Daubechies Wavelet Research Articles

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals.

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise

Considering that it is easily disturbed by various engineering factors such as weather, hydrology, and construction during engineering monitoring, the collected subsidence data contain various noises. In order to reduce the influence of engineering noise on the accuracy of subsidence prediction, it is proposed to use the Daubechies (DB) wavelet to decompose the original subsidence time series; the items with the low-frequency trend, after decomposition, are predicted using long short-term memory (LSTM) model, items with high-frequency noise used the autoregressive (AR) time series model to make predictions, and the prediction results of the low-frequency trend term and the high-frequency noise term are summed to obtain the total time series predicted value. Combining the actual engineering subsidence monitoring data of the old goaf, compared with the prediction results of the LSTM and RNN models without DB wavelet decomposition and the gray model GM (1,1), the results show that the DB wavelet has an obvious improvement effect in reducing the influence of measurement data noise on prediction error. Compared with the single prediction model LSTM, RNN, and GM (1,1), the proposed prediction model has higher prediction accuracy, smaller error, and better trend. It can be used as a calculation method to improve the prediction accuracy of surface subsidence in old goaf.

Highly Sensitive Hydrogen Sensing Based on Tunable Diode Laser Absorption Spectroscopy with a 2.1 μm Diode Laser

As a new form of energy, hydrogen (H2) has clean and green features, and the detection of H2 has been a hot topic in recent years. However, the lack of suitable laser sources and the weak optical absorption of H2 limit the research concerning its detection. In this study, a continuous-wave distributed feedback (CW-DFB) diode laser was employed for sensing H2. Tunable diode laser absorption spectroscopy (TDLAS) was adopted as the detection technique. The strongest H2 absorption line, located at 4712.90 cm−1 (2121.83 nm, line strength: 3.19 × 10−26 cm−1/cm−2 × molec), was selected. We propose a H2-TDLAS sensor based on the wavelength modulation spectroscopy (WMS) technique and a Herriott multipass gas cell (HMPC) with an optical length of 10.13 m to achieve a sensitive detection. The WMS technique and second harmonic (2f) demodulation technique were utilized to suppress system noise and simplify the data processing. The 2f signal of the H2-TDLAS sensor, with respect to different H2 concentrations, was measured when the laser wavelength modulation depth was at the optimal value of 0.016 cm−1. The system’s signal-to-noise ratio (SNR) and minimum detection limit (MDL) were improved from 248.02 and 0.40% to 509.55 and 0.20%, respectively, by applying Daubechies (DB) wavelet denoising, resulting in 10 vanishing moments. The Allan variance was calculated, and the optimum MDL of 522.02 ppm was obtained when the integration time of the system was 36 s.

A Wavelet-Based Steganographic Method for Text Hiding in an Audio Signal.

The developed method of steganographic hiding of text information in an audio signal based on the wavelet transform acquires a deep meaning in the conditions of the use by an attacker of deliberate unauthorized manipulations with a steganocoded audio signal to distort the text information embedded in it. Thus, increasing the robustness of the stego-system by compressing the steganocoded audio signal subject to the preservation of the integrity of text information, taking into account the features of the psychophysiological model of sound perception, is the main objective of this scientific research. The task of this scientific research is effectively solved using a multilevel discrete wavelet transform using adaptive block normalization of text information with subsequent recursive embedding in the low-frequency component of the audio signal and further scalar product of the obtained coefficients with the Daubechies wavelet filters. The results of the obtained experimental studies confirm the hypothesis, namely that it is proposed to use recursive embedding in the low-frequency component (approximating wavelet coefficients) followed by their scalar product with wavelet filters at each level of the wavelet decomposition, which will increase the average power of hidden data. It should be noted that upon analyzing the existing method, which is based on embedding text information in the high-frequency component (detailed wavelet coefficients), at the last level of the wavelet decomposition, we obtained the limit CR = 6, and in the developed, CR = 20, with full integrity of the text information in both cases. Therefore, the resistance of the stego-system is increased by 3.3 times to deliberate or passive compression of the audio signal in order to distort the embedded text information.

Automatic detection of auroral Pc5 geomagnetic pulsation using machine learning approach guided with discrete wavelet transform

Geomagnetic micropulsations are ultra-low frequency (ULF) signals observed in the magnetosphere as well as on the ground. These signals offer us an effective means to study the coupling of the magnetosphere-ionosphere processes in the space field. The most prominent observed type of such ULF waves is the Pc5 pulsations (with a frequency range of 1–7 mHz), known to have their maximum amplitude in the auroral oval. The low magnitude of Pc5 signals withstands against distinguishing them from the background noise. This study presents a machine learning approach for the automatic detection of geomagnetic Pc5 pulsations in the auroral zone using artificial neural networks (ANN) guided by discrete wavelet transform. Our ANN algorithm is validated and tested using a huge amount of datasets of auroral ground-based geomagnetic records from the Svalbard network during the two solar cycles 23 and 24. The wavelet-based coherence was used to determine the signal’s consistency detected by the magnetometer stations of the Svalbard network; since they are sensitive to all sorts of space wave-related phenomena that left their footprint on the magnetic field time series. The Daubechies wavelet transform was utilized to classify and extract Pc5 signals from the artificial noise and the results are correlated with the geomagnetic pulsation records as detected by our ANN-based model. The ANN-based model showed a good correlation of an average of 98% for the different phases of the two studied solar cycles. The statistical regression analysis of the post-processed results yielded a high coefficient of determination of R2 = 0.9 and norms of residuals of 8–21 nT. The Pc5 events detected by the ANN-based algorithm during the two solar cycles showed a good correlation with the Kp index, which enables our model for space weather forecasting.

Wavelet based Video Compression techniques for Industrial monitoring applications

The principle of industrial machine monitoring and vehicle camera transmission system are focusing on this paper. Video compression is widely used in many industrial applications like continuous monitoring of machines which consumes more storage by capturing every motion detection in machine, hence video coding is highly recommended for video compression without any loss in the actual video. By using wavelet transform delivers the superior localization both in frequency and time domain and its results showcases the better performance while comparing with discrete cosine transform. A comparative analysis has been carried on Video compression using Haar and orthogonal (Daubechies) wavelet. Duplicate coefficients of discrete wavelet transform is reduced by using quantization technique. It aims to attain minimum error while preserving the high peak signal to noise ratio and image quality in the acceptable range. Using PSNR as measure of quality, this paper shows that Daubechies wavelet provides the better quality of video compared to Haar wavelet. Evaluation of performance is depends upon on compression ratio, PSNR, MSE, and SSIM.

Important role of turbulent wind gust and its coherent structure in the rapid removal of urban air pollution

The removal process of urban air pollution is very rapid even under the strong obstruction of various buildings in the city. In this study, based on high-accuracy ultrasonic anemometer observations at seven different altitudes on a 325 m tower, we proposed a physical mechanism in which turbulent wind gust and its coherent structure may play important roles in this phenomenon. Pollutants suspended within the urban canopy layer UCL cannot be quickly removed by means of basic flow but diffuse and crosse the UCL by means of the coherent structure of gust wind and then propagate further under strong basic flows above the UCL. With a continuous increase in wind speed in the whole layer, combined with gust-wind and turbulence mixing, the rapid transformation from heavily polluted days to clean days in urban areas can take place in just a few hours. Turbulent wind gust and its coherent structure were extracted by third-order Daubechies wavelet transformation. The downwind wind was decomposed into three separate components, basic flow gust-wind and turbulence The results show that in the early rapid drop of urban surface pollutant concentration, horizontal wind speed at 8 m was still low, and sinking movements dominated in the lower layer. Friction velocity calculated from gust-wind significantly affected the surface PM2.5 concentration, and was the most obvious factor with an inverse correlation coefficient −0.56. The correlation coefficient of coherent structure of gustiness Ru g w g at 8 m basically changed to positive values before the rapid decline of the surface PM2.5 concentration. The concentration of PM2.5 decreased significantly when Ru g w g > 0, and exhibited an increasing trend when Ru g w g < 0. The close relationship between the concentration of PM2.5 and Ru g w g indicated the important role of the coherent structure of gustiness in the rapid removal process of urban pollutants. The research deepens the vertical exchange process of matter and energy in the UCL, and provides a scientific reference for the rapid dissipation mechanism of urban air pollution.

Comparison of Static Signature Identification using Artificial Neural Networks Based on Haar, Daubechies and Symlets Wavelet Transformations

Signature is a biometric attribute that is quite important for each individual that can be used as self-identity. Until now, the signature is still used as a sign of legal approval and is agreed upon by everyone. This makes the signature worthy of attention from a security aspect. Various approaches have been proposed in the development of signature identification to minimize signature forgery. This study will discuss the identification of signatures by using the image of the signature on paper. This identification consists of two processes, namely training and testing by utilizing Artificial Neural Networks Backpropagation and Wavelet Transform. Optimal results are obtained by using ANN which has learning rate 0,09, two hidden layers, each 20 and 10 nodes with the most superior Wavelet Haar reaching 94.44%.

Accurate and Efficient Quantum Computations of Molecular Properties Using Daubechies Wavelet Molecular Orbitals: A Benchmark Study against Experimental Data

Although quantum computation (QC) is regarded as a promising numerical method for computational quantum chemistry, current applications of quantum-chemistry calculations on quantum computers are limited to small molecules. This limitation can be ascribed to technical problems in building and manipulating more qubits and the associated complicated operations of quantum gates in a quantum circuit when the size of the molecular system becomes large. As a result, reducing the number of required qubits is necessary to make QC practical. Currently, the minimal STO-3G basis set is commonly used in benchmark studies because it requires the minimum number of spin orbitals. Nonetheless, the accuracy of using STO-3G is generally low and thus cannot provide useful predictions. We propose to adopt Daubechies wavelet functions as an accurate and efficient method for QCs of molecular electronic properties. We demonstrate that a minimal basis set constructed from Daubechies wavelet basis can yield accurate results through a better description of the molecular Hamiltonian, while keeping the number of spin orbitals minimal. With the improved Hamiltonian through Daubechies wavelets, we calculate vibrational frequencies for H$_2$ and LiH using quantum-computing algorithm to show that the results are in excellent agreement with experimental data. As a result, we achieve quantum calculations in which accuracy is comparable with that of the full configuration interaction calculation using the cc-pVDZ basis set, whereas the computational cost is the same as that of a STO-3G calculation. Thus, our work provides a more efficient and accurate representation of the molecular Hamiltonian for efficient QCs of molecular systems, and for the first time demonstrates that predictions in agreement with experimental measurements are possible to be achieved with quantum resources available in near-term quantum computers.

Multiresolution finite wavelet domain method for efficient modeling of guided waves in composite beams

A Multiresolution finite wavelet domain meshless approach is presented for the simulation of guided waves in composite beams. The Daubechies wavelet and scaling functions are both employed as basis functions and their remarkable properties for the hierarchical spatial approximation of state variables are explored. The first level of the multiresolution approximation provides the coarse solution, while finer resolution components are sequentially calculated and superimposed on the coarse solution, until the desired level of accuracy is achieved. The development of the multiresolution wavelet-based beam element that encompasses first-order shear deformation theory kinematic assumptions is described, and the multiresolution equations of motion including stiffness and mass matrices are formulated. Numerical results for the simulation of guided wave propagation in aluminum and laminated composite beams are presented, demonstrating substantial reduction in computational effort compared to single resolution approaches and traditional finite elements. Convergence studies using various Daubechies wavelet interpolation functions, are evincing extraordinary convergence rates with very low requirements of grid points per wavelength. Furthermore, additional benefits of the proposed method are encompassed in the analysis of multiple guided wave modes, attributed to the low-pass filter behavior of the scaling functions and the band-pass filter behavior of the wavelet functions that can result in isolation of different wave modes by the individual resolution components of the total solution.

FAULT DETECTION BASED ON THE DISCRETE WAVELET TRANSFORM FOR THE CONVERTER OF A WIND POWER GENERATOR SYSTEM OPERATING UNDER DISTURBANCES IN THE GRID

In this work, an open-circuit fault detection scheme for the converter of a wind power generation system is proposed and evaluated, which is robust to false detections under grid disturbance conditions such as sags and swells. The wind energy system is composed of a doubly-fed induction generator (DFIG) that supplies energy to the electrical grid through a power electronics converter based on the neutral point-clamped topology in a back-to-back configuration (NPC-BTB). The converter operates with a field-oriented-control (FOC) scheme, whereas the fault detection method is based on the processing of the current signals flowing through the converter at the rotor and stator sides of the generator using the discrete wavelet transform (DWT) with the Haar and Daubechies4 wavelet types. The fault detection scheme is evaluated verifying that false detections do not arise, analyzing cases where magnitude variations in the grid voltage of 15%, 30% and 50% occur, with respect to the nominal operating condition.

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

&lt;span lang="EN-US"&gt;Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a &lt;a name="_Hlk88480766"&gt;&lt;/a&gt;linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.&lt;/span&gt;

A wavelet-based set of constructive masses for the Minkowskian Feynman propagator

We continue our pursuit of Minkowskian quantum field theory—specifically, a nonperturbative analysis of the characteristic functional generating the time-ordered Wightman distributions for the quartic scalar field interaction. As in a previous paper, our starting point is the Minkowskian Feynman propagator for the free scalar field theory analyzed by a system of expansion functions in space-time. Here, we choose a significantly different system of expansion functions. We start with an L2-orthonormal wavelet basis {Ψw:w∈W}, where each Ψw has the properties of a Daubechies wavelet in the temporal coordinate and the properties of a Meyer wavelet in the spatial coordinates. We consider the matrix elements Gww′(m) of the mass-m Feynman propagator based on the expansion functions vw=Lw−1Ψw, where Lw denotes the length scale of w∈W. For every finite Λ⊂W, we let GΛ(m) denote the Λ × Λ matrix of such elements. We define a constructive mass to be any value of m such that GΛ(m) is nonsingular for each finite Λ⊂W. We prove that the set of such masses is at least co-countable (although we expect every mass value to be constructive). For an arbitrary constructive mass m and an arbitrary finite Λ⊂W, we rigorously construct the interacting characteristic functional as an integral in amplitudes, where the Λ-cutoff on the quartic interaction is supplemented by a cutoff on the free-field action defined by GΛ(m)−1.

Earthquake-induced nonlinear dynamic response assessment of structures in terms of discrete wavelet transform

Nonlinear dynamic analysis (NDA) needs a large computation time and volume. In this study, an innovative method is proposed based on utilizing discrete wavelet transform to reduce the computational volume and time of NDA. For this purpose, several far- and near-field ground motion records are selected and decomposed into three levels using Wavelet Daubechies 4. In this way, several single-degree-of-freedom systems are generated and modeled using OpenSees with different beam-to-column stiffness ratios (six states). Then, the nonlinear pseudo-velocity response spectra are plotted for two ductility coefficients to show the ability of the proposed method. The results indicate that the nonlinear response spectral error of the near-field earthquakes for all the natural period intervals is less than those of the far-field earthquakes. Also, the error value for the structures, with a period of more than 0.5 sec, is less than 10%. The results also show that the error value of the wavelet nonlinear response spectrum could be neglected by increasing the ductility coefficient of the structures. Also, it is concluded that the error value is about 10% when utilizing incremental dynamic analysis, which is not significant compared to the 70% reduction in the computational time. Consequently, in analyzing and designing structures with a natural period of more than 0.5 sec, the A3 level of decomposition is recommended. In contrast, the A2 level of decomposition is suggested for the structures with a natural period of less than 0.5 sec.

Comparison of Time Series Models before and after Using Wavelet Shrinkage Filtering to Forecast the Amount of Natural Gas in Iraq

The procedure of reducing noise or reduction before analyzing the time series is important to get accurate and reliable outcomes when building models. Wavelet Shrinkage consisting of wavelets with thresholding is a powerful mathematical method used to reduce noise that can be exposed. It has time-series observations and selects the cut-off threshold level to be suitable for removing most noise. In this paper, natural gas production data in Iraq were used during (1981-2019), which included non-stationary data or outliers, so the researcher must treat this problem before starting the analysis. Thus, the researcher selected the way of time series analysis using Box- Jenkins (ARIMA (p,d,q) models and the method of wavelet shrinkage in the two methods of cutting the threshold before and after filtering wavelet shrinkage to choose the most suitable mathematical model that defines the study data. The paper was concerned with finding an efficient model by comparing the (Box-Jenkins) linear ARIMA (p, d, q) models estimated from the time series data before and after filtering the wavelet shrinkage, and then decreasing the rank of the estimated model from the candidate observations (While maintaining the accuracy and suitability of the estimated models) and re-comparing it with the estimated linear models of the original observations, and then measuring the most efficient model based on some statistical criteria, including the AIC (Akaike information criterion), SBIC (Schwarz information criterion, Bayesian information criterion BIC)and HQIC (Hannan-Quinn information criterion). Statistical programs such as Statgraphics XVII - X64 and MATLAB were used to analyze the data. The paper reached the efficiency of wavelet shrinkage filters in treating the noise problem and getting efficient estimated models, specifically the wavelet shrinkage filter (Daubechies(db8)) with a soft-threshold cut-off estimated by the fixed-form method, and the possibility of obtaining linear models with lower orders and higher efficiency for the filtered observations compared to the corresponding models estimated from the original observations, that is, the suitable model for the data is a model ARIMA(0,1,1) for the data paved in a Daubechies method, i.e., it is preferable to do the waveform analysis of the data before analyzing the time series concerning the observations of natural gas production in Iraq The preference was given to the model estimated by Box and Jenkins ARIMA (0,1,1) model using wavelet shrinkage. Daubechies (db8) wavelet reduction

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

As a high-precision gravity measuring device, a marine atomic gravimeter is highly sensitive to vibration signals. Accurate measurement and analysis of vibration signal is the primary condition to realize vibration compensation and vibration suppression. Denoising plays a crucial role in the processing of these vibration signals. The vibration signals of a marine gravimeter contain numerous nonlinear and nonstationary components. In this paper, a vibration signal denoising method of marine atomic gravimeter based on improved variational mode decomposition (VMD) was put forward to effectively suppress the noise. An improved genetic particle swarm optimization (GPSO) was first adopted for the parametric optimization of VMD by taking minimum permutation entropy (PE) as fitness function and adaptively determining the optimal parameters of VMD. PE was then utilized to calculate the proportion of noise-containing components in the intrinsic mode function (IMF) components obtained by VMD. The components were classified into noise and signal components by searching for the mutation points of two adjacent IMF permutation entropies. On this basis, noise components were denoised by Savitzky-Golay (SG) filter. In the end, the denoised components were reconstructed with the signal components to generate denoised vibration signals. To verify the effectiveness, the proposed method was applied in denoising, simulated and measured vibration signals of a marine atomic gravimeter, and compared with Daubechies (db) wavelet, Symlets (sym) wavelet, and empirical mode decomposition (EMD). The results showed that the proposed method could effectively remove the noise from nonlinear vibration signals and retain the authentic and useful information, so that it was able to provide the supporting data for gravity compensation of marine atomic gravimeter.

Filtered-orthogonal wavelet division multiplexing (F-OWDM) technique for 5G and beyond communication systems.

Filtered-orthogonal frequency division multiplexing (F-OFDM) is one of the most protruding multicarrier modulation (MCM) techniques for fifth-generation and beyond wireless communication. However, it possesses a high peak-to-average power ratio (PAPR), which results in its poor performance. Thus, a novel wavelet based MCM technique, namely filtered orthogonal wavelet division multiplexing (F-OWDM), is proposed as an efficient alternative to conventional F-OFDM (C-F-OFDM) to reduce the PAPR. In this model, the traditional Fourier transforms (FT) as used in C-F-OFDM was replaced with Wavelet Transforms. The proposed F-OWDM system does not require a cyclic prefix because of the overlapping sub-carriers in the time and frequency domains. Thus, the F-OWDM system exhibits higher bandwidth efficiency. In this paper, the performance of the F-OWDM system with various wavelets from different wavelet families under an additive white Gaussian noise and flat fading channel is investigated. The PAPR and bit error rate (BER) of the F-OWDM system using Haar, discrete Meyer, bi-orthogonal, symlet, and Daubechies wavelets are analyzed. A comparative study between F-OWDM and C-F-OFDM is also presented. From the results, it is able to prove that F-OWDM has the advantage of lower PAPR and lower bit error rate than the C-F-OFDM system.

Wavelet transform approach for denoising and decomposition of satellite-derived ocean color time-series: Selection of optimal mother wavelet

Wavelet Transform (WT) has remarkable advantages for feature extraction and denoising of satellite-derived ocean color time series datasets. The selection of mother wavelet (MWT) is the main challenging issue in the wavelet transform analysis of ocean color time series, since different MWT applied to the same dataset may produce different results. In this study, 57 MWTs were investigated to find the optimal wavelet functions for WT analysis of ocean color time series datasets with particular emphasis on the quantitative approaches. The daily, weekly, and monthly averaged of MODIS Level-3 Chlorophyll-a (Chl-a) series were denoised and decomposed by Discrete Wavelet Transform (DWT) using different MWTs. The seasonal trends of all datasets were extracted using the Seasonal Trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) (STL) and Census X-11 methods, used as references. The statistical Cross Correlation Function (CCF), Signal to Noise Ratio (SNR), and Mean Standard Error (MSE), indexes, plus a Magnitude-Shape quantitative model were used to calculate the correlation and similarity between original and corresponding decomposed and denoised reference Chl-a signals. The similarity indexes were analyzed to evaluate the similarity and correlation between reconstructed denoised signals with original Chl-a series, and between DWT seasonal components with the reference seasonal trends. For a more assessment, two different weekly averaged Chl-a series of SeaWiFS satellite sensor over Bermuda-Atlantic region (open ocean waters) and Chesapeake Bay (coastal waters) were evaluated using SeaBASS in situ measurements, and seasonal trends in comparison with corresponding DWT components. The results revealed that the “db10” and “db5” from Daubechies wavelet family were respectively the best options for denoising and decomposition of Chl-a series using WT methods. Also MWTs from Biorthogonal and Reverse biorthogonal wavelet families gave a poor performance, and therefore these functions are not recommended to use in DWT denoising and decomposition of Chl-a signals.

Mathematically Based Assessment of the Accuracy of Protection of Cardiac Data Realized with the Help of Cryptography and Steganography

This paper describes the application of cryptography and steganography in the protection of cardiac databases. The original cardiac data studied were transformed using a Daubechies wavelet transform. The next step is to conduct Energy Packing Efficiency-based compression. A watermark is added to the received data to protect against unauthorized access, before hybrid cryptography is performed using a suitably selected encryption algorithm. The presented and analyzed algorithm includes protection using public and symmetric key cryptography. The proposed software algorithm is performed on real electrocardiographic, photoplethysmographic, and Holter cardio data. We have evaluated the effectiveness of the applied approach and concluded that a sufficient level of protection of the studied data has been achieved, with methods of authentication and accuracy applied to the users.

New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. There is still a great deal of space for further research on this area before reaching a definite decision. This study introduced a novel hybrid framework based on a bidirectional recurrent neural network (BiRNN) with a multilayered dilated convolution neural network (CNN) for arrhythmia classification. Initially, the raw ECG signals are filtered using Chebyshev Type II method and the Daubechies wavelet method is used to solve fractal problems and signal discontinuities. Then, a synthetic signal is generated using a generative adversarial network (GAN) to handle imbalanced signal classes. The proposed Bidirectional RNN with Dilated CNN (BRDC) architecture takes advantage of multilayered dilated CNN and bidirectional RNN units (bidirectional gated recurrent Units, BiGRU, bidirectional long short-term memory, BiLSTM) to generate fusion features and then, fusion features are classified in the fully connected layer. The PhysioNet 2017 challenge (MIT-BIH) dataset is used to train and validate the proposed approach. By combining fusion features with dilated CNN, the proposed approach outperforms the existing model for arrhythmia detection with 99.90 % accuracy, 98.41 % F1-score, 97.96 % precision, and 99.90 % recall. Overall, our hybrid BRDC model provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. In the future, an automatic and cloud-based system with more arrhythmia data variance to test the model&#x2019;s robustness will be given the highest priority.