Tunable Q-factor Wavelet Transform Research Articles

Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals

Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3 rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals

AbstractElectroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.

A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

Modeling and sparsity-promoting separation of wind turbine noise in common-shot gathers

In land seismic acquisition, the quality of common-shot gathers is severely degraded by wind turbine noise (WTN) when wind turbines are operating continuously in survey areas. The high-amplitude WTN overlaps or even completely submerges the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are observed: (1) it is periodic with nearly constant frequencies over time, (2) it is coherent but exhibits different apparent velocities in space, and (3) it has relatively narrow bands with varying central frequencies. The first feature enables WTN to distort signals from shallow to deep, whereas the latter two features make traditional methods that separate noise and signals based on velocity and frequency differences less effective. To suppress the WTN, we first analyze its formation and propagation mechanism and then develop a WTN simulation model to validate the presented mechanism. Based on our analysis of WTN and signals, we consider common-shot gathers as the linear superpositions of periodic WTN and relatively broadband signals (referred to as low-oscillatory signals). This additive mixture aligns with the feasibility premise of morphological component analysis (MCA). Finally, based on MCA theory, we develop a sparsity-promoting separation method to suppress WTN in common-shot gathers. To implement our separation method, we construct two dictionaries using the tunable Q-factor wavelet transform (TQWT) and the discrete cosine transform (DCT). TQWT and DCT can sparsely represent the oscillating waves (signals) and periodic waves (WTN), respectively. This work contributes to the existing knowledge of WTN separation by modeling the periodicity of WTN and the low-oscillatory behavior of a signal, rather than relying on velocity or frequency differences. Our method is tested on synthetic and field data, and both tests demonstrate its effectiveness in separating WTN and preserving signals.

Wide-frequency-range vibration positioning based on adaptive TQWT for long-distance asymmetric interferometer sensor

The asymmetric dual Mach–Zehnder interferometer (ADMZI) based vibration sensor effectively extends the sensing distance, however, the asymmetry seriously deteriorates the positioning accuracy. In this paper, an adaptive tunable Q-factor wavelet transform (TQWT) based long-distance asymmetric sensing system is proposed and experimentally demonstrated for the positioning of wide-frequency-range knocking vibration signals. The TQWT can extract the time-frequency features of the non-stationary signals by performing multi-scale decomposition. Firstly, the positioning method adaptively determines the decomposition levels by analyzing the power spectrum of the vibration signals, which not only effectively suppresses the effect of low-frequency noise, but also accurately extracts the main time-frequency variation characteristics of the vibration signals with various bandwidths. Then, the time delay between the time-frequency characteristics is obtained using a cross-correlation algorithm, and the vibration position is demodulated. Experimental results show that the method can accurately locate vibration signals with bandwidths of 10–80 kHz at a sensing length of 125 km. For high-frequency, strong vibration signals, the standard deviation of the positioning is around 35.1 m. For low-frequency, weak signals, the proposed method can still achieve effective positioning compared with the traditional approaches with a standard deviation of approximately 238.1 m. The proposed scheme can significantly improve the applicability of the asymmetric interferometer based vibration sensing system.

PPG SİNYALLERİNİN TQWT TABANLI AYRIŞTIRILMASI YOLUYLA KAN BASINCI VE KALP ATIŞ HIZI TAHMİNİ

Photoplethysmography (PPG) signals are getting more popular and promising for medical applications because of the non-invasive, fast, and simple recording techniques. Using PPG signals for monitoring the blood pressure (BP) and heart rate (HR) levels instead of traditional invasive and cuff-based measurement techniques is possible and continuous tracing of BP and HR levels can be accomplished with high measurement accuracies. These developments are very important and helpful, especially for people suffering from high tension and cardiac problems. In this study, we propose to use Tunable Q-factor Wavelet Transform (TQWT) for decomposing the PPG signals into sub-signals and extracting some statistical features from each of the sub-signals and main signal. Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Machines (SVM) algorithms are employed to estimate diastolic blood pressure (DBP), systolic blood pressure (SBP), and heart rate (HR) values. PPG signals, DBP, SBP, and HR values which were measured with traditional methods were obtained from the open dataset of Guilin People’s Hospital of China. This dataset includes information of 219 individuals. Each machine learning method was applied to the features separately, and the results of the regression analysis were interpreted by using the error rates and correlations between the actual and estimated values. Results show that the RF algorithm is more successful than ANN and SVM for the estimation of DBP, SBP, and HR levels.

Power allocation strategy for fuel cell distributed drive electric tractor based on adaptive multi-resolution analysis theory

Fuel cell distributed drive electric tractor (FCDET) provided a novel pathway for the development of green agricultural machinery technology. However, FCDET faced problems such as low traction efficiency, short endurance, and high hydrogen consumption. Relevant studies have shown that reasonable and effective power allocation strategy can improve the efficiency and reduce the energy consumption of fuel cell systems. In this paper, a drive power allocation strategy based on adaptive multi-resolution analysis theory (AMRA) was proposed. The strategy could realize effective decoupling between various energy sources, reduce the problems of frequent start-stop and large power fluctuation for fuel cells. We started with an equivalent circuit model of the fuel cell system, a total efficiency solution model, an energy dissipation model, and a drive motor response model. The first-time reconstruction strategy was based on Tunable Q-factor Wavelet Transform (TQWT) to obtain a subsequence that responds to the oscillatory characteristics of the power signal. The second-time reconstruction strategy took the low-frequency subsequence through Variational Mode Decomposition (VMD) into a number of discrete sub-signals with special sparse properties. Meanwhile, the Sparrow Search Algorithm (SSA) was used to obtain the optimal combined values of the modal decomposition layers and the quadratic penalty factor in the VMD on real time. Finally, the second-time decomposed subsequences and sub-signals were reconstructed according to the frequency characteristics, and the reconstructed power signals were redistributed among the individual energy sources. In order to validate the proposed power allocation strategy, we used the ET504-H prototype as a research object. The power information of plowing and transportation operating conditions was collected in Mengjin test base of China YTO Group. The MATLAB/Simulink-PXI joint simulation platform was set up and the driving motor bench test was carried out in the New Energy Key Laboratory of Henan Province. The results show that the AMRA-based FCDET drive power allocation strategy can effectively improve the energy utilization of the fuel cell system and the economy of the whole tractor operating unit. Compared to the power-following strategy under plowing and transportation conditions, the average efficiency of the fuel cell system was improved by 8.3 % and 3.3 %, the equivalent hydrogen consumption was reduced by 35.6 % and 43.89 %, respectively. The average efficiency of the drive motor was improved by 2.06 % and 2.08 %, the total energy consumption was reduced by 3.73 % and 2.6 %, respectively. This study can provide a theoretical foundation and a novel technical approach for the development of FCDET control system.

ResNet-Based Parkinson's Disease Classification

Parkinson&#x0027;s Disease (PD) is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. The symptoms usually begin gradually and get worse over time. Early diagnosis is very important, because treatments are more effective and easier to perform during the early stages of PD. However, early diagnosis is challenging, because the symptoms start gradually, and at the early stages, they are not very noticeable. In this paper, we propose a method that uses ResNet50, a Residual Network that has 50 layers, to help diagnosis PD. The data used is a collection of frequency features acquired by applying spectral analysis strategies to the speech recordings of the patient. We then convert the frequency features into a 2-dimensional heat map. This heat map is passed to ResNet50, which predicts whether the patient has PD or not. We have conducted experiments and compared the accuracy with several state-of-art methods. The results have demonstrated the feasibility and robustness of the proposed method. <i>Impact Statement</i> &#x2014; Parkinson&#x0027;s Disease (PD) is the most common movement disorder with more than 10 million PD patients worldwide. In the United States, around 60,000 are diagnosed with PD every year. Treatments are more effective at early stage, so early diagnosis is very important. A simple and effective classifier to diagnose PD patients is therefore crucial for doctors and patients. For the last decade, Convolutional Neural Networks (CNN) are the dominant approach for image classifications. Our proposed method benefits from the versatility, solid performance of pretrained CNN architecture by converting the frequency domain features of patients' speech recordings into heat maps. Our method achieved an accuracy of 90.7&#x0025; in diagnosis of PD by only using Tunable Q-Factor Wavelet Transform (TQWT) features, outperforming other state-of-art methods. Therefore, the conversion from non-graphical features to heat maps provides simple, accurate deep network for PD diagnosis that is fast to train, and only requires TQWT features.

Oscillatory time–frequency concentration for adaptive bearing fault diagnosis under nonstationary time-varying speed

Being a promising post-processing solution, application of time–frequency concentration (TFC) suffers in an adaptive fault diagnosis scenario due to non-adaptive extraction of bearing fault signature from noise-associated vibration signals under nonstationary time-varying speed. Aiming at solving this problem, a novel method called oscillatory time–frequency concentration (OTFC) is proposed by oscillation based adaptive extraction of time-varying bearing fault signature with the help of tunable Q factor wavelet transform (TQWT). OTFC can effectively suppress unwanted noise while adaptively reveals the time-varying fault characteristic frequency (FCF) and corresponding harmonics with excellent readability. Two distinct numerical simulations and experimental case studies have been utilized to verify the performance of the proposed OTFC. The results show that the proposed OTFC not only performs better than the conventional TFC method namely synchrosqueezing wavelet transform (SST) but also shows superior performance than traditional continuous wavelet transform (CWT) and advanced TFC method namely multitaper synchrosqueezing wavelet transform (MST).

A novel feature extraction method based on TQWT and instantaneous energy variation for Parkinson’s disease detection

Voice disorder is an early symptom of Parkinson’s disease (PD), and extracting effective voice features is of great significance for PD detection. The voice features based on tunable Q-factor wavelet transform (TQWT) have promising performance in PD detection only using conventional energy and entropy features. To further develop the potential of TQWT, a novel feature extraction method named instantaneous energy variation based on TQWT (IEV-TQWT) is proposed. Firstly, TQWT is adopted to perform multi-scale decomposition to obtain the oscillation information of the voice signals. Next, IEV-TQWT features are obtained by calculating instantaneous energy variation vectors from the spectrograms of sub-signals. Finally, the IEV-TQWT features are input to multiple classifiers to verify the PD classification performance. The results have shown that IEV-TQWT features with explicit physical information have a significant improvement in classification performance. In addition, IEV-TQWT features outperform other advanced features on the CPPDD dataset and reach mainstream level on SPDD dataset, indicating that IEV-TQWT features can be used as an effective assisted diagnosis tool of PD.

A pragmatic approach of Parkinson disease detection using hybrid case-based reasoning neuro-fuzzy classification system over Mobile edge computing

Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier.

BP19: Tek Boyutlu İkili Modele Dayalı Doğru Bir Sesli Şiddet Tespit Modeli

Audio violence detection (AVD) is a hot-topic research area for sound forensics but there are limited AVD researches in the literature. Our primary objective is to contribute to sound forensics. Therefore, we collected a new audio dataset and proposed a binary pattern-based classification algorithm. &#x0D; Materials and method: In the first stage, a new AVD dataset was collected. This dataset contains 301 sounds with two classes and these classes are violence and nonviolence. We have used this dataset as a test-bed. A feature engineering model has been presented in this research. One-dimensional binary pattern (BP) has been considered to extract features. Moreover, we have applied tunable q-factor wavelet transform (TQWT) to generate features at both frequency and space domains. In the feature selection phase, we have applied to iterative neighborhood component analysis (INCA) and the selected features have been classified by deploying the optimized support vector machine (SVM) classifier. &#x0D; Results: Our model achieved 97.01% classification accuracy on the used dataset with 10-fold cross-validation.&#x0D; Conclusions: The calculated results clearly demonstrated that feature engineering is the success solution for violence detection using audios.&#x0D; .

An automated accurate sound-based amateur drone detection method based on skinny pattern

Nowadays, the popularity of amateur drones (ADr) has increased marginally, and many research areas, such as machine learning, security systems, and the military industry, have been interested in ADrs. The prime goals of this paper are to present a new sound dataset for ADr detection and present an accurate classification model for ADr detection. This research proposes a novel classification model using the proposed skinny pattern and iterative neighborhood component analysis (INCA) feature selector using ADr sounds. The skinny pattern is a new nonlinear pattern that uses the skinny cipher's substitution box (S-Box). Tunable Q-factor wavelet transform (TQWT) and skinny pattern are used together to extract features in multilevel. The most informative features from the generated features are selected using the INCA selector. 16 classifiers were used in five categories to illustrate the general success of the presented model, and these categories are decision tree (DT), discriminant (D), support vector machine (SVM), k nearest neighborhood (kNN), and ensemble classifiers (EC). An ADr sound dataset was collected, and the presented skinny pattern and INCA-based model are applied to the gathered sound dataset to implement the presented model. The best classification accuracy was calculated as 99.72% using Fine kNN. The proposed model was also compared to other ADr detection/classification models, and it outperformed. These obtained results demonstrate the success of the skinny pattern and INCA-based ADr detection model.

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction.

K-complex detection traditionally relied on expert clinicians, which is time-consuming and onerous. Various automatic k-complex detection-based machine learning methods are presented. However, these methods always suffered from imbalanced datasets, which impede the subsequent processing steps. In this study, an efficient method for k-complex detection using electroencephalogram (EEG)-based multi-domain features extraction and selection method coupled with a RUSBoosted tree model is presented. EEG signals are first decomposed using a tunable Q-factor wavelet transform (TQWT). Then, multi-domain features based on TQWT are pulled out from TQWT sub-bands, and a self-adaptive feature set is obtained from a feature selection based on the consistency-based filter for the detection of k-complexes. Finally, the RUSBoosted tree model is used to perform k-complex detection. Experimental outcomes manifest the efficacy of our proposed scheme in terms of the average performance of recall measure, AUC, and F10-score. The proposed method yields 92.41 ± 7.47%, 95.4 ± 4.32%, and 83.13 ± 8.59% for k-complex detection in Scenario 1 and also achieves similar results in Scenario 2. The RUSBoosted tree model was compared with three other machine learning classifiers [i.e., linear discriminant analysis (LDA), logistic regression, and linear support vector machine (SVM)]. The performance based on the kappa coefficient, recall measure, and F10-score provided evidence that the proposed model surpassed other algorithms in the detection of the k-complexes, especially for the recall measure. In summary, the RUSBoosted tree model presents a promising performance in dealing with highly imbalanced data. It can be an effective tool for doctors and neurologists to diagnose and treat sleep disorders.

Cathode Position Detection in a Transferred Arc Plasma Using Artificial Neural Network

In a transferred arc plasma system, the position of the cathode is difficult to detect during the smelting process as it remains inside the cylindrical anode. Real-time and accurate cathode position detection leads to efficient smelting operation with optimal use of electrical energy. In this article, a machine learning technique is proposed to accurately detect the position of the cathode in a direct current (DC) transferred arc plasma system. The measured voltage signal sampled at 20 kHz is processed using a tunable Q-factor wavelet transform (TQWT) followed by statistical features extraction and a machine learning algorithm to provide accurate cathode position information. Two different machine learning algorithms are used in this work, namely, single hidden layer neural network (SHLNN) and single-layer extreme learning machine (SELM). The output of these machine learning algorithms provides accurate position information and is also compared to the traditional voltage-related position information. The experimental signal of a 30-kW DC plasma system and cathode position detection results is shown.

SEMG Signal-Based Lower Limb Movements Recognition Using Tunable Q-Factor Wavelet Transform and Kraskov Entropy

BackgroundThe recognition of lower limb movement has a wide range of applications in rehabilitation training, wearable exoskeleton control, and human activity monitoring. Surface electromyography (sEMG) signals can directly reflect the intention of human movement and can be used as the source of lower limb movement recognition. Literature reports have shown that extracting features from sEMG signals is the core of human movement recognition based on sEMG signals. However, how to effectively extract features from the sEMG signal of the lower limbs affected by body gravity is a difficult problem for the recognition of lower limb movement based on the sEMG signal. ObjectivesThe main objective of this paper is to propose an efficient lower limb movement recognition model based on sEMG signals to accurately recognize the four lower limb movements. Methods and resultsWe proposed a novel method of lower limbs movements recognition based on tunable Q-factor wavelet transform (TQWT) and Kraskov entropy (KrEn). Firstly, the sEMG signals of four different lower limb movements from twenty subjects were recorded by seven wearable sEMG signal sensors, and the recorded sEMG signals were denoised by multi-scale principal component analysis (MSPCA). Then, the denoised sEMG signal is decomposed into multiple sub-band signals by TQWT and the KrEn feature is extracted from each sub-band signal. Next, the representative features are selected from the extracted KrEn features by the minimum redundancy maximum relevance (mRMR) feature selection method. Finally, the four lower limb movements are recognized by three machine learning classifiers. Besides, to improve the recognition performance, a majority voting (MV) technology is proposed for the post-processing of decision flow. Experimental results show that the combination of TQWT, KrEn, and MV technology achieved the average recognition accuracy of 98.42% using the linear discriminant analysis (LDA) classifier. ConclusionThe method proposed in this paper can recognize lower limb movements with high accuracy. Compared with existing methods, this method is more advanced and accurate, indicating that it has great application potential in rehabilitation training, wearable exoskeleton control, and daily activity monitoring.

Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection.

Electroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet transform (TQWT), which is a constant-Q transform that is easily invertible and modestly oversampled, has been successfully used for feature extraction of EEG signals. Because the constant-Q is set in advance and cannot be optimized, further applications of the TQWT are restricted. To solve this problem, the revised tunable Q-factor wavelet transform (RTQWT) is proposed in this paper. RTQWT is based on the weighted normalized entropy and overcomes the problems of a nontunable Q-factor and the lack of an optimized tunable criterion. In contrast to the continuous wavelet transform and the raw tunable Q-factor wavelet transform, the wavelet transform corresponding to the revised Q-factor, i.e., RTQWT, is sufficiently better adapted to the nonstationary nature of EEG signals. Therefore, the precise and specific characteristic subspaces obtained can improve the classification accuracy of EEG signals. The classification of the extracted features was performed using the decision tree, linear discriminant, naive Bayes, SVM and KNN classifiers. The performance of the new approach was tested by evaluating the accuracies of five time-frequency distributions: FT, EMD, DWT, CWT and TQWT. The experiments showed that the RTQWT proposed in this paper can be used to extract detailed features more effectively and improve the classification accuracy of EEG signals.

Exploiting Tunable Q-Factor Wavelet Transform Domain Sparsity to Denoise Wrist PPG Signals

Wearable health monitoring devices based on photoplethysmogram (PPG) can estimate valuable physiological parameters and are often regarded as a touchstone for indicating the cardiovascular status of a person. Severe motion artifacts are generally observed in the acquired PPG signals from the biosensors in the wearables when the PPG acquisition is performed during different physical activities. Classical filters can remove thermal noise and electromagnetic interference from the raw PPG signals, but they fail to handle the motion artifacts that cover a wider range of signal frequencies. This work introduces a new motion artifact removal scheme that exploits the sparsity of PPG signals in the tunable-Q factor wavelet transform (TQWT) domain using the basis pursuit denoising (BPDN) scheme. The proposed scheme deftly removes noise while preserving the original morphology of the PPG signal without using any reference accelerometer sensor data. In particular, a regularized SALSA-based BPDN algorithm is employed for sparse recovery, which utilizes a soft thresholding approach to denoise the noisy PPG signals. TQWT can be tuned according to the oscillatory behavior of the raw PPG signal. At the same time, BPDN exploits the sparsity of the wavelet coefficients to yield an artifact-free reconstruction of PPG. Further, heart rate estimation is performed using the denoised PPG signal with support vector regression to demonstrate the clinical information preservation during the proposed denoising process. The proposed denoising scheme is evaluated on two publicly available databases, and an overall minimum mean absolute error (MAE) of 1.03 and 0.76 beats per minute (bpm) is achieved. The suggested denoising approach has demonstrated better performance with the lowest MAE compared to the previously reported methods.

Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset.

Emotion identification is an essential task for human-computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time-frequency plots using the synchrosqueezed wavelet transform. Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines, and neural networks are used for the automated classification of angry, neutral, happy, and sad emotions. Our developed model obtained an average accuracy of 90.7% using HOG features and a KNN classifier with a tenfold cross-validation strategy.

EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network

Electroencephalogram (EEG)-based emotion recognition has gained high attention in Brain-Computer Interfaces. However, due to the non-linearity and non-stationarity of EEG signals, it is difficult to analyze and extract effective emotional information from these signals. In this paper, a novel EEG-based emotion recognition framework is proposed, which includes Tunable Q-factor Wavelet Transform (TQWT)-feature extraction method, a new spatiotemporal representation of multichannel EEG signals and a Hybrid Convolutional Recurrent Neural Network (HCRNN). According to the oscillation behavior of signals, TQWT is first employed to decompose EEG into several sub-bands with stationarity characteristics. The features of mean absolute value and differential entropy are extracted from these sub-bands and named as TQWT-features. Next, the TQWT-features are transformed into TQWT-Feature Block Sequences (TFBSs) as the spatiotemporal representation to train the deep model. Then, the HCRNN model is introduced, which is fused by a lightweight Convolutional Neural Network (CNN) and a recurrent neural network with Long-Short Term Memory (LSTM). CNN is utilized to learn the spatial correlated context information of TFBSs, and LSTM is further adopted to capture the temporal dependency from CNN’s outputs. Finally, extensive subject-dependent experiments are carried out on SEED dataset to classify positive, neutral, negative emotional states. The experimental results demonstrate that the TQWT-features in high-frequency sub-bands are effective for EEG-based emotion recognition tasks. The recognition accuracy of HCRNN with TFBSs achieves superior performance (95.33 ± 1.39 %), which outperforms state-of-the-art deep learning models.