Flexible Analytic Wavelet Transform Research Articles

A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition.

Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. At present, SER is an important aspect of artificial psychology and artificial intelligence, as it is widely implemented in many applications in the human-computer interface, medical, and entertainment fields. In this work, six transforms, namely, the synchrosqueezing transform, fractional Stockwell transform (FST), K-sine transform-dependent integrated system (KSTDIS), flexible analytic wavelet transform (FAWT), chirplet transform, and superlet transform, are initially applied to speech emotion signals. Once the transforms are applied and the features are extracted, the essential features are selected using three techniques: the Overlapping Information Feature Selection (OIFS) technique followed by two biomimetic intelligence-based optimization techniques, namely, Harris Hawks Optimization (HHO) and the Chameleon Swarm Algorithm (CSA). The selected features are then classified with the help of ten basic machine learning classifiers, with special emphasis given to the extreme learning machine (ELM) and twin extreme learning machine (TELM) classifiers. An experiment is conducted on four publicly available datasets, namely, EMOVO, RAVDESS, SAVEE, and Berlin Emo-DB. The best results are obtained as follows: the Chirplet + CSA + TELM combination obtains a classification accuracy of 80.63% on the EMOVO dataset, the FAWT + HHO + TELM combination obtains a classification accuracy of 85.76% on the RAVDESS dataset, the Chirplet + OIFS + TELM combination obtains a classification accuracy of 83.94% on the SAVEE dataset, and, finally, the KSTDIS + CSA + TELM combination obtains a classification accuracy of 89.77% on the Berlin Emo-DB dataset.

Complex Flexible Analytic Wavelet Transform for UAV State Identification Using RF Signal

Complex Flexible Analytic Wavelet Transform for UAV State Identification Using RF Signal

Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

Mind-wandering (MW) is when an individual’s concentration drifts away from the task or activity. Researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society.

Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features.

Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection process and has a favorable impact on patients' health. Currently, a biomarker termed electroencephalography (EEG) is considered as vital tool to detect abnormal electrical activity in the brain. In this context, the present paper brings forth a novel approach for automated diagnosis of ASD from multichannel EEG signals using flexible analytic wavelet transform (FAWT). Firstly, this approach processes the acquired EEG signals with filtering and segmentation into short-duration EEG segments in the range of 5-20 s. These segmented EEG signals are decomposed into five levels using FAWT technique to obtain various sub-bands. Further, multiscale permutation entropy values are extracted from decomposed sub-bands which are used as feature vectors in the present work. Afterwards, these feature vectors are evaluated by traditional machine learning algorithms viz., k-nearest neighbor, logistic regression, support vector machine, and random forest, as well as convolutional neural network (CNN) as deep learning algorithm with different segment durations. The analysis of results reveals that CNN provides maximum accuracy, sensitivity, specificity, and area under the curve of 99.19%, 99.34%, 99.21%, and 0.9997, respectively, for 10 s duration EEG segment to identify ASD patients among healthy individuals. Thus, the proposed CNN architecture would be extremely helpful during diagnostic process of autism disease for neurologists.

EEG-Based Driver Fatigue Detection Using FAWT and Multiboosting Approaches

Globally, 14%–20% of road accidents are mainly due to driver fatigue, the causes of which are instance sickness, travelling for long distance, boredom as a result of driving along the same route consistently, lack of enough sleep, etc. This article presents a flexible analytic wavelet transform (FAWT)-based advanced machine learning method using single modality neurophysiological brain electroencephalogram signals to detect the driver fatigues (i.e., FATIGUE and REST) and to alarm the driver at the earliest to prevent the risks during driving. First, signals of undertaking study groups are subjected to the FAWT that separates the signals into LP and HP channels. Subsequently, relevant subband frequency components with proper setting of tuning parameters are extracted. Then, comprehensive low order features which are statistically significant for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p&lt; 0.05$</tex-math></inline-formula> , are evaluated from the input subband searched space and embedded them to various ensemble methods under multiboost strategy. Results are evaluated in terms of various parameters including accuracy, F-score, AUC, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> . Results show that the proposed approach is promising in classification and it achieves optimum individual accuracies of 97.10% and 97.90% in categorizing FATIGUE and REST states with F-score of 97.50%, AUC of 0.975, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> of 0.950. Comparison of the proposed method with the prior methods in the context of feature, accuracy, and modality profiles undertaken, indicates the effectiveness and reliability of the proposed method for real-world applications.

A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features

Parkinson's disease (PD), a neurodegenerative disorder characterized by rest tremors, muscular rigidity, and bradykinesia, has become a global health concern. Currently, a neurologist determines the diagnosis of Parkinson's disease by taking into account several factors. An automated decision-making system would enhance patient care and improve the outcomes for the patient. Biomarkers, such as electroencephalograms (EEGs), can aid in the diagnosis process as they have proven useful in detecting abnormalities in the brain. This study presents a novel algorithm for the automated diagnosis of Parkinson's disease from EEG signals using a flexible analytic wavelet transform (FAWT). First, these acquired EEG signals are preprocessed before decomposition into five frequency sub-bands based on the FAWT method. Several entropy parameters are computed from the decomposed sub-bands and ranked based on their significance level in detecting PD through analysis of variance (ANOVA). Various classifiers are used to identify appropriate feature sets, including support vector machines (SVM), logistics, random forests (RF), radial basis functions (RBF), and k-nearest neighbors (KNN). The proposed approach is evaluated using data collected from two centers in Malaysia (Dataset-I) and the United States (Dataset-II). In dataset-I, the KNN classifier produces accuracy, specificity, sensitivity, and area under the curve of 99%, 99.45%, 99.12%, and 0.991, respectively, while in dataset-II, these values are 95.85%, 95.88%, 96.14%, and 0.959. The proposed system would be extremely useful for neurologists during their diagnostic process, as well as for current clinical practices.

Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform

The COVID-19 epidemic has been causing a global problem since December 2019. COVID-19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is essential. The progression of COVID-19 lung illness has been demonstrated to be aided by chest imaging. The respiratory system is the most vulnerable component of the human body to the COVID virus. COVID can be diagnosed promptly and accurately using images from a chest X-ray and a computed tomography scan. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist venous entry, and pinpoint any new heart problems. The traditional and trending tools are physical, time-inefficient, and not more accurate. Many techniques for detecting COVID utilizing CT scan images have recently been developed, yet none of them can efficiently detect COVID at an early stage. We proposed a two-dimensional Flexible analytical wavelet transform (FAWT) based on a novel technique in this work. This method is decomposed pre-processed images into sub-bands. Then statistical-based relevant features are extracted, and principal component analysis (PCA) is used to identify robust features. After that, robust features are ranked with the help of the Student's t-value algorithm. Finally, features are applied to Least Square-SVM (RBF) for classification. According to the experimental outcomes, our model beat state-of-the-art approaches for COVID classification. This model attained better classification accuracy of 93.47%, specificity 93.34%, sensitivity 93.6% and F1-score 0.93 using tenfold cross-validation.

Sleep apnea detection using electrocardiogram signal input to FAWT and optimize ensemble classifier

Sleep apnea refers to a sleep disorder consist of inconsistent breathing during sleep for extensive duration of time. During this, one faces difficulty in breathing leading to loss of oxygenated blood circulation in human body. It leads to damage in hippocampus region of the brain. Many medical problems like hypertension and inducing type two diabetes are also common in patients. The early-stage detection of apnea can save someone from these severe conditions. This work introduces the automatic apnea detection method using electrocardiogram (ECG) signals. The ECG signals are analyzed with the help of flexible analytic wavelet transform (FAWT) which allows the conversion of non-stationary ECG into predictable wavelets. Features for events of apnea and non-apnea are extracted by these wavelets. The extracted features are checked for their statistical significance and then fed into different kinds of machine learning algorithms for apnea events detection. In tested algorithms, the optimized ensemble is obtained the best classification results. The proposed approach for apnea detection has better performance as compared to other existing same dataset works.

Automated Gearbox Fault Diagnosis Using Entropy-Based Features in Flexible Analytic Wavelet Transform (FAWT) Domain

Automated Gearbox Fault Diagnosis Using Entropy-Based Features in Flexible Analytic Wavelet Transform (FAWT) Domain

Improving the separability of drowsiness and alert EEG signals using analytic form of wavelet transform

Drowsiness is a state in which individual decision-making ability is declined. Drowsy driving is one of the key factors for worldwide accidents which claim many lives, causing injuries and permanent disability. Electroencephalogram (EEG) signals are the most common source used for the detection of drowsiness because of changes in neuron behavior. More hidden information can be obtained if it is decomposed into multi-components. Therefore, a flexible analytic wavelet transform (FAWT) based drowsiness detection is explored in this paper. FAWT decomposes a signal in multi-frequency subbands. Multiple features are extracted from these subbands and selected using statistical analysis. Different classification techniques are employed to separate drowsy and alert signals. An accuracy of 95.6%, a sensitivity of 95.2%, a specificity of 98.6%, a precision of 99.1%, and 96.1% of area under the curve are achieved with an extreme learning machine classifier. This shows that our developed method can find more hidden information which is helpful for accurate detection of drowsiness.

Bearing fault diagnosis based on flexible analytical wavelet transform and fuzzy entropy approach

Abstract For fault detection of bearings to avoid unexpected downtime in rotating machinery (RM), their monitoring is continuously required. Whenever a fault develops in any of the rotating machinery components, the characteristics vibration signature of such a mechanical system will change. Revealing such characteristics in a vibration signature is the main aim of any signal processing technique. Wavelet transforms and its variants have been widely used for their ability to perform under different non-stationarity nature of vibration signals. Among them, a new variant known as Flexible analytic wavelet transform (FAWT) enjoys attractive characteristics such as flexible time–frequency (TF) covering, better shifting property in which the same amount of input shifted corresponds to the same amount of shifted output, and adjustable periodic nature of the wavelet, providing appropriate shape and wavelet window to meet the weak fault component frequencies. After that, features are extracted using fuzzy entropy (FzEn) to determine the complexity of these vibration signals. After fault feature extraction by fuzzy entropy (FzEn), several state-of-the-art classifiers are used to validate the effectiveness of the proposed method. The results show that the proposed method is advantageous in identifying the fault type and severity of bearings.

Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images

Glaucoma is one of the leading causes of irreversible vision loss, it progresses gradually without easily noticeable symptoms. The detection of glaucoma in the early stage is crucial as it may help to decelerate the progress. The traditional instrument methods are manual, time-consuming and less accurate. Hence, the automated diagnosis of glaucoma is needed for detection of glaucoma in the early stage with high accuracy. The flexible analytic wavelet transform (FAWT) based novel method has been proposed for the classification of glaucoma stages. In the proposed method, FAWT has been used to decompose the preprocessed images into various sub-band images. Then, ReliefF and sequential box-counting (SBC) algorithms are applied to extract the various entropies and fractal dimension (FD) based features, respectively. Further, the extracted feature values are ranked using Fisher's linear discriminant analysis (LDA) dimensionally reduction. Finally, the higher rank features have been used for the classification of glaucoma stages using least squares-support vector machine (LS-SVM) classifier. The proposed method has been evaluated on publicly available large and diverse glaucoma database. The classification accuracy of the proposed method is 93.40% using tenfold cross-validation. The proposed method has demonstrated better performance for glaucoma classification as compared to the existing methods. The proposed method is ready to help the ophthalmologist in their daily screening for glaucoma detection.

Motor imagery EEG classification based on flexible analytic wavelet transform

Motor imagery electroencephalogram (MI-EEG) based brain-computer interface (BCI) is a burgeoning auxiliary means to realize rehabilitation therapy. One of the major concerns in MI-EEG based BCI is to have an accurate classification, and effective and fast feature extraction is the key to build a successful MI-EEG classification model. In this paper, a novel classification system for MI-EEG signals is proposed based on flexible analytic wavelet transform (FAWT). The filtered MI-EEG signals are firstly subjected to the FAWT to obtain sub-bands, and time-frequency features are calculated from the sub-bands. MDS is then adopted to reduce the dimension of the extracted features, and principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE) and Laplacian Eigenmaps (LE) are utilized as comparison. Finally, linear discriminant analysis (LDA) is utilized to complete the classification of left-hand (LH) and right-hand (RH) MI-EEG signals. The proposed method is experimentally validated on BCI Competition II Data Set III (BCI Dataset III) and BCI Competition III Data Set IIIb (BCI Dataset IIIb). As a result, the combined method of FAWT, MDS attains the maximal mutual information (MaI) of 0.95 and the maximum accuracy (ACC) of 94.29% using BCI Dataset III, and the mean of the maximal MaI steepness of 0.3740 using BCI Dataset IIIb. The proposed method yields better performance in comparison to the existing methods. Overall, the effectiveness of the proposed approach suggests that it can be a worthwhile and promising method for a MI-EEG based BCI system.

A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications

Background and Objective: Motor Imagery (MI) based Brain-Computer-Interface (BCI) is a rising support system that can assist disabled people to communicate with the real world, without any external help. It serves as an alternative communication channel between the user and computer. Electroencephalogram (EEG) recordings prove to be an appropriate choice for imaging MI tasks in a BCI system as it provides a non-invasive way for completing the task. The reliability of a BCI system confides on the efficiency of the assessment of different MI tasks.Methods: The present work proposes a new approach for the classification of distinct MI tasks based on EEG signals using the flexible analytic wavelet transform (FAWT) technique. The FAWT decomposes the EEG signal into sub-bands and temporal moment-based features are extracted from the sub-bands. Feature normalization is applied to minimize the bias nature of classifier. The FAWT-based features are utilized as inputs to multiple classifiers. Ensemble learning method based Subspace k-Nearest Neighbour (kNN) classifier is established as the best and robust classifier for the distinction of the right hand (RH) and right foot (RF) MI tasks.Results: The sub-band (SB) wise features are tested on multiple classifiers and best performance parameters are obtained using the ensemble method based subspace kNN classifier. The best results of parameters are obtained for fourth SB as accuracy 99.33%, sensitivity 99%, specificity 99.6%, F1-Score 0.9925, and kappa value 0.9865. The other sub-bands are also attained significant results using subspace KNN classifier.Conclusions: The proposed work explores the utility of FAWT based features for the classification of RH and RF MI tasks EEG signals. The suggested work highlights the effectiveness of multiple classifiers for classification MI-tasks. The proposed method shows better performance in comparison to state-of-arts methods. Thus, the potential to implement a BCI system for controlling wheelchairs, robotic arms, etc.

Complex-valued distribution entropy and its application for seizure detection

Embedding entropies are powerful indicators in quantifying the complexity of signal, but most of them are only applicable for real-valued signal and the phase information is ignored if the analyzed signal is complex-valued. To assess the complexity of complex-valued signal, a new entropy called complex-valued distribution entropy (CVDistEn) was first proposed in this study. Two rules, namely equal width criterion and equal area criterion, were employed to demarcate the complex-valued space and two kinds of CVDistEn, i.e., CVDistEn1 and CVDistEn2 were raised. Furthermore, two novel feature extraction methods: (1) flexible analytic wavelet transform (FAWT)-based CVDistEn1 and logarithmic energy (LE) (FAWTC1L), (2) FAWT-based CVDistEn2 and LE (FAWTC2L) were subsequently put forward to characterize the interictal and ictal EEGs. Fuzzy k-nearest neighbors (FKNN) classifier was finally employed to classify these two types of EEGs automatically. Experiment results show the fusion method of FAWTC1L and FKNN leads to the best accuracies (ACCs)/Matthews correlation coefficients (MCCs) of 99.99%/99.97% and 100%/100% for Bonn and Neurology & Sleep Centre EEG datasets, respectively, while the other fusion scheme of FAWTC2L and FKNN results in the highest ACCs/MCCs of 99.97%/99.93% and 99.94%/99.89% for the same datasets. The proposed methods outperform other entropy-related seizure detection schemes and most of state-of-the-art techniques, they provide another new way for automated seizure detection in EEG.

Automatic focal and non-focal EEG detection using entropy-based features from flexible analytic wavelet transform

Surgical treatment is one of the most important methods to cure or control drug-resistant epilepsy, and preoperative localization of epileptic lesions plays an important role in the success of a surgery. Given that the manual diagnosis takes time and effort, an automatic detection system is needed to aid clinical diagnosis. Therefore, in the present study, a new automatic focal electroencephalogram (EEG) detection algorithm combining flexible analytic wavelet transform (FAWT) with entropies was put forward. The differential focal (F) and non-focal (NF) EEG signals were decomposed into 15-level sub-bands using FAWT, and this was followed by computing log energy entropy (LEE) and fuzzy distribution entropy (fDistEn) of the detail coefficients of 15 sub-bands and the differential EEG signal. Kruskal–Wallis one-way analysis of variance (ANOVA) was adopted to select the statistically significant features, and five classifiers including general regression neural network (GRNN), support vector machine (SVM), least squares support vector machine (LS-SVM), K-nearest neighbor (KNN), and fuzzy K-Nearest neighbors (fKNN) were then used to verify the effectiveness of the selected features. The proposed methodology was tested on the Bern Barcelona database, and a maximum accuracy of 94.80 % was achieved in distinguishing F and NF EEG signals via LS-SVM classifier. The results suggest that the proposed method is a valuable approach to aid clinicians in locating the epileptic focus in practical application.

Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed

Non-stationary vibration signals of a gearbox under varying speed display complicated modulations, which lead to intense sidebands thereby resulting in difficulty to identify the presence of a fault. Variational mode decomposition (VMD) being highly adaptive, effective in attenuating mode-mixing problem, low computational time requirement and therefore it is suitable to decompose a modulated multi-component non-stationary gearbox vibration signal. This research work thus utilizes the merits of VMD for demodulation and to diagnose localized gear tooth faults under real-time speed variation. In the present study, the vibration signals of a gearbox for different faults were acquired under complete speed variation (i.e., run-up, random fluctuation, and coast-down). To identify the symptoms confirming the presence of faults, the vibration signals were decomposed by VMD thereby demodulating the raw vibration signal. The decomposed VMFs exhibited the presence of transients due to faults and were analyzed statistically. To state the effectiveness of VMD in exhibiting fault by extracting fault transients, the performance of VMD was compared with the performance of recently developed wavelet based empirical wavelet transform (EWT) and flexible analytic wavelet transform (FAWT). The fault detection performance of VMD outperforms EWT as demonstrated by simulated signal and validated by the experimental investigations. FAWT was found ineffective in decomposing the vibration signal under varying speed.

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis. Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function. ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset. ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.

Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals

Human emotion is a physical or psychological process which is triggered either consciously or unconsciously due to perception of any object or situation. The electroencephalogram (EEG) signals can be used to record ongoing neuronal activities in the brain to get the information about the human emotional state. These complicated neuronal activities in the brain cause non-stationary behavior of the EEG signals. Thus, emotion recognition using EEG signals is a challenging study and it requires advanced signal processing techniques to extract the hidden information of emotions from EEG signals. Due to poor generalizability of features from EEG signals across subjects, recognizing cross-subject emotion has been difficult. Thus, our aim is to comprehensively investigate the channel specific nature of EEG signals and to provide an effective method based on flexible analytic wavelet transform (FAWT) for recognition of emotion. FAWT decomposes the EEG signal into different sub-band signals. Furthermore, we applied information potential to extract the features from the decomposed sub-band signals of EEG signal. The extracted feature values were smoothed and fed to the random forest and support vector machine classifiers that classified the emotions. The proposed method is applied to two different publicly available databases which are SJTU emotion EEG dataset and database for emotion analysis using physiological signal. The proposed method has shown better performance for human emotion classification as compared to the existing method. Moreover, it yields channel specific subject classification of emotion EEG signals when exposed to the same stimuli.