Log Energy Entropy Research Articles

Automated System for Epileptic EEG Detection Using Iterative Filtering

The nonstationary characteristics present in electroencephalogram (EEG) signal require a crucial analysis that can reveal a method for diagnosis of neurological abnormalities, especially epilepsy. This article presents a new technique for automated classification of epileptic EEG signals based on iterative filtering (IF) of EEG signals. The superiority of IF over empirical mode decomposition for the classification of seizure EEG signals is presented. In this article, EEG epochs are decomposed into their intrinsic mode functions (IMFs) using IF. Amplitude envelope (AE) function is extracted from these modes, using the discrete separation energy algorithm. The features are extracted from these IMFs and AE functions. The feature set includes K-nearest neighbor entropy estimator, log energy entropy, Shannon entropy, and Poincar $\acute{\text{e}}$ plot parameters. These features are tested for their discriminative strength, on the basis of their $p$ -values, for classification of EEG signals into seizure, seizure-free, and normal classes. This proposed methodology has obtained a high classification accuracy using random forest classifier and takes far less time, which can be suitable for real-time implementation.

Do meditators and non-meditators have different HRV dynamics?

PurposeThe heart is a complex system and many researchers have been recently studying cardiac behavior using the theory of nonlinear dynamical systems. One of the most appealing tools for analyzing heart function is the heart rate variability (HRV) signal. This study aimed to elucidate the HRV dynamics of six distinct states: spontaneous normal breathing (SNB) and metronomic breathing (MB), as non-meditator groups, before Chinese Chi meditation (CCM), during CCM, before Kundalini yoga meditation (KYM), and during KYM, as meditator groups. MethodsThe HRV data were obtained from the Physionet database. Lagged Poincare indices, Lyapunov exponent (LE), Lempel-Ziv complexity (LZ), and 4 types of entropy were calculated. ResultsThe results showed the greatest discrepancies in the lagged Poincare indices for KYM and CCM. In contrast, the least variations were achieved for MB. Compared to SNB, an enhancement in the log energy entropy and a reduction in the LZ and other entropies were concluded during KYM and CCM practices. In contrast, a reverse pattern was observed for MB. Using support vector machine, HRV dynamics were classified with average accuracies of 99.14 and 98.2% and average sensitivities of 99.87 and 99.57% for pre-KYM and during KYM, respectively. ConclusionIt was shown that the HRV dynamics were significantly different in meditators and non-meditators.

Detection and Identification of Vehicles Based on Their Spark-Free Unintended Electromagnetic Emissions

Detection and identification of devices using their electromagnetic emissions is a widespread practice. Vehicles are among the most complex with emissions from a whole range of electrical and mechanical components. The authors in a previous work [11] , detected and identified vehicles using their electromagnetic emissions by neural network analysis of data derived from the fast Fourier transform (FFT) of measurements. The method was successful provided there was an ignition spark event captured. In this letter, the authors focus on the no-spark case and instead of FFT, use wavelet packet analysis (WPA). WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and nonstationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are further analyzed to obtain feature vectors of log energy entropy. Similar to the previous work [11] , training and testing is done on separate days. Emissions from three cars and ambient noise are analyzed and then classified using a multilayer perceptron. 100% detection and identification rate is accomplished when there is no ignition spark event present.

Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system

In this paper, a new islanding detection technique is proposed for a three-phase grid connected photovoltaic inverter system using the multi-signal analysis method. The proposed strategy is divided into two steps: first step, all possible grid faults, switching transients and islanding events are simulated and the essential detection parameters are measured. By means of the Slantlet Transform theory, the energy, mean value, minimum, maximum, range, standard deviation and log energy entropy at any decomposition level of Slantlet Transform for parameter detection is computed and the best of them are selected as input data of second step. Second step, an advanced machine learning based on Ridgelet Probabilistic Neural Network is utilized to predict islanding and none islanding states. In order to train Ridgelet Probabilistic Neural Network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is proposed. The results depicting the effectiveness of the proposed method are explained and outcomes are drawn.

Vehicle speed measurement by on-board acoustic signal processing

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

BCI oriented EEG analysis using log energy entropy of wavelet packets

Abstract Brain computer interfacing (BCI) is the practice of establishing a direct communication between a human and a computer by self-regulation of some electrical activity in the brain. Electroencephalography (EEG) based BCI is one of the most promising form of this application where a subject learns to control brain waves of type μ-rhythm (8–14 Hz) or β-rhythm (14–30 Hz) or P300 event related potential or slow cortical potentials (SCPs). Intents of the person is extracted using signal processing techniques. In this study self-regulation of SCPs is studied using wavelet packet analysis (WPA), rather than traditional time or frequency domain methods. WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are analyzed further by log energy entropy. These feature vectors are fed into a multilayer perceptron (MLP) for classification. We use the BCI Competition 2003 datasets Ia and Ib to test our approach. Performance of the MLP is compared with that of k-NN and SVM. Our method achieves accuracies of 92.8% and 63.3% on datasets Ia and Ib respectively. This outperforms the other reported results on the same datasets including the winners of the competition. Overall, WPA followed by Log Energy Entropy and MLP provides an efficient tool for the aforementioned kind of BCI oriented EEG analysis.

Engine Speed–Independent Acoustic Signature for Vehicles

A vehicle, when running, makes a complex sound emission from the engine, the exhaust, the air conditioner, and other mechanical parts. Analysis of this sound for the purpose of vehicle identification is an interesting practice which has security- and transportation-related applications. Engine speed variation, which causes shifts in the frequency content of the emissions, makes Fourier-based methods ineffective in terms of providing a stable signature for the vehicle. We search for an engine speed–independent acoustic signature for the vehicle, and for this purpose, we propose wavelet packet analysis rather than traditional time- or frequency-domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Under varying engine speed, sound emissions are recorded from four cars and analyzed by wavelet packet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors in the form of log energy entropy, norm entropy, and energy. These feature vectors are fed into a classifier, multilayer perceptron, for evaluation. While norm entropy achieves a classification rate of 100%, log energy entropy and energy achieves classification rates of 99.26% and 97.79%, respectively. These results indicate that, wavelet packet analysis along with norm entropy and multilayer perceptron provides an accurate vehicle-specific acoustic signature independent of the engine speed.

EEG based epileptiform pattern recognition inside and outside the seizure states

Abstract Automatic diagnosis of epilepsy by computers based on Electroencephalography (EEG) analysis is a beneficial practice which increases recognition rate, speeds up diagnosis and saves physicians from long hours of EEG inspection. Most studies on this subject report results on detecting seizures; but seizures, which appear during the ictal states are very rare to catch. Therefore, an efficient algorithm must be able to detect epilepsy during nonseizure periods, or in other words interictal states as well. We present a novel algorithm to detect epileptiform patterns during both states: ictal and interictal. For this purpose, we use wavelet packet analysis (WPA) rather than traditional time and frequency domain methods. WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are analyzed further to obtain feature vectors of Log Energy Entropy, Norm Entropy and Energy. These features are fed into a classifier, multilayer perceptron (MLP). We test our method on a well-known and widely studied database which includes healthy, ictal and interictal EEG recordings. Normal vs. Ictal and Interictal vs. Ictal zone classifications are realized at 100% accuracy by using any of the three features. Nonseizure vs. Seizure state classification is realized with 100% accuracy using Norm entropy. Normal vs. Interictal zone classification is achieved with 100% accuracy using Log Energy Entropy. Normal vs. Interictal vs. Ictal Zone classifications are considered as two-step binary classifications of Nonseizure vs. Seizure state followed by Normal vs. Interictal zone both of which are realized at 100% accuracy. Therefore, jointly use of Log Energy Entropy and Norm entropy is able to realize all possible classifications with 100% accuracy. We make the most comprehensive comparison of results belonging to all possible classification categories reported on the same datasets. The proposed method outperforms all the other results in all possible classifications by achieving 100% accuracy in all possible classifications. The method presented is the only single method that achieves this. Overall, the method provides us with a promising tool for the detection of epileptiform patterns during and outside the seizure states.

Ground moving target recognition using log energy entropy of wavelet packets

Automatic target recognition of ground surveillance radars is a beneficial practice that reduces cost, error, duration of detection and response time. The identification of echoes from eight classes using an RF pulsed Doppler radar is studied. Instead of traditional methods, the wavelet packet analysis (WPA) is used. WPA, by providing arbitrary time–frequency resolution, enables analysing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. WPA subimages are further analysed to obtain feature vectors of log energy entropy in all cases except one case where norm entropy is used. These features are fed into a multilayer perceptron for classification which is done in a hierarchical scheme and composed of binary classification steps. Hundred per cent accuracy was possible in all steps except two which were realised at 99.5 and 98.2%. An overall classification rate of 98.2% was achieved. The results were compared with other published results that report accuracy in the same application field. The proposed method achieved the highest accuracy although it had the highest number of classes. The method provides a promising tool for the automatic recognition of ground surveillance radar targets.

Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT. We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis.

BREAST CANCER DETECTION USING ENSEMBLE CLASSIFICATION AND EXTENDED WEIGHTED VOTING METHOD

Breast cancer disease increases the mortality rate of women. Early detection of cancer helps to increase the life span and decreases the mortality rate. The daubechies-8 wavelet filter, discrete wavelet transform entropies such as Shannon, Log energy and Sure entropy were applied to extract the textural features of mammogram image. The ensemble classifiers K.Nearest Neighbor, Bayes and Support Vector Machine were used to classify. The novel method of extended weighted voting method is applied to obtain the accurate result which produces the increased performance of 97.3% of results.

Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach

Early and accurate detection of myocardial infarction is imperative for reducing the mortality rate due to heart attack. Present work proposes a novel technique aiming toward accurate and timely detection of inferior myocardial infarction (IMI). Stationary wavelet transform has been used to decompose the segmented multilead electrocardiogram (ECG) signal into different sub-bands. Sample entropy, normalized sub-band energy, log energy entropy, and median slope calculated over selected bands of multilead ECG are used as features. Support vector machine (SVM) and K-nearest neighbor (KNN) have been used to classify between subjects admitted for health control (HC) and patients suffering from IMI, using attributes selected on the basis of gain ratio. The full length ECG of lead II, III, and aVF of all the subjects having IMI or admitted for HC from Physikalisch-Technische Bundesanstalt Database (PTB-DB) has been used in the present work. The proposed technique has been scrutinized under both “class-oriented,” and more practical, “subject-oriented” approach. Under the class-oriented approach, data have been divided into training and test data irrespective of the patients, whereas in subject-oriented approach, data from one patient have been used for test and training has been done on the rest of the subjects. Under the class-oriented approach, area under the receiver operating characteristic curve (Roc), sensitivity (Se%), specificity (Sp%), positive predictivity (+P%), and accuracy (Ac%) is Roc $$=$$ 0.9945, Se% $$=$$ 98.67, Sp% $$=$$ 98.72, +P% $$=$$ 98.79, Ac% $$=$$ 98.69 using KNN and Roc $$=$$ 0.9994, Se% $$=$$ 99.35, Sp% $$=$$ 98.29, +P% $$=$$ 98.41, Ac% $$=$$ 98.84 using SVM. For the subject-oriented approach, an average Ac% $$=$$ 81.71, Se% $$=$$ 79.01, Sp% $$=$$ 79.26, and +P% $$=$$ 80.25 has been achieved. This shows the potential of the proposed technique to work for an unknown subject, on which it has not been trained.

Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform

Epilepsy is a neurological disease which is difficult to diagnose accurately. An authentic detection of focal epilepsy will help the clinicians to provide proper treatment for the patients. Generally, focal electroencephalogram (EEG) signals are used to diagnose the epilepsy. In this paper, we have developed an automated system for the detection of focal EEG signals using differencing and flexible analytic wavelet transform (FAWT) methods. The differenced EEG signals are subjected to 15 levels of FAWT. Various entropies namely cross correntropy, Stein’s unbiased risk estimate (SURE) entropy, and log energy entropy are extracted from the reconstructed original signal and 16 sub-band signals. The statistically significant features are obtained from Kruskal–Wallis test based on (p < 0.05). K-nearest neighbor (KNN) and least squares support vector machine (LS-SVM) classifiers with different distances and kernels respectively are used for automated diagnosis. In the proposed methodology, we have achieved classification accuracy of 94.41% in detecting focal EEG signals using LS-SVM classifier with ten-fold cross validation strategy.

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier.

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50Hz from raw EEG recordings. Raw EEGs were segmented into 1s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70% for normal-pre-ictal, 99.70% for normal-epileptic and 99.85% for pre-ictal-epileptic.

Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain

Abstract In this paper, a comprehensive analysis of focal and non-focal electroencephalography is carried out in the empirical mode decomposition and discrete wavelet transform domains. A number of spectral entropy-based features such as the Shannon entropy, log-energy entropy and Renyi entropy are calculated in the empirical mode decomposition and discrete wavelet transform domains and their efficacy in discriminating the focal and non-focal EEG signals is investigated. The electroencephalogram signals are obtained from a publicly available electroencephalography database that consists of 7500 signal pairs which contain over 80 h of electroencephalogram data collected from five epilepsy patients. It is shown that in the log-energy entropy when calculated in the combined empirical mode decomposition–discrete wavelet transform domain gives a better discrimination of these signals as compared to that of the other entropy measures that is the Shannon and quadratic Renyi entropy as well as to that obtained in empirical mode decomposition or discrete wavelet transform domain. When the log-energy entropy values are utilized as features in a K-nearest neighbor classifier to classify the signals, it provides 89.4% accuracy (with 90.7% sensitivity), which is higher than that of the state-of-the-art methods. Overall, the proposed classification method reports a significant improvement in terms of sensitivity, specificity and accuracy in comparison to the existing techniques. Besides, for being computationally fast, the proposed method has the potential for identifying the epileptogenic zones, which is an important step prior to resective surgery usually performed on patients with low responsiveness to anti-epileptic medications.

EEG complexity and frequency in chronic residual schizophrenia

Objective: Some studies on schizophrenia showed an increased complexity in electroencephalography (EEG) whereas others detected a decreased complexity. Because this discrepancy might be due to the clinical features or complexity measures used, we employed two different complexity measures in a group of schizophrenics similar in illness duration (chronic) and symptom profile (residual). Methods: Right-handed chronic residual schizophrenic patients (10 male, 10 female) and age- and sex-matched 20 healthy controls were included in the study. Eyes-closed resting EEG series were measured through quantitative EEG band activities, the log energy entropy (LEE) values, and the Hurst exponents (HE) of EEG measurements were computed for each electrode site. Results: Significantly higher LEE values in the prefrontal, frontal, temporal and parietal locations were observed in schizophrenic patients compared with controls. HE values were significantly higher on the right frontal area in the schizophrenics. Patient group showed increased prefrontal, frontal and parietal delta activity, prefrontal, left temporal and right parietal theta activity and increased left temporal alpha activity. Discussion: In the present study, we found that chronic residual schizophrenia is associated with decreased complexity and increased smoothness in EEG. In addition, EEG of patients was characterized by obvious slowness at prefrontal and frontotemporal regions, dominantly. An integration of EEG complexity and frequency analysis can be proposed as an innovative tool in schizophrenia research.

Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals

The scaling behavior of human electroencephalogram (EEG) signals is well exploited by appropriate extraction of time – frequency domain and entropy based features. Such measurable inherently helps understanding the neurophysiological phenomenon of brain as well as its associated cortical activities. Being a non-linear time series, EEG's are assumed to be fragment of fluctuations. Several attempts have been made to study the EEG signals for clinical applications such as epileptic seizure detection, evoked response potential recognition, tumor detection, identification of alcoholics and so on. In all such applications appropriate selection of feature parameter plays an important role in discriminating normal EEG from abnormal. In the recent past one can find the importance of wavelet and wavelet packet towards EEG analysis. This proposed research work investigates the effect of wavelet packet log energy entropy on EEG signals. Entropy being the measure of relative information, the proposed study attempts to discriminate the normal EEGs from abnormal EEG's by employing the log energy entropy features. For better brevity, this study restricts to the analysis of epileptic seizure from normal EEGs. Different decomposition levels from 2 to 5 were considered for wavelet packets with application of Haar, rbio3.1, sym7, dmey wavelets. A one second windowing was introduced for the data segmentation and Shannon's log energy entropy was estimated. Then the statistical non-parametric Wilcoxon model was employed. The result shows that the application of wavelet packet log energy entropy found to be a potential indicator for discriminating epileptic seizure from normal.

An Efficient Feature Extraction Method Based on Entropy for Power Quality Disturbance

This study explores the applicability of entropy defined as thermodynamic state variable introduced by German Physicists Rudolf clausius and also presents the concepts and application of said state variable as a measure of system disorganization. Later an entropy-based feature Analysis method for power quality disturbance analysis has been proposed. Feature extraction of a disturbed power signal provides information that helps to detect the responsible fault for power quality disturbance. A precise and faster feature extraction tool helps power engineers to monitor and maintain power disturbances more efficiently. Firstly, the decomposition coefficients are obtained by applying 10-level wavelet multi resolution analysis to the signals (normal, sag, swell, outage, harmonic and sag with harmonic and swell with harmonic) generated by using the parametric equations. Secondly, a combined feature vector is obtained from standard deviation of these features after distinctive features for each signal are extracted by applying the energy, the Shannon entropy and the log-energy entropy methods to decomposition coefficients. Finally the entropy methods detect the different types of power quality disturbance.

Feature extraction and fault severity classification in ball bearings

The present study attempts to diagnose severity of faults in ball bearings using various machine learning techniques, like support vector machine (SVM) and artificial neural network (ANN). Various features are extracted from raw vibration signals which include statistical features such as skewness, kurtosis, standard deviation and measures of uncertainty such as Shannon entropy, log energy entropy, sure entropy, etc. The calculated features are examined for their sensitivity towards fault of different severity in bearings. The proposed methodology incorporates extraction of most appropriate features from raw vibration signals. Results revealed that apart from statistical features uncertainty measures like log energy entropy and sure entropy are also good indicators of variation in fault severity. This work attempts to classify faults of different severity level in each bearing component which is not considered in most of the previous studies. Classification efficiency achieved by proposed methodology is compared to the other methodologies available in the literature. Comparative study shows the potential application of proposed methodology with machine learning techniques for the development of real time system to diagnose fault and it’s severity in ball bearings.

Classification of Power Quality Disturbances Using Wavelet Packet Energy Entropy and LS-SVM

The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.