Entropy-based Feature Extraction Research Articles

Identification of Anesthesia Stages from EEG Signals using Wavelet Entropy and Backpropagation Neural Network

This study focuses on entropy based analysis of EEG signals for extracting features for a neural network based solution for identifying anesthetic levels. The process involves an optimized back propagation neural network with a supervised learning method. We provided the extracted features from EEG signals as training data for the neural network. The target outputs provided are levels of anesthesia stages. Wavelet analysis provides more effective extraction of key features from EEG data than power spectral density analysis using Fourier transform. The key features are used to train the Back Propagation Neural Network (BPNN) for pattern classification network. The final result shows that entropy-based feature extraction is an effective procedure for classifying EEG data.

Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification.

Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.

Epileptic Seizure Detection With Permutation Fuzzy Entropy Using Robust Machine Learning Techniques

The automatic and accurate determination of the epileptogenic area can assist doctors in presurgical evaluation by providing higher security and quality of life. Visual inspection of electroencephalogram (EEG) signals is expensive, time-consuming and prone to errors. Several numbers of automated seizure detection frameworks were proposed to replace the traditional methods and to assist neurophysiologists in identifying epileptic seizures accurately. However, these systems lagged in achieving high performance due to the anti-noise ability of feature extraction techniques, while EEG signals are highly susceptible to noise during acquisition. The present study put forwards a new entropy index Permutation Fuzzy Entropy (PFEN), which may delineate between ictal and interictal state of epileptic seizure using different machine learning classifiers. 10-fold cross-validation has been used to avoid the over-fitting of the classification model to achieve unbiased, stable, and reliable performance. The proposed index correctly distinguishes ictal and interictal states with an average accuracy of 98.72%, sensitivity of 98.82% and a specificity of 98.63%, across 21 patients with six epileptic seizure origins. The proposed system manifests the fact that lower PFEN characterizes the EEG during seizure state than in the Interictal seizure state. The study also helps us to investigate the more profound enactment of different classifiers in term of their distance metrics, learning rate, distance, weights, multiple scales, etc. rather than the conventional methods in the literature. Compared to other state of art entropy-based feature extraction methods, PFEN showed its potential to be a promising non-linear feature for achieving high accuracy and efficiency in seizure detection. It also show’s its feasibility towards the development of a real-time EEG-based brain monitoring system for epileptic seizure detection.

A New Bearing Fault Diagnosis Method Based on Fine-to-Coarse Multiscale Permutation Entropy, Laplacian Score and SVM

Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the fine-to-coarse multiscale permutation entropy (F2CMPE), Laplacian score (LS) and support vector machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on the wavelet packet decomposition. The entropy measure estimates the complexity of time series from both low- and high-frequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional composite multiscale permutation entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification, respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify the bearing fault patterns under different fault states and severity levels.

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm.

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

Entropy‐based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise

Non-invasive methods accomplished by a computer aided diagnosis of knee-joint disorders provide an effective tool. The objective of this study is to analyse vibroarthographic (VAG) signals using non-linear signal processing technique. This study includes different entropy-based feature extraction techniques to attain highly distinguishable features. The authors proposed to use a non-linear method known as complete ensemble empirical mode decomposition with adaptive white noise to decompose the VAG signals into intrinsic mode functions (IMFs). Entropy-based features involving approximate entropy, sample entropy, Shannon entropy, Renyi entropy, Tsallis entropy and permutation entropy (PeEn) are computed from dominant IMFs and reconstructed VAG signals. These extracted features are given as input to the least squares support vector machine as a classifier. The results illustrated that PeEn performed better with respect to other entropies. PeEn gives a classification accuracy of 86.61% and Matthews correlation coefficient of 0.7082. The computational complexity of entropies was also analysed. Results inferred that PeEn has a computational complexity of O ( N ) provided a simple, robust and low computational feature extraction technique. Analysis of VAG signals using non-linear preprocessing and entropy-based features can provide highly distinguishable features for accurate detection of knee-joint disorders.

Entropy based feature extraction of electrohysterogram signal for the prediction of preterm birth

Entropy based feature extraction of electrohysterogram signal for the prediction of preterm birth

Wavelet Sub Band Entropy Based Feature Extraction Method for BCI

The study and analysis of the electrical activity of the brain is valuable in understanding the human mental state, intentions and will. This aids the development of Brain Computer Interface (BCI), facilitating communication between the human brain and computer, by converting the brain waves (EEG waves - Electroencephalography) into control signals. These control signals can then be used to trigger an external device, thereby enabling a seamless communication with the intelligent system. It unfolds various avenues for research and further applications in the realm of prosthetic device control, development of thought controlled intelligent systems and other complex interfaces. This will also be an aid to persons with disabilities or various other amputations. In this work, a novel feature extraction algorithm is proposed for extracting event related potentials from the EEG signals using wavelet sub band entropy which can be used in BCI applications. An attention index is defined which gives a measure of the amount of concentration or attention the subject has, upon focussing on a particular event or thought.

Ball Bearing Fault Diagnosis using Supervised and Unsupervised Machine Learning Methods

This paper deals with the approach of using multiscale permutation entropy as a tool for feature selection for fault diagnosis in ball bearings. The coefficients obtained from the wavelet transformation of the vibration signals of the bearings are used for the calculation of statistical parameters. Based on the minimum multiscale permutation entropy criteria, the best scale is selected and statistical parameters such as crest factor, form factor, and permutation entropy are calculated. Finally, the faults are classified by considering the statistical parameters and permutation entropy as features in supervised and unsupervised machine learning methods, such as a support vector machine and self-organizing maps, respectively. Results revealed that the multiscale permutation entropy-based feature extraction techniques provide higher classification accuracy in comparison to the other methodologies that have been proposed in previous published works. The methodology proposed in this paper also gives good results for unsupervised learning methods, i.e. self-organizing maps. 1

A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings

The detection and diagnosis of bearing health status using vibration signal has been an important subject for extensive research over the past few decades. The objective of this paper is to proposed permutation entropy as a tool to select best wavelet for feature selection for the detection as well as fault classification of ball bearings. The continuous wavelet coefficients of the time domain signal are calculated at real, positive scales using various real and complex wavelets. Best wavelet and corresponding scale is selected based on minimum permutation entropy. Eleven statistical parameters were used for defect classification in outer race, inner race, ball defect and healthy bearing respectively. Proposed methodology for fault classification is compared with two artificial intelligence techniques such as artificial neural network and support vector machine. Results revealed that permutation entropy based feature extraction techniques provide higher classification accuracy even when there is a slight variation in operating condition which is useful for development of online fault diagnosis.

ELIC-Based Texture Advection and Information Entropy-Based Feature Extraction for the Visualization of Surface Flow Field

IBFVS is a classical method for visualizing surface flow field, but the quality of the final image is inadequate to get a fully understanding for the visualized flow field. In order to improve the quality of the result image of IBFVS, this paper presents an enhanced IBFVS method based on short ELIC filtering. We take IBFVS as the basic mechanism of our method, and use ELIC filtering to process the injected background image to increase the contrast of the result image. Furthermore, we use information entropy to extract the most important features with highest information in the flow field. Experiment results show that our method generates a better visualization result than IBFVS and the information entropy-based feature extraction method distinguishes the most valuable part in the flow field.

Recognition of Promoters in DNA Sequences Using Weightily Averaged One-dependence Estimators

The completion of the human genome project in the last decade has generated a strong demand in computational analysis techniques in order to fully exploit the acquired human genome database. The human genome project generated a perplexing mass of genetic data which necessitates automatic genome annotation. There is a growing interest in the process of gene finding and gene recognition from DNA sequences. In genetics, a promoter is a segment of a DNA that marks the starting point of transcription of a particular gene. Therefore, recognizing promoters is a one step towards gene finding in DNA sequences. Promoters also play a fundamental role in many other vital cellular processes. Aberrant promoters can cause a wide range of diseases including cancers. This paper describes a state-of-the-art machine learning based approach called weightily averaged one-dependence estimators to tackle the problem of recognizing promoters in genetic sequences. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based feature extraction approach to select relevant nucleotides that are directly responsible for promoter recognition. We carried out experiments on a dataset extracted from the biological literature for a proof-of-concept. The proposed system has achieved an accuracy of 97.17% in classifying promoters. The experimental results demonstrate the efficacy of our framework and encourage us to extend the framework to recognize promoter sequences in various species of higher eukaryotes.

Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images

In this study, a computer-assisted entropy-based feature extraction and decision tree induction protocol for breast cancer diagnosis using thermograph images was proposed. First, Beier–Neely field morphing and linear affine transformation were applied in geometric standardization for whole body and partial region respectively. Gray levels of pixel population at the same anatomical position were statistically analyzed for abnormal region classification. Morphological closing and opening operations were used to identify unified abnormal regions. Three types of 25 feature parameters (i.e. 10 geometric, 7 topological and 8 thermal) were extracted for parametric factor analysis. Positive and negative abnormal regions were further reclassified by decision trees to induce the case-based diagnostic rules. Finally, anatomical organ matching was utilized to identify the corresponding organ with the positive abnormal regions. To verify the validity of the proposed cased-based diagnostic protocol, 71 and 131 female patients with and without breast cancer were analyzed. Experimental results indicated that 1750 abnormal regions (703 positive and 1047 negative) were detected and 822 branches were broken down into the decision space. Fourteen branches were found to have more than 4 positive abnormal regions. These critical diagnostic paths with less than 10% of positive abnormal regions (61/703 = 8.6%) can effectively classify more than half of the cancer patients (42/71 = 59.2%) in the abovementioned 14 branches.

A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. Substantial data is generated by the EEG recordings of ambulatory recording systems, and detection of epileptic activity requires a time-consuming analysis of the complete length of the EEG time series data by a neurology expert. A variety of automatic epilepsy detection systems have been developed during the last ten years. In this paper, we investigate the potential of a recently-proposed statistical measure parameter regarded as Sample Entropy (SampEn), as a method of feature extraction to the task of classifying three different kinds of EEG signals (normal, interictal and ictal) and detecting epileptic seizures. It is known that the value of the SampEn falls suddenly during an epileptic seizure and this fact is utilized in the proposed diagnosis system. Two different kinds of classification models, back-propagation neural network (BPNN) and the recently-developed extreme learning machine (ELM) are tested in this study. Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed.

Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement

This paper introduces the fundamental of cross-section measurement system based on Electrical Resistance Tomography (ERT). The measured data of four flow regimes of the gas/liquid two-phase flow in horizontal pipe flow are obtained by an ERT system. For the measured data, five entropies are extracted to analyze the experimental data according to the different flow regimes, and the analysis method is examined and compared in three different perspectives. The results indicate that three different perspectives of entropy-based feature extraction are sensitive to the flow pattern transition in gas/liquid two-phase flow. By analyzing the results of three different perspectives with the changes of gas/liquid two-phase flow parameters, the dynamic structures of gas/liquid two-phase flow is obtained, and they also provide an efficient supplementary to reveal the flow pattern transition mechanism of gas/liquid two-phase flow. Comparison of the three different methods of feature extraction shows that the appropriate entropy should be used for the identification and prediction of flow regimes.

Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.