisher Vector (FV) encoding for automatic classification of EEG signals. Firstly the EEG signals are converted into T-F representation by using spectrograms of Short Time Fourier Transform (STFT), which are used to obtain the T-F images. The obtained T-F images are then converted into 8-bits gray-scale images and then are divided into five sub-images corresponding to the frequency-bands of the rhythms. Then, the GLCM texture descriptors are employed to extract distinctive features which are fed into the FV encoding. Finally obtained features are fed to extreme learning machine (ELM) classifier as input for identifying abnormalities from EEG signals. The proposed method was applied to epileptic and sleep stages EEG datasets. The experimental outcomes are promising on both databases. It can be anticipated that upon its implementation in real-time practice, the proposed scheme will assist the researchers and physicians to advance the existing methods for detecting neurological diseases from EEG signals. [ABSTRACT FROM AUTHOR]