Wavelet Entropy Features Research Articles

Biorthogonal wavelet based entropy feature extraction for identification of maize leaf diseases

Crop disease is considered as a major constraint to both food quality and production. Even in this era of precision agriculture, the lacking of compulsory infrastructure has made rapid identification of crop diseases quite hard in numerous regions around the world. In this paper, we introduced a new method based on biorthogonal wavelet transform (BWT) to identify prime maize leaf diseases. We performed biorthogonal wavelet decomposition and pixel wise morphological operation to segment the maize leaf lesion from input image. For feature extraction, by applying 2-D biorthogonal wavelet transform (BWT) at multiple levels we proposed a novel method to extract colour channel wise wavelet entropy features by investigating discriminatory potential of three different biorthogonal wavelet filters (bior3.3, bior3.5, and bior3.7). The effectiveness of our extracted features were evaluated by employing five different classifiers and obtaining 95.78% overall identification accuracy with 10-fold cross validation. All the materials related our study can be found at: https://github.com/BadhanMazumder/BiorthogonalWavelet_MaizeDiseaseDetection.git.

Self-Study System Assessment of Spoken English considering the Speech Scientific Computing Knowledge Assessment Algorithm

For the purpose of effectively improving the assessment accuracy of spoken English self-study quality of English learners, a speech scientific computing knowledge assessment algorithm is introduced into the assessment of the self-study system of spoken English. Through the combination of spoken English speech assessment and accuracy detection, a DSP-based self-study assessment system for spoken English is designed. This system is mainly divided into two parts: voice signal processing and hardware circuit design. The features of spoken English self-study speech are extracted based on the multilayer wavelet feature conversion method, and the English pronunciation is detected and analyzed by adaptive filtering based on the characteristic values obtained. The corresponding wavelet entropy features of spoken English self-study speech are automatically assessed and assessed for the self-study quality. Finally, a practical case is analyzed, and the results indicate that the system proposed in this paper has high accuracy and excellent stability in assessing the quality of self-study in spoken English. Hence, it is of practical value in self-study assessment of spoken English.

EEG-Based Emotion Recognition With Haptic Vibration by a Feature Fusion Method

Emotion recognition based on electroencephalogram (EEG) signals has been one of the most active research topics of affective computing. In previous studies of emotion recognition, the selection of stimulus sources was usually focused on single stimuli, such as visual or auditory. In this work, we propose a novel emotional stimulation scheme that synchronizes haptic vibration with audiovisual content to form a mixed sense of visual–auditory–haptic to trigger emotions. Fifteen subjects were recruited to watch the four kinds of emotional movie clips (happiness, fear, sadness, and neutral) with haptic or not, and their EEG signals were collected simultaneously. The power spectral density (PSD) feature, differential entropy (DE) feature, wavelet entropy (WE) feature, and brain function network (BFN) feature were extracted and fused to reflect the time–frequency–spatial domain of emotional EEG signals. The t-distributed stochastic neighbor embedding (t-SNE) was utilized for dimensionality reduction and feature selection. In addition, the fusion features are classified by the stacking ensemble learning framework. The experimental results show that the proposed haptic vibration strategy can enhance the activity of emotion-related brain regions, and the average classification accuracy was 85.46%.

Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD)

Diagnostic ambiguity between chronic pulmonary diseases like asthma and Chronic Obstructive Pulmonary Disease (COPD) is very high, as they exhibit similar symptoms, which is the factor responsible for misdiagnosis, leading to heavy deaths every year. To prevent misdiagnosis, some useful work is highly required. This article presents the implementation of a computerized lung sound (LS) based method to classify asthma and COPD cases.The study is conducted on 80 asthma, 80 COPD and 80 healthy LSs.The LS denoising is carried out using empirical mode decomposition (EMD), Hurst analysis and spectral subtraction method. Wavelet entropy (WE) and wavelet packet energy (WPE) features of LS’s are extracted. Various classifiers like support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and discriminant analysis (DA) are accessed to classify healthy, COPD and asthma using WE and WPE features of LS to produce better outcomes. Using the proposed algorithm, the study discriminates between healthy, asthma and COPD cases based on LS with a considerable classification accuracy of 99.3% using the decision tree (DT) classifier. Thus, the study confirms the successful differentiation of asthma and COPD based on LS. Future endeavours will be based on the validation of this algorithm to distinguish the real-time LS data acquired from asthmatic and COPD patients.

Evolution of Entropy in Art Painting Based on the Wavelet Transform.

Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their complexity and energy. Wavelet energy entropy is a feature that can characterize rich information in images, and this is the first study to introduce this feature into aesthetic analysis of art paintings. This study shows that the process of entropy change coincides with the development process of art painting. Further, the experimental results demonstrate an important change in the evolution of art painting, and since the rise of modern art in the twentieth century, the entropy values in painting have started to become diverse. In comparison with Western paintings, Eastern paintings have distinct low entropy characteristics in which the wavelet entropy feature of the images has better results in the machine learning classification task of Eastern and Western paintings (i.e., the F1 score can reach 97%). Our study can be the basis for future quantitative analysis and comparative research in the context of Western and Eastern art aesthetics.

Fatigue Detection in SSVEP-BCIs Based on Wavelet Entropy of EEG

Among various types of brain computer interfaces (BCIs), steady state visually evoked potential (SSVEP) based BCIs can provide high information transfer rate (ITR), however the users could suffer serious fatigue that may induce discomfort, health hazards and deterioration of system performance. To overcome the fatigue obstacle, the first step is to detect the fatigue accurately, reliably and quickly. This paper proposes an approach based on the wavelet entropy of the measured EEG to fatigue detection in real time when using an SSVEP-BCI. Specifically, the wavelet analysis is first applied to the EEG, resulting in the approximation and detail components at different levels. The sample entropy values of these components are then calculated to generate features for classification. Experimental results identified the entropy of the lower frequency components (0 – 4.6875Hz) as the most important feature. The proposed wavelet entropy improved the fatigue detection accuracy to 87.7% from 65.1% by the traditional entropy method, when distinguishing subjects’ mental states between alert (before task) and fatigue (after task). Furthermore, the detection accuracy based on the state of art multiple conventional fatigue indices can be improved from 91.9% to 96.5% by replacing the delta band amplitude with the new wavelet entropy feature.

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms.

Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals.

The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank

Hypertension (HPT) is a serious risk factor for cardiovascular disease and if not controlled in the early stage, can lead to serious complications. Long-standing HPT can induce heart muscle hypertrophy which will be reflected on electrocardiography (ECG). However, early stage of HPT may have no clinically discernible ECG perturbations, and is difficult to diagnose manually from the standard ECG. Hence, we propose an automated ECG based system that can automatically detect the ECG changes in the early stages of HPT. This work is based on ECG signals obtained from 139 HPT patients (SHAREE database) and 52 healthy subjects (PTB database). The ECG signal is non-stationary with relatively short duration, and rhythmic. Two-band optimal bi-orthogonal wavelet filter bank (BOWFB) and machine learning are used to automatically diagnose low, high-risk hypertension, and healthy control using ECG signals. Five-level wavelet decomposition is used to produce six sub-bands (SBs) from each ECG signal using BOWFB. Sample and wavelet entropy features are calculated for all six SBs. The features calculated SBs are fed to the k-nearest neighbor (KNN), support vector machine (SVM), and ensemble bagged trees (EBT) classifiers. In this work, we have obtained the highest average classification accuracy of 99.95% and area under the curve of 1.00 using EBT classifier in classifying healthy control (HC), low-risk hypertension (LRHPT) and high-risk hypertension (HRHPT) classes with ten-fold cross validation strategy. Hence the developed system can be used in clinics, or even in remote detection of HPT stages using ECG signals.

Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm

In order to improve the ability of software dynamic image fault detection, a software dynamic image fault recovery path detection algorithm based on a fuzzy control algorithm is proposed. A software dynamic image fault signal model of a software dynamic image fault was constructed by adopting an embedded feature extraction and a fuzzy control algorithm, and the dynamic image fault signal of the embedded software under the multi-load was subjected to frequency spectrum decomposition and blind source separation. The method comprises the following steps: (1) carrying out noise reduction processing on a software dynamic image fault signal by adopting a multi-dimensional wavelet decomposition method, (2) carrying out wavelet entropy feature extraction on the software dynamic image fault signal of the noise reduction output, and (3) combining the wavelet structural feature recombination method to carry out the recombination of the software dynamic image fault feature. The high-order spectral characteristic of the software dynamic image fault signal was extracted and the high-order spectral characteristic of the extracted embedded software dynamic image fault recovery path was automatically matched, and the automatic identification and detection of the fault part of the software dynamic image was realized.

Automatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning.

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

Performance evaluation of English learning through computer mode using neural network and AI techniques

Learning performance evaluation is an important part of computer English teaching. Through evaluation, students can understand whether they have reached the learning goals of a lesson, and what are their shortcomings, so that they can continuously improve and improve themselves against these shortcomings. Combining actual needs, based on neural network and artificial intelligence technology, this paper constructs a computer English learning performance evaluation model based on machine learning. The computer English learning performance evaluation system in this paper is constructed on the basis of auditory characteristics and introduces a wavelet entropy feature based on the best tree wavelet packet decomposition, and it is applied to the establishment of an adaptive model. Moreover, this paper uses controlled experiments to analyze the model performance and combines mathematical statistics to visually display the model effect. From the research results, it can be seen that the performance of the model constructed in this paper basically meets the expected requirements and actual needs.

Identity Vector Extraction by Perceptual Wavelet Packet Entropy and Convolutional Neural Network for Voice Authentication.

Recently, the accuracy of voice authentication system has increased significantly due to the successful application of the identity vector (i-vector) model. This paper proposes a new method for i-vector extraction. In the method, a perceptual wavelet packet transform (PWPT) is designed to convert speech utterances into wavelet entropy feature vectors, and a Convolutional Neural Network (CNN) is designed to estimate the frame posteriors of the wavelet entropy feature vectors. In the end, i-vector is extracted based on those frame posteriors. TIMIT and VoxCeleb speech corpus are used for experiments and the experimental results show that the proposed method can extract appropriate i-vector which reduces the equal error rate (EER) and improve the accuracy of voice authentication system in clean and noisy environment.

Application of wavelet entropy and grey relational analysis in fault diagnosis of hydraulic system of engineering machinery

The working environment of engineering machinery is complex. The signal of the equipment in operation is characterized by non-stationary and complex variability. In this paper, the wavelet transform is used to extract the non-stationary fault signals of the hydraulic system of the equipment. At the same time, a fault classification method based on the multi wavelet entropy feature extraction is proposed with the information entropy. The fault features are extracted quickly and accurately. The grey correlation analysis method is used to identify the fault, and the remote fault is set up. Diagnosis platform. The results show that the method can improve the support and accuracy of the fault diagnosis, and provides a feasible new method for the fault diagnosis of the engineering machinery.

Deception Decreases Brain Complexity

Extensive evidence suggests the feasibility of lie detection using electroencephalograms (EEGs). However, it is largely unknown whether there are any differences in the nonlinear features of EEGs between guilty and innocent subjects. In this study, we proposed a complexity-based method to distinguish lying from truth telling. A total of 35 participants were randomly divided into two groups, and their EEG signals were recorded with 14 electrodes. Averages for sequential sets of five trials were first calculated for the probe responses within each subject. Next, a common wavelet entropy (WE) measure and an improved one were used to quantify complexity from each five-trial average. The results show that for both measures, the WE values in the guilty subjects are statistically lower than those in the innocent subjects for most of the 14 electrodes. More importantly, using the improved measure, the difference in WE between the two groups of subjects significantly increases for 11 brain regions compared with the values from the common measure. Finally, the highest balanced classification accuracy, 89.64%, is achieved when using the combined WE feature vector in five brain regions from the sites of Pz, P3, C4, Cz, and C3. Our findings indicate that the lying task elicits a more ordered brain activity in some specific brain regions than the task of telling the truth. This study not only demonstrates that improved WE measurements could be a powerful quantitative index for detecting lying but also sheds light on the brain mechanisms underlying deceptive behaviors.

Analysis of Feature Fatigue EEG Signals Based on Wavelet Entropy

Fatigue driving is bringing more and more serious harm, but there are various reasons for fatigue driving, it is still difficult to test the driver’s fatigue. This paper defines a method to test driver’s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier’s results. The accuracy of the driver fatigue state monitoring using the wavelet entropy is 90.7%, which is higher than the use of spectral entropy as the characteristic accuracy rate of 81.3%. The results show that the frequency characteristics of EEG can be well applied to driving fatigue testing, but different frequency feature calculation methods will affect the classification accuracy.

Heart sound classification from unsegmented phonocardiograms

Objective: Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. Approach: PCG segments of 5 s duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 s segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation. Normalised spectral amplitude was determined by fast Fourier transform and wavelet entropy by wavelet analysis. For each of these a simple single feature threshold-based classifier was implemented and the frequency/scale and thresholds for optimum classification accuracy determined. The analysis was then repeated using relatively noise free 5 s segments (seg 2) of each recording. Spectral amplitude and wavelet entropy features were then combined in a classification tree. Main results: There were significant differences between normal and abnormal recordings for both wavelet entropy and spectral amplitude across scales and frequency. In the wavelet domain the differences between groups were greatest at highest frequencies (wavelet scale 1, pseudo frequency 1 kHz) whereas in the frequency domain the differences were greatest at low frequencies (12 Hz). Abnormal recordings had significantly reduced high frequency wavelet entropy: (Median (interquartile range)) 6.63 (2.42) versus 8.36 (1.91), p < 0.0001, suggesting the presence of discrete high frequency components in these recordings. Abnormal recordings exhibited significantly greater low frequency (12 Hz) spectral amplitude: 0.24 (0.22) versus 0.09 (0.15), p < 0.0001. Classification accuracy (mean of specificity and sensitivity) was greatest for wavelet entropy: 76% (specificity 54%, sensitivity 98%) versus 70% (specificity 65%, sensitivity 75%) and was further improved by selecting the lowest noise segment (seg 2): 80% (specificity 65%, sensitivity 94%) versus 71% (specificity 63%, sensitivity 79%). Classification tree with combined features gave accuracy 79% (specificity 80%, sensitivity 77%). Significance: The feasibility of accurate classification without segmentation of the characteristic heart sounds has been demonstrated. Classification accuracy is comparable to other algorithms but achieved without the complexity of segmentation.

EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal Mutual Information method.

Recognizing emotion from EEG signals is a complicated task that requires complex features and a substantial number of EEG channels. Simple algorithms to analyse the feature and reduce the EEG channel number will give an indispensable advantages. Therefore, this study explores a combination of wavelet entropy and average wavelet coefficient (WEAVE) as a potential EEG-emotion feature to classify valence and arousal emotions with the advantage of the ability to identify the occurrence of a pattern while at the same time identify the shape of a pattern in EEG emotion signal. The complexity of the feature was reduced using the Normalized Mutual Information (NMI) method to obtain a reduced number of channels. Classification with the WEAVE feature achieved 76.8% accuracy for valence and 74.3% for arousal emotion, respectively. The analysis with NMI shows that the WEAVE feature has linear characteristics and offers possibilities to reduce the EEG channels to a certain number. Further analysis also reveals that detection of valence emotion with reduced EEG channels has a different combination of EEG channels compared to arousal emotion.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients

To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system "WE + BPSO-MT + PNN" yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. The proposed method is effective for MR brain classification.