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The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence. [ABSTRACT FROM AUTHOR]

Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50 % overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected. [ABSTRACT FROM AUTHOR]

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that affects millions of children. In this manuscript, we propose methods to classify two groups of children, viz. healthy children, and children with ADHD, providing supplementary information to the doctors for analysis and prediction. An electroencephalogram (EEG) correlation and EEG channel selection are used to discriminate between ADHD EEG and healthy children's EEG. The primary objective of the channel selection method is to reduce the dimensions of the data, reduce computational complexity, improve model performance, and provide faster processing. We use EEG signals recorded in three different scenarios. We propose two models for channel selection using Pearson's correlation coefficient and Hoeffding's D correlation coefficient. Various nonlinear features are extracted from selected channels and used with multiple classifiers. The performance of different classifiers has been tested by calculating the accuracy, precision, recall, and ROC curves among different datasets. The proposed algorithms achieved similar or better accuracy above 90 %with significantly fewer features and channels. [ABSTRACT FROM AUTHOR]

When studying the relationship between acoustic emission features and coal samples and fractures, both raw coal and briquette can be used as experimental samples. Most coal seams have soft materials, making it difficult to manufacture standard raw coal samples. Therefore, it is common to use briquettes as research samples in experiments. However, coal briquettes change the original structure of coal, affecting its physical and mechanical properties. The applicability of using briquettes instead of raw coal as experimental samples has always been a focus of academic discussion. And currently, there is relatively limited research on the differences in acoustic emission features between raw coal and briquette in pseudo triaxial compression experiments. In order to solve the above problems, pseudo triaxial compression acoustic emission experiments are conducted on raw coal and briquette, with a focus on discussing and analyzing mechanical properties, fracture modes, spatiotemporal evolution of acoustic emission, frequency band energy distribution, nonlinear features, and other aspects. The results show that the acoustic emission energy released during the loading process and the total peak stress energy are closely related to the strength of the coal sample. The raw coal mainly exhibits a mixed failure mode of shear and tension, while the briquette mainly exhibits a tensile axial crack failure mode. The acoustic emission positions of coal samples correspond to their macroscopic fracture morphology, but their occurrence time and spatial distribution are different. In the pre peak loading stage, the acoustic emission signal of raw coal is relatively small, while the acoustic emission response of briquette is intense and reaches its maximum value at the peak stress moment. Through wavelet packet analysis, it is found that the energy distribution of the acoustic emission frequency band of briquette is smaller than that of raw coal. The acoustic emission signals of raw coal are mainly concentrated in the frequency range of 10-120 kHz, while the acoustic emission signals of briquette only jump in the frequency range of 0-100 kHz, indicating that the micro fracture scale of briquette is larger than that of raw coal. 90% of the waveform energy of raw coal and briquette is active at 0-150 kHz. When the loaded sample approaches instability failure, i.e. around 99% of the peak stress, the Hurst index of the acoustic emission signals of raw coal and briquette are both greater than 0.5. It indicates a long-term correlation between the acoustic emission time series and the loading process.

AbstractEpilepsy is a brain disorder that causes patients to suffer from convulsions, which affects their behavior and way of life. Epilepsy can be detected with electroencephalograms (EEGs), which record brain neural activity. Traditional approaches for detecting epileptic seizures from an EEG signal are time-consuming and annoying. To supersede these traditional methods, a myriad of automated seizure detection frameworks based on machine learning techniques have recently been developed. Feature extraction and classification are the two essential phases for seizure detection. The classifier assigns the proper class label after feature extraction lowers the input pattern space while maintaining useful features. This paper proposes a new feature extraction method based on calculating nonlinear features from the most relevant EEG frequency bands. The EEG signal is first decomposed into smaller time segments from which a vector of nonlinear features is computed and supplied to machine learning (ML) and deep learning (DL) classifiers. Experiments on the Bonn dataset reveals an accuracy of 99.7% reached in classifying normal and ictal EEG signals; and an accuracy of 98.8% in the discrimination of ictal and interictal EEG signals. Furthermore, a performance of 100% is achieved on the Hauz Khas dataset. The classification results of the proposed approach were compared to those from published state of the art techniques. Our results are equivalent to or better than some recent studies appeared in the literature. [ABSTRACT FROM AUTHOR]

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Objective: Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo. Materials and methods: In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification. Results: The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49± 0.01 %, and 98.53± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data. Conclusions: External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

Sudden Cardiac Death (SCD) leads to the killing of millions of people worldwide every year. In this article, sudden cardiac death is predicted by utilizing electrocardiogram signal processing. For this purpose, after extracting the signal of heart rate variations from the electrocardiogram signal, temporal and non-linear features have been extracted. In the next step, by applying LDA to the combined feature vector, the feature dimensions are reduced and finally, healthy people and high-risk people are classified through Hybrid-RBF classifiers. The obtained results show that there are features in the signal of heart rate variations related to risk-taking individuals near the occurrence of sudden cardiac death, that completely distinguish them from healthy persons. It has also been shown that from 6 minutes before the occurrence of cardiac death, this increase in the probability of risk is quite evident, so that as we get closer to the occurrence of the accident, the probability of its occurrence also increases, and this is enough time to adopt strategies to prevent it. The simulation results achieved by the data available in the MIT-BIH database prove the ability of the presented methods to achieve accurate diagnosis. [ABSTRACT FROM AUTHOR]

In an uncontrolled scenario like hospitals, medical research centres, laboratories, forensics, the voice recordings are normally affected with babble noise. Most of the speech enhancement (SE) methods are limited by the colour noise due to non-consideration of phase aspect. Hence, the objective of the work is to depict a more efficient and compact SE method through the comparative study of conventional SE methods taking into account of both continuous speech and sustained vowels. This approach is supported with supervised voice activity detection (VAD). The study includes an experimental investigation of time and transform-based seven conventional methods. This approach consists of pre-processing, SE evaluation block and supervised VAD section. The speech segments are efficiently detected by VAD using bio-inspired Mel frequency cepstral coefficients and nonlinear features with support vector machine. This supervised VAD helps in reliable estimation of SNR-based parameters. The performance of each of the methods is evaluated with continuous NOIZEUS corpus at varying signal-to-noise ratio (SNR) levels, laryngeal pathology-based data from Saarbruecken voice database and voice disorder data collected from cancer hospitals. Experimental results show that the performance of the Wiener filtering and geometrical-based SE methods is found to be better comparatively, as far as continuous and sustained vowels are concerned with babble noise. The paper also reveals the significant involvement of nonlinear features in VAD, as the nonlinear dynamical methods are capable of analysing irregular behaviours of vocal cords. [ABSTRACT FROM AUTHOR]

The goal of this paper was to assess the impact of learning a new skill on several electroencephalographic (EEG) measurements. In addition, it intended to examine the active brain regions associated with learning. To this effect, changes in brain activity during the pre- and post-learning task were investigated using EEG power spectral density (PSD) and nonlinear features. The evaluated task was learning to type using the Colemak keyboard layout in a twelve-lesson training. 10 participants with a mean age of 29.3 ± 5.7 were included in the experiment. For the first time, Fractal dimension, approximate entropy, and Lyapunov exponents were extracted from a 9-channel EEG signal to characterize brain dynamics. In addition, the PSD of various EEG rhythms was estimated. To identify statistically significant changes in the EEG characteristics, as well as to determine the prominent channels, the t-test was performed. Our results showed maximum EEG power in Beta and Gamma waves; however, the most differences in the power distribution belonged to the Beta band. Among nonlinear features, entropy showed a significant difference in 70% of the EEG channels. This has mainly occurred in the F3, Fz, C3, Cz, POz, and P4 electrodes. Taken together, the results of this study pave the way to evaluate brain dynamics and actively involved areas in pre- and post-learning tasks. [ABSTRACT FROM AUTHOR]

In recent years, the use of intelligent methods for automatic detection of sleep stages in medical applications to increase diagnostic accuracy and reduce the workload of physicians in analyzing sleep data by visual inspection is one of the important issues. The most important step for the automatic classification of sleep stages is the extraction of useful features. In this paper, an EEG-based algorithm for automatic detection of sleep stages is presented using features extracted from the recurrence plot and artificial neural network. Due to the non-stationary of the EEG signal, the recurrence plot was used in this paper for nonlinear analysis and extraction of signal features. Various extracted features have different numerical ranges. Normalization was performed to prevent the undesirable effects of large values of data. As all normalized features could not correctly classify different stages of sleep, effective features were selected. The results of this paper show the selected features and the Multi-Layer Perceptron (MLP) neural network able to achieve the values of 98.54 ± 1.88%, 99.03 ± 1.43%, and 98.32 ± 2.11%, respectively, for specificity, sensitivity, and accuracy between the two types of sleep, i.e., Non-Rapid Eye Movement (Non-REM) and Rapid Eye Movement (REM). Also, the results show that the selection of Pz-Oz channel compared to Fpz-Cz channel leads us to a higher percentage for the separation of stages I-IV, awake, while the separation of REM stage using Fpz-Cz channel is better. The results show that the proposed method has a higher success rate in classifying sleep stages than previous studies. The proposed method could well identify and distinguish all stages of sleep at an acceptable level. In addition to saving time, automatic analysis of sleep stages can help better and more accurate diagnosis and reduce physicians' workload in analyzing sleep data through visual inspection.

Fault detection and classification is a crucial issue in modern industrial processes for ensuring steady operation and high product quality. The process data collected and stored fully reflect the equipment running state and the production process. Moreover, the extracted nonlinear features can directly affect the effectiveness of the data‐driven fault classification model. In this paper, a novel fault classification method based on nonlinear feature extraction using reconstructed distance‐based discriminant locality preserving projection (RD‐DLPP) is proposed. First, a hypersphere model for each class of data is developed according to the spatial structures and classes information in high‐dimensional space. The hyperspheres are used as indicators to evaluate the discriminatory difficulty of samples. Second, the constraints of the correlations between the k‐nearest neighbour points of the sample and the hypersphere are introduced, which can efficiently reconstruct new measure metrics between the sample and its k‐nearest neighbour points. Finally, an improved fault classification model based on RD‐DLPP is established for the construction of the highly discriminant subspace. The Bayesian decision is then used to classify the samples. The feasibility and efficiency of the proposed method are verified by the Tennessee Eastman process as a case study. [ABSTRACT FROM AUTHOR]

It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable. [ABSTRACT FROM AUTHOR]

Celiac Disease (CD) is a common ailment that affects approximately 1% of the world population. Automated CD detection can help experts during the diagnosis of this condition at an early stage and bring significant benefits to both patients and healthcare providers. For this purpose, scientists have created automatic and semi-automatic CD diagnostic support systems. In this study, we performed information extraction methods that were found useful for efforts to differentiate CD versus non-CD. To focus the review process, only methods for endoscopy, video capsule endoscopy (VCE) and biopsy image analyses were considered. As described herein, we have learned that statistical and non-linear methods are most important for information extraction. These information extraction tools might benefit clinical workflows by reducing intra- and inter-observer variability. However, bias, introduced by resolving design choices during the creation of diagnostic support systems, may limit the general validity of the performance results, impacting the transferability of study outcomes. Therefore, having am overview of information extraction tools. Together with their general and specific limitations, might be assistive in improving the information extraction process. We hope our review results will provide a foundation for the design of next-generation statistical and nonlinear methods that can be used in CD detection systems. We have also compared various review articles and discussed recommendations to improve CD diagnosis. From this review, it is evident that CD diagnosis is slowly moving away from conventional techniques towards advanced deep learning techniques. [ABSTRACT FROM AUTHOR]

This study proposes a novel adaptive super-twisting sliding mode controller (ASTSMC) for hydraulic servo actuator with nonlinear features and modeling uncertainties. In the proposed method, an extended state observer (ESO) is utilized to estimate the value of lumped uncertainties. The core feature of this paper is the combination of ASTSMC with ESO to compensate disturbance in hydraulic servo actuator. Moreover, the proposed ASTSMC does not need to obtain the bound of uncertainties in advance and ensures that the sliding variable and its derivative reach to zero in a finite time. In addition, the stability of the closed-loop is proved by Lyapunov theory. Simulation and experiment results demonstrate that the proposed ASTSMC can effectively mitigate the lumped uncertainties and obviously improve the tracking performance. [ABSTRACT FROM AUTHOR]

In recent years, the use of intelligent methods for automatic detection of sleep stages in medical applications to increase diagnostic accuracy and reduce the workload of physicians in analyzing sleep data by visual inspection is one of the important issues. The most important step for the automatic classification of sleep stages is the extraction of useful features. In this paper, an EEG-based algorithm for automatic detection of sleep stages is presented using features extracted from the recurrence plot and artificial neural network. Due to the non-stationary of the EEG signal, the recurrence plot was used in this paper for nonlinear analysis and extraction of signal features. Various extracted features have different numerical ranges. Normalization was performed to prevent the undesirable effects of large values of data. As all normalized features could not correctly classify different stages of sleep, effective features were selected. The results of this paper show the selected features and the Multi-Layer Perceptron (MLP) neural network able to achieve the values of 98.54 ± 1.88%, 99.03 ± 1.43%, and 98.32 ± 2.11%, respectively, for specificity, sensitivity, and accuracy between the two types of sleep, i.e., Non-Rapid Eye Movement (Non-REM) and Rapid Eye Movement (REM). Also, the results show that the selection of Pz-Oz channel compared to Fpz-Cz channel leads us to a higher percentage for the separation of stages I-IV, awake, while the separation of REM stage using Fpz-Cz channel is better. The results show that the proposed method has a higher success rate in classifying sleep stages than previous studies. The proposed method could well identify and distinguish all stages of sleep at an acceptable level. In addition to saving time, automatic analysis of sleep stages can help better and more accurate diagnosis and reduce physicians' workload in analyzing sleep data through visual inspection. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal ideation (SI). Current clinical diagnostic approaches are solely based on psychiatric interview questionnaires. Thus, a computational intelligence tool for the automated detection of MDD with and without suicidal ideation is presented in this study. Since MDD is proven to affect cardiovascular and respiratory systems, the aim of the study is to automatically identify the disorder severity in MDD patients using corresponding multi-modal physiological signals, including electrocardiogram (ECG), finger photoplethysmography (PPG) and respiratory signals (RSP). Data from 88 subjects were used in this study, out of which 25 were MDD patients without SI (MDDSI−), 18 MDD patients with SI (MDDSI+), and 45 normal subjects. Multi-modal physiological signals were acquired from each subject, including ECG, RSP, and PPG signals, and then pre-processed. Discrete wavelet transform (DWT) was applied to the signals, which were decomposed up to six levels, and then eleven nonlinear features were extracted. The features were ranked according to the analysis of variance test and Marginal Fisher Analysis was employed to reduce the feature set, after which the reduced features were ranked again to select the most discriminatory features. Support vector machine with polynomial radial basis function (SVM-RBF) as well as k-nearest neighbor (KNN) classifiers were used to classify the significant features. The performance of the classifiers was evaluated in a 10-fold cross validation scheme. The best performance achieved for the classification of MDDSI+ patients was up to 85.2%, by using selected features from the obtained multi-modal signals with SVM-RBF, while it was up to 96.6% for the detection of MDD patients against healthy subjects. This work is a step toward the utilization of automated tools in diagnostics and monitoring of MDD patients in a personalized and wearable healthcare system.

Objective: Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo., Materials and Methods: In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification., Results: The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49 ± 0.01 %, and 98.53 ± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data., Conclusions: External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person's life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student's t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student's t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features. [ABSTRACT FROM AUTHOR]

Purpose: Sleep apnea is a common disease among women, and mainly men. The most dangerous complication of this disorder is heart stroke. Other complications include insufficient sleep and resulting daytime tiredness and illness that affect the individual's activities during the day, disrupt their life. Therefore, identifying this disease is important. Materials and Methods: We used Electroencephalogram (EEG) and Electrocardiogram (ECG) channels from the data of 25 patients with sleep apnea, for each type of sleep apnea, 8 nonlinear-like features, including fractal dimension, correlation dimension, certainty, recurrence rate, mean diagonal lines, the entropy of recursive quantification analysis, sample Entropy, and Shannon entropy were extracted. Then, feature matrices were sorted using principal component analysis in the order of linear combination of features, and the 20 selected features were chosen, normalized using common methods, and fed to different classifiers. Two 5-class and 2-class classification methods were assessed. In the 5-classification, three classifiers were used; the support vector machine, k-nearest neighbor, and multilayer perceptron. Results: The results showed that the highest mean validity, accuracy, sensitivity, and specificity for the SVM classifier was 88.45%, 88.35%, 88.33%, and 88.32%, respectively. In the 2-class approach, in addition to the mentioned classifiers, linear discriminant analysis, Bayes, and majority voting were used, and each class was considered against all classes. The highest average validity, average accuracy, average sensitivity, average specificity using the majority rule voting was 94.35%, 94.30%, 94.32%, and 94.15% respectively. Conclusion: When the results of classifiers are combined with the majority voting method, the validity of identifying the classes increases. The average validity for this method was obtained at 94.42%, which was higher than several other studies. It is recommended that databases with a larger sample size be used. This would lead to increased reliability of the proposed analysis method. Moreover, using novel deep-learning-based methods could help obtain better results.

• A combination of time and nonlinear domain features based on RR intervals is proposed. • The features extracted were tested on the ECG recording from the MIT-BIH Atrial Fibrillation Database and MIT-BIH Arrhythmia Database with intra-patient and inter-patient paradigm. • Avoid overfitting problems with random forests and ten-fold cross-validation. • The proposed method in different paradigms of intra-patient and inter-patient achieves a great performance with accuracy of 99.50% and 95.5%, respectively. Paroxysmal Atrial Fibrillation (PAF) may lead to the decline of atrial mechanism and hemodynamic disturbance, which is the major risk factor for stroke, myocardial infarction, heart failure and other diseases. Although a number of methods have been developed to detect PAF, they rely on long ECG signals. Furthermore, most studies are only for the intra-patient test, ignoring individual specificity. Therefore, this paper proposes an automatic detection algorithm based on the combination of RR interval time-domain and nonlinear features to detect short-term PAF. The proposed features reflect the underlying physiological phenomenon of highly irregular ventricular activity caused by atrial unstable conduction. These include changes in morphological features and intrinsic changes in the cardiac dynamic system, which can capture subtle changes in early short-term PAF episodes and attenuate specificity between individuals. Finally, the random forest algorithm was used to detect the MIT-BIH Atrial Fibrillation Database and MIT-BIH Arrhythmia Database with intra-patient and inter-patient paradigm. The results show that the two databases' algorithm accuracy in different paradigms of intra-patient and inter-patient have achieved more than 99.50% and 95.50%, respectively. Therefore, the proposed algorithm has a potential application for clinical monitoring. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) generally applied to an unpredicted death from a cardiovascular cause in a subject with or without preexisting heart disease. The main goal of this study was analyzing the Electrocardiogram (ECG) signal to design an algorithm to predict SCD risk. In this paper, ECG signals of 23 subjects (13 males, 8 females and 2 unknown), ranging from 17 to 89 years old necessary for the research were obtained from the Physionet database. For this purpose, we developed a new method to predict SCD, a 10-min prior heart attack using the return map. The aim of this study is a novel method based on Lag return map for in control patients and SCD classes. Return map with six different lags (1–6) was constructed in two-time intervals. After that, the non-linear features that include SD1, SD2, SD1/SD2 for each Lag was measured. The result shows that the rate of changes in SD1 and SD1/SD2 with increasing lags were increased significantly but in SD2 with increasing lags was decreased in two intervals. Statistical analysis indicates that return map parameters show changes in the transition to death episode (p < 0.05). Besides, there were significant changes (p < 0.01) in closer segments to death. In conclusion, it will be possible to predict SCD based on the nonlinear feature that can alarm doctors of an imminent SCD, helping them provide timely treatments that can increase the survival rate of patients and thus reduce the mortality rate. [ABSTRACT FROM AUTHOR]

Sudden cardiac arrest (SCA) prediction using electrocardiogram (ECG) and heart rate variability (HRV) signals has received the attention of researchers in recent years. Ventricular fibrillation (VF) is one of the most common identifiers for SCA. This work aims to investigate the ECG morphological feature, R peak to T-end (R–Tend), to foresee the imminent SCA 5 min before VF onset. ECG signals for SCA and Normal Controls from The MIT-BIH databases are divided into 1-min duration and is used to predict the onset of VF. Four nonlinear features, the Largest Lyapunov Exponent, Hurst exponent, sample entropy, and approximate entropy were extracted from the R–Tend beats and classified using three classifiers: support vector machine, subtractive fuzzy clustering, and neuro-fuzzy classifier. The performance of the proposed methodology confirms that the sample entropy features efficiently predict the SCA five min before VF onset using SVM classifier and produces the maximum mean classification rate of 100% compared to other classifiers. The proposed algorithm using R–Tend beats predicts the onset of SCA better than HRV signals and is computationally efficient. The proposed marker based on ECG morphological characteristics can be used as a tool to predict SCA for smarter healthcare management. [ABSTRACT FROM AUTHOR]

Typical convolutional filter only extract features linearly. Although nonlinearities are introduced into the feature extraction layer by using activation functions and pooling operations, they can only provide point-wise nonlinearity. In this paper, a Gaussian convolution for extracting nonlinear features is proposed, and a hybrid nonlinear convolution filter consisting of baseline convolution, Gaussian convolution and other nonlinear convolutions is designed. It can efficiently achieve the fusion of linear features and nonlinear features while preserving the advantages of traditional linear convolution filter in feature extraction. Extensive experiments on the benchmark datasets MNIST, CIFAR10, and CIFAR100 show that the hybrid nonlinear convolutional neural network has faster convergence and higher image recognition accuracy than the traditional baseline convolutional neural network. [ABSTRACT FROM AUTHOR]

The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which has affected $\approx ~60$ million people in the world. Hence, an early detection of the focal epileptic seizures can be carried out using the EEG signals, which act as a helpful tool for early diagnosis of epilepsy. Several EEG-based approaches have been proposed and developed to understand the underlying characteristics of the epileptic seizures. Despite the fact that the early results were positive, the proposed techniques cannot generate reproducible results and lack a statistical validation, which has led to doubts regarding the presence of the pre-ictal state. Various methodical and algorithmic studies have indicated that the transition to an ictal state is not a random process, and the build-up can lead to epileptic seizures. This study reviews many recently-proposed algorithms for detecting the focal epileptic seizures. Generally, the techniques developed for detecting the epileptic seizures were based on tensors, entropy, empirical mode decomposition, wavelet transform and dynamic analysis. The existing algorithms were compared and the need for implementing a practical and reliable new algorithm is highlighted. The research regarding the epileptic seizure detection research is more focused on the development of precise and non-invasive techniques for rapid and reliable diagnosis. Finally, the researchers noted that all the methods that were developed for epileptic seizure detection lacks standardization, which hinders the homogeneous comparison of the detector performance.

Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann–Whitney U test and Support Vector Machine Attribute evaluation. [ABSTRACT FROM AUTHOR]

Nowadays, an increasing number of photovoltaic (PV) plants are becoming integrated into one regional power grid. Under this circumstance, the probabilistic forecast of regional PV power generation is of significance for the regional power system operation and control. This study presents a novel probabilistic forecast method for regional PV generation that integrates the convolutional neural network (CNN) with non‐linear quantile regression (QR). In this method, the CNN structure is enhanced to extract the non‐linear features of the input data and generate the non‐linear QR function. As a result, the improved CNN can effectively process high‐dimensional and complex input data and the non‐linear QR model can provide quantile forecast information of regional PV power. The validity of the proposed method is verified by using it to forecast the regional PV generation from the clustered PV plants in the Weifang region of China. [ABSTRACT FROM AUTHOR]

Dynamic data modeling has been attracting much attention from researchers and has been introduced into the probabilistic latent variable model in the process industry. It is a huge challenge to extend these dynamic probabilistic latent variable models to nonlinear forms. In this article, a supervised nonlinear dynamic system (NDS) based on variational auto-encoder (VAE) is introduced for processes with dynamic behaviors and nonlinear characteristics. Based on the framework of VAE, which has a probabilistic data representation and a high fitting ability, the supervised NDS can extract effective nonlinear features for latent variable regression. The feasibility of the proposed supervised NDS is tested on two numerical examples and an industrial case. Detailed comparisons verify the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

An unforeseen fault of the key bearing of production system due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. To improve the reliability of industry production, this article presents an intelligent diagnosis method for element rolling bearing based on the integrated interaction relationship of vibration nonlinear features. The nonlinear features of vibration signals are extracted using recurrence quantification analysis (RQA) and regrouped into different subsets of nonredundant features with the same level of discrimination ability through the technology of ReliefF-affinity propagation clustering. The weighted voting variable predictive model class discrimination (WV-VPMCD) is proposed to fully utilize the interaction of RQA features to do intelligent diagnostics for bearing faults. The experimental results have showed that the WV-VPMCD outperformes the conventional intelligent diagnosis methods in terms of accuracy, consistency, stability, and robustness, especially in the case of small number of samples. [ABSTRACT FROM AUTHOR]

Respiratory sound is able to differentiate sleep stages and provide a non-contact and cost-effective solution for the diagnosis and treatment monitoring of sleep-related diseases. While most of the existing respiratory sound-based methods focus on a limited number of sleep stages such as sleep/wake and wake/rapid eye movement (REM)/non-REM, it is essential to detect sleep stages at a finer level for sleep quality evaluation. In this paper, we for the first time study a sleep stage detection method aiming at classifying sleep states into four sleep stages: wake, REM, light sleep, and deep sleep from the respiratory sound. In addition to extracting time-domain features, frequency-domain features of respiratory sound, non-linear features of snoring sound are devised to better characterize snoring-related signals of respiratory sound. To effectively fuse the three sets of features, a novel feature fusion technique combining the generalized canonical correlation analysis with the ReliefF algorithm is proposed for discriminative feature selection. Final stage detection is achieved with popular classifiers including decision tree, support vector machines, K-nearest neighbor, and the ensemble classifier. To evaluate our proposed method, we built an in-house dataset, which is comprised of 13 nights of sleep audio data from a sleep laboratory. Experimental results indicate that our proposed method outperforms the existing related ones and is promising for large-scale non-contact sleep monitoring. [ABSTRACT FROM AUTHOR]

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.

As software system becomes complex and large, some phenomena, performance degradation, error service, and even the abrupt system outage, have been found in the operating system, web server, and even in the security system. These phenomena can be called software ageing problems. In order to make software re-enter into a robust state, some prediction methods need be used to find an optimal time to do rejuvenation. Since the prediction method used now is linear or non-linear, a hybrid method is proposed in this work to improve the prediction accuracy. The proposed method contains three phases. Firstly, a linear method is applied to capture the linear characteristics of resource consumption series. Secondly, a non-linear method is used to fit non-linear features in the original data and residual data. Finally, the characteristic of the linear and non-linear part will be fitted by a non-linear model. In the experiments, the results show that the proposed method outperforms the other two single models.

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

As software system becomes complex and large, some phenomena, performance degradation, error service, and even the abrupt system outage, have been found in the operating system, web server, and even in the security system. These phenomena can be called software ageing problems. In order to make software re-enter into a robust state, some prediction methods need be used to find an optimal time to do rejuvenation. Since the prediction method used now is linear or non-linear, a hybrid method is proposed in this work to improve the prediction accuracy. The proposed method contains three phases. Firstly, a linear method is applied to capture the linear characteristics of resource consumption series. Secondly, a non-linear method is used to fit non-linear features in the original data and residual data. Finally, the characteristic of the linear and non-linear part will be fitted by a non-linear model. In the experiments, the results show that the proposed method outperforms the other two single models. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non‐linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non‐linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non‐linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) analysis is mostly used as a valuable tool in the evaluation of cognitive tasks. By taking and analyzing measurements in vast quantities, researchers are working toward a better understanding of how human physiological systems work. For the first time, this study investigated the function of the cardiovascular system during creative thinking. In addition, the difference between male/female and normal/creativity states from ECG signals was investigated. Overall, the purpose of this paper was to illustrate the heart working during the creativity, and discover the creative men or women subjects. For these goals, six nonlinear features of the ECG signal were extracted to detect creativity states. During the three tasks of the Torrance Tests of Creative Thinking (TTCT- Figural B), ECG signals were recorded from 52 participants (26 men and 26 women). Then, the proficiency of two kinds of classification approaches was evaluated: Artificial Neural Network (ANN) and Support Vector Machine (SVM). The results indicated the high accuracy rate of discriminations between male/female (96.09%) and normal/creativity states (95.84%) using ANN classifier. Therefore, the proposed method can be useful to detect the creativity states.

Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.