Db4 Wavelet Research Articles

Performance Analysis of Adaptive Filters with various Wavelets for Noise Removal in EEG Signals

Noise removal from recorded EEG signal is most essential for better analysis of brain disorders. During recoding time, EEG signals are usually contaminated by various noise and distortions due to several artifacts. These noisy EEG signals may lead to wrong diagnosis of brain disorders. There are several techniques available to remove the noise from EEG signals. But these techniques are unable to remove the noise completely. However, they can minimize the noise in EEG signals so that the physicians can predict brain disorders. This work presents to minimize the noise by Discrete Wavelet Transform Methods using haar, db2, symlet and coiflet wavelets. EEG original signals from public EEG database are used for experimentation and wavelet transformations, are applied by using Matlab code. The filters performance is measured and analyzed on the basis of performance parameters like SNR and MSE which are calculated for various step sizes of signal and filter orders. Wavelet analysis techniques shows better performance when compared to others

Assessment of Wavelets Transform based Processing of Features of Forearm Muscle Signals for Prosthesis

People who have lost forearm are suffer from hand mobility limitations due to trauma, disease or defect, Prosthesis arm help those people to do their daily actions. Researchers have been focused on developing artificial hand. In this regard, better processing of features of electromyographic (EMG) signal has a significant role from residual forearm muscle. To achieve this, Wavelet Transform (WT) technique has been applied because it is acceptable with the characteristics of EMG as a nonstationary signal. Results have shown that db5 wavelet decomposition performs best denoising at fifth level in other wavelets comparison. Furthermore, the ratio of Signal to Noise (S/N) and the error of percentage (PE) are calculated to evaluate the eminence and the usefulness features of EMG.

Assessment of Wavelets Transform based Processing of Features of Forearm Muscle Signals for Prosthesis

People who have lost forearm are suffer from hand mobility limitations due to trauma, disease or defect, Prosthesis arm help those people to do their daily actions. Researchers have been focused on developing artificial hand. In this regard, better processing of features of electromyographic (EMG) signal has a significant role from residual forearm muscle. To achieve this, Wavelet Transform (WT) technique has been applied because it is acceptable with the characteristics of EMG as a nonstationary signal. Results have shown that db5 wavelet decomposition performs best denoising at fifth level in other wavelets comparison. Furthermore, the ratio of Signal to Noise (S/N) and the error of percentage (PE) are calculated to evaluate the eminence and the usefulness features of EMG.

Estimation of heart rate and respiratory rate from the seismocardiogram under resting state

This study proposes a dedicated algorithm to estimate heart rate (HR) and respiratory rate (RR) from seismocardiogram (SCG). The proposed algorithm primarily consists of the wavelet decomposition (by db6 wavelet), the Fourier-based envelope detection and the time-averaged power spectral density (PSD) from the scalogram by complex Morlet wavelet. The records in the combined measurement of ECG, breathing and SCG (CEBS) database of PhysioNet are adopted to evaluate the performance of the proposed algorithm for HR and RR estimation. The performance was evaluated by intraclass correlation coefficient (ICC) and Bland-Altman’s agreement analysis.The statistical results for HR estimation show good to excellent correlation between the approaches by SCG and ECG and also meet the maximal allowable error for the HR meter. As the quality of some respiratory signals in the CEBS database is not good for RR estimation, additional experiments have been conducted in the university laboratory under well-controlled procedure. The statistical results for the RR estimation in these experiments show good to excellent correlation between the approaches of RR estimation by SCG and respiratory signal. The RR estimation from SCG also meets the specification given in contemporary medical device.The proposed algorithm does not take the removal of motion artifact into account and thus SCG signal must be measured under resting state. From this study, SCG can be a potential tool to estimate HR and RR for monitoring the patient in intensive care unit (ICU) or monitoring the sleep quality in clinical setting or in daily life.

Analysing impact of major solar flares on ionospheric TEC with RTISM model using IRNSS receiver, at SVNIT, Surat, India

Indian regional navigation satellite system (IRNSS) is a navigation system developed by the Indian Space Research Organisation (ISRO), India. The IRNSS receiver granted by Space Application Centre (SAC), ISRO, Ahmedabad is installed at SVNIT, Surat, India. A solar flare is a natural event that occurs when an unpredicted flash of distended Sun's illumination is observed near its surface. The ionosphere is greatly ionised by solar radiations. Hence it is a current challenge to investigate effects of ionospheric scintillation on IRNSS signals. This study examines impact of major solar flare events like Class ‘C’ solar flare happened on 22 July 2016 and class ‘M’ solar flare occurred on 23 and 24 July 2016, on IRNSS signals with measurement of total electron content (TEC). Real-time ionospheric scintillation monitoring (RTISM) model based on empirical mode decomposition (EMD) and second-order Daubechies (db2) wavelet is used for comparing measured and denoised TEC. The RTISM model is also used for determining automatic threshold using Neyman–Pearson detector, probability of detection and probability of false alarm. It is proven with analysis that EMD is giving more sharp results as compared to db2 wavelet for determining fluctuations in TEC during the solar flare events.

Denoising of Electrocardiogram Based on Norm Parameter using Wavelet Packet Transform

The electrocardiogram is an electrical activity of the human heart and it is mainly used in the diagnosis of different diseases associated with the human heart. In the medical field, it is also known as electrophysiological activity of the human heart. During its recording an unwanted noise known as artifacts is getting contaminated into it, this creates an obstruction in its clinical analysis. As for application of wavelet analysis is concerned, it is then fit to mention that denoising of ECG is effectively done by wavelet tools, so that identification of hidden diseases of the patient becomes possible. In this research article an algorithm is proposed for denoising ECG signal by applying a wavelet transform popularly known as wavelet packet transformation and three wavelet functions: Haar Wavelet, Db-3 Wavelet and Coiflet-3 Wavelet. The performance of this algorithm is studied on the basis of constraints popularly known in the mathematical field as Signal to Noise ratio (SNR), Norm 1 and Norm 2. This algorithm is first applied in deciding the optimum level of decomposition applied to the ECG recording No. S10m.mat, then a comparative study is laid down for a sample of 10 ECG recordings obtained from MIT-BIH database available on www.physio.net.

Wavelet packet transform applied to a series-compensated line: A novel scheme for fault identification

For series-compensated transmission lines (SCTLs), identification of the fault section with respect to the compensator as well as fault phase selection are important issues. In this paper, first, the various methods applied for SCTLs are classified and discussed according to specified performance indices selected by the operator. Further, an investigation of solutions to their drawbacks is presented. Second, a novel scheme based on the application of wavelet packet transform (WPT) is proposed for an SCTL. A db10 wavelet packet is used for decomposing the faulted phase current waveform to obtain the energy coefficients. Third, the ATP/EMTP package is used to test and validate the proposed scheme, in which several fault scenarios with different fault locations, inception angles, fault resistances, noise, and variation in voltage and source impedance are examined to show its robustness. The effectiveness of the proposed scheme is demonstrated compared to other methods in the literature.

Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.

An improved wavelet-based signal-denoising architecture with less hardware consumption

In this paper, we propose the VLSI implementation of a wavelet packet transform-based architecture. Moreover, the proposed architecture is compared to the wavelet transform architecture in terms of their signal denoising capabilities, hardware resources used, maximum operating frequency and maximum combination path delay. The Wavelet packet transform architecture is designed and implemented for db2 and db3 wavelet types. For the proposed real-time VLSI implementation, the Xilinx Spartan-6 series FPGA platform is used. Simulation results show that the proposed architecture implementation results in an improved performance for ECG signals taken from the Fantasia Database as well as from the self-recorded database, as compared to the WT wavelet architectures which result in distorted outputs. Moreover, from the synthesis results, it is concluded that the proposed architecture implementation requires significantly less hardware as compared to the WT architectures considered. In terms of LUTs, the proposed architecture consumes 38.20 percent and 84.96 percent less resources than the WT-db2 and WT-db5 architectures, respectively. Similarly, in terms of slice registers, the proposed architecture consumes, respectively, 21.39 percent and 75.61 percent less resources than the WT-db2 and WT-db5 architectures. Furthermore, the maximum operating frequency of the proposed architecture is 180.278 MHz as compared to that of 195.496 MHz and 121.038 MHz for the WT-db2 and WT-db5 architectures respectively. The maximum combinational path delay of the proposed architecture is 48.109 ns, as compared to that of 13.784 ns and 15.202 ns for the WT-db2 and WT-db5 architectures, respectively.

Experimental study and Shannon entropy analysis of pressure fluctuations and flow mode transition in fluidized dense phase pneumatic conveying of fly ash

The objective of this study was to relate experimental pressure fluctuation behavior to the transition in mode of flow observed in fluidized dense phase pneumatic conveying of fly ash. Shannon entropy and wavelet analysis were utilized to extract features of the flow regimes. Daubechies db4 wavelet analysis of pulsating air pressure revealed that the flow mechanism of fly ash in a fluidized dense phase possessed non-steady characteristics associated with gradual aeration of dunes along the direction of flow. Variations of Shannon entropy values along the length of the pipeline were assessed to determine the location at which the flow converted from dense to dilute phase mode. The effects of conveying parameters and specific power consumption on Shannon entropy and variations of the local power consumption coefficient are discussed.

The EEG Analysis of Actual Left/Right Lateral Bending Movements in Patient of Lumbar Disc Herniation.

The purpose of this study was to investigate surface EEG of two groups (lumbar disc herniation (LDH) patients and healthy controls (HC)) when they performed real smooth movements (left/right lateral bending) with the maximum voluntary movement without pain. Thirty female LDH patients and thirty healthy controls volunteered to participate in the experiment. We also tested a healthy participant's motion imaginery (MI) of left/right lateral bending movement for over 200 times. We used Daubechies 4 (db4) wavelet to decompose EEG signal and we extracted δ, θ, α and β rhythms of the EEG signal. Wavelet entropy and sample entropy (SampEn) of four frequency bands were calculated. The results showed that there was significant difference of wavelet entropy EEG in T7, O2, and AF4 channels between the LDH and the healthy group when they did real left lateral bending. The topographic map also showed that SampEn value of four rhythms of the MI right lateral bending were significantly less than the values of the MI left lateral motion in healthy participant. Classification and Regression Trees (CART), Logistic regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), and the Linear discrimination analysis (LDA) classifiers showed averaged accuracies more than 96% for MI of left/right lateral bending.

Reduced-reference image quality assessment method based on wavelet feature extraction and fusion

Virtual reality has many characteristics such as multi-sensation, interactivity, and presence. Virtual reality images, also known as 360-degree panoramic images, provide users with an immersive experience and have been widely used in education, medical, military and many other fields in recent years. 360-degree panoramic images have high resolution and are subject to varying degrees of distortion during transmission and storage. Therefore, the evaluation of virtual reality images has practical significance. To this end, we propose a method for evaluating the quality of images in the new CVIQD database. The proposed image quality assessment model is developed based on Haar wavelet and Db4 wavelet feature extraction and fusion. The method is compared with a single wavelet feature extraction method. Experiments show that the method has better performance.

EEG Based Biometric Identification Using Correlation and MLPNN Models

This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject. Correlation model has yielded great significant difference of coefficient between autocorrelation and cross-correlation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition.

Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR

In order to study the deformation of bored pile, it is necessary to monitor the strain of the pile. The distributed optical fiber sensing technology realizes the integration of sensing and transmission, which is incomparable with traditional point monitoring method. In this paper, the Brillouin optical time domain reflectometer (BOTDR) distributed optical fiber sensing technology is used to monitor the deformation of the bored pile. The raw data monitored by BOTDR is processed by the wavelet basis function, that can perform noise removal processing. Three different methods of noise removal are chosen. Through the processing, the db5 wavelet is used to decompose the deformation data of bored pile monitored by BOTDR into two layers. The decomposed high-frequency signal is denoised by the Stein-based unbiased risk threshold, rigrsure. The decomposed data is smoothed by the translational mean method, and the final data after denoising and smoothing processing is real and reliable. The results of this study will provide data support for the deformation characteristics of bored pile, and also show the advantages of distributed optical fiber sensing technology.

Detection and diagnosis of fault bearing using wavelet packet transform and neural network

Bearings, considered crucial components in rotating machinery, are widely used in the industry. Bearing status monitoring has become an essential step in the deployment of preventive maintenance policy. This work is part of the diagnosis and classification of bearing defects by vibration analysis of signals from defective bearings using time domain and frequency analysis and wavelet packet transformations (Wavelet Packet Transform WPT) with Artificial Neural Networks (ANN). WPT is used for extracting defect indicators to train the neural classifier. The main goal is the determination of the wavelet generating the most representative indicators of the state of the bearings for better detection and classification of defects. Using the WPT-based neural classifier, the obtained simulation results showed that the db6 wavelet with level 3 decomposition is best suited for diagnosing and classifying bearing defects

EEG signal based classification before and after combined Yoga and Sudarshan Kriya

Nowadays, the style of living is restless and busy which has resulted in increased stress among many people. Stress causes various mental and health illness such as depression, anxiety, mood disorders, and aggressive behavior. Yoga and Sudarshan Kriya (SK) meditation are healthy ways to eradicate stress from people’s lives. Based on the previous study, it has been analyzed that SK practice helps to enhance relaxation, management of emotion, alertness, focus, and antidepressant effect. In this paper, the combined impact of yoga and SK meditation has been analyzed on brain signals by using statistical parameters. To the best of the authors’ knowledge, no such study has been conducted in the past. In this study, the pre and post Electroencephalogram (EEG) signals were captured from the control and study group before and after three months regular practice of combined yoga and SK. Discrete Wavelet Transform (DWT) has been used to decompose the signal into 6 sub-bands (0–4, 4–8, 8–16, 16–32, 32–64, 64–128) hertz (Hz) by using db4 wavelet for analysis, statistical features such as variance, standard deviation, kurtosis, zero crossing, maximum and minimum have been calculated from each sub-band. The obtained parameters have been validated by using Kruskal–Wallis statistical test. Further, Artificial Neural Network (ANN) has been applied on aforementioned statistical parameters to classify subjects as meditators and non-meditators. The experimental results indicated that the proposed method achieved 87.2% accuracy for classification and could be further extended to construct an accurate classification system for detection of meditators and non-meditators. This study forms a scientific foundation to encourage the use of meditation in clinical practices.

An Activity Determination Method of Crack Defects in Aluminum Plate and Steel Plate Based on EMAT

The purpose of this paper is to analytically explore that an electromagnetic ultrasonic transducer (EMAT) can be used to do the activity determination of crack defects. Acoustic emission (AE) testing has been done through the innovative use of EMAT, and the activity of defects can be determined based on the Kaiser effect. In consideration of the Lorentz force and magnetostrictive force, the magnetism, force, and vibration displacement of aluminum sheet and steel sheet are analyzed through the finite-element method and fast Fourier transformation. The db3 wavelet is used to obtain the rule of signal change with excitation current. The experimental system of aluminum sheet and steel sheet was established for verification. This method effectively expands the AE theory based on global loading and also gives full play and expands the defect detection capability of EMAT equipment.

Research on Fault Diagnosis of DC Charging Pile Power Device Based on Wavelet Packet and Elman Neural Network

In order to improve the fault diagnosis accuracy of DC charging pile power devices, a fault diagnosis method based on wavelet packet analysis (WPA) and Elman neural network is proposed in this paper. This method sampled the output voltage signal of DC bus in fault state, decomposed the three-layer db10 wavelet packet and reconstructed the single branch, then calculated the characteristic energy spectrum of the fault signal using the signal in the frequency band, and identified it by Elman neural network. In order to test the diagnostic ability of the model, the PWM rectifier model of DC charging pile is used as an example to simulate and compare with the diagnostic results of standard BP neural network. The simulation results show that the fault diagnosis method based on WPA and Elman neural network has faster diagnosis speed, higher accuracy and stronger generalization ability.

Impact Localization of Thin Plate Structures Using Pzt-Array Based Passive Wave Method

Impact of foreign objects is one of the main reasons for the damage of thin plate structures, resulting in property losses or unexpected accidents. Therefore, a rapid and effective impact localization of thin plate structures is of great value. In this paper, a localization method is proposed based on the passive wave method, aiming at locating the impact quickly and accurately. First, a square PZT array composed of 4 PZT sensors was designed, and a hyperbolic localization algorithm was derived according to time-different of arrival (TDoA) for different sensors in the array. Then, Matlab calculation software was used to perform DB wavelet decomposition on impact signals, the time delay was calculated by Lamb wave of A0 mode in D5 detail signals, and the velocity of A0 mode wave was determined by the dispersion curve for 1 mm thick aluminum plate. Finally, the impact point was located to experimentally verify the effectiveness of the proposed localization method. Experimental results show that the hyperbolic positioning algorithm based on TDoA can accurately identify impact locations, and wavelet decomposition can effectively identify single mode Lamb waves. The proposed method has the characteristics of fastness and accuracy, which is suitable for single object impact localization on thin plate structures.

A novel wavelet packet transform based fault identification procedures in HV transmission line based on current signals

<p>This paper presents an adaptive fault identification algorithm bases on wavelet packet transform (WPT) for two-terminal power transmission lines. The proposed scheme performs four functions which are the fault detection, fault classification, distinguishing among the temporary and the permanent faults, and detection of the arc extinguish instant. The presented algorithm only uses the measured current at one terminal reducing the required cost. Also, it can mitigate the error resulting from the load variations via updating the presetting value. Consequently, it does not need retesting under changing the transmission system configurations. The proposed scheme is deduced in the spectral domain and depended on the application of the WPT. The db6 wavelet packet is used for decomposing the faulty phase current waveform (level 7) to get the energy coefficients. The presented algorithm is assessed under various fault conditions such as fault distances, inception angles, and faults nature via simulating different secondary arc models via using ATP/EMTP. The obtained results are investigated and evaluated.</p>