Standard Thresholding Methods Research Articles

Predicting Risks of Dry Eye Disease Development Using a Genome-Wide Polygenic Risk Score Model.

The purpose of this study was to conduct a large-scale genome-wide association study (GWAS) and construct a polygenic risk score (PRS) for risk stratification in patients with dry eye disease (DED) using the Taiwan Biobank (TWB) databases. This retrospective case-control study involved 40,112 subjects of Han Chinese ancestry, sourced from the publicly available TWB. Cases were patients with DED (n = 14,185), and controls were individuals without DED (n = 25,927). The patients with DED were further divided into 8072 young (<60 years old) and 6113 old participants (≥60 years old). Using PLINK (version 1.9) software, quality control was carried out, followed by logistic regression analysis with adjustments for sex, age, body mass index, depression, and manic episodes as covariates. We also built PRS prediction models using the standard clumping and thresholding method and evaluated their performance (area under the curve [AUC]) through five-fold cross-validation. Eleven independent risk loci were identified for these patients with DED at the genome-wide significance levels, including DNAJB6, MAML3, LINC02267, DCHS1, SIRPB3P, HULC, MUC16, GAS2L3, and ZFPM2. Among these, MUC16 encodes mucin family protein. The PRS model incorporated 932 and 740 genetic loci for young and old populations, respectively. A higher PRS score indicated a greater DED risk, with the top 5% of PRS individuals having a 10-fold higher risk. After integrating these covariates into the PRS model, the area under the receiver operating curve (AUROC) increased from 0.509 and 0.537 to 0.600 and 0.648 for young and old populations, respectively, demonstrating the genetic-environmental interaction. Our study prompts potential candidates for the mechanism of DED and paves the way for more personalized medication in the future. Our study identified genes related to DED and constructed a PRS model to improve DED prediction.

Remote Sensing Extraction of Crown Planar Area and Plant Number of Papayas Using UAV Images with Very High Spatial Resolution

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu’s method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology.

P15.15.B DIFFUSE ADULT-TYPE GLIOMA INFILTRATION DEFINED ON FET-PET

Abstract BACKGROUND O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-PET is a valuable tool in glioma imaging used to the definition of biopsy site, monitoring of treatment response and differentiation of treatment-related changes from glioma progression The physiological uptake within reactive astrogliosis may limits FET-PET ability to precisely define glioma extent. For the implementation of FET-PET into clinical trials, a methodological PET standardization is urgently needed to enable more precise and reproducible FET PET quantification. The primary aim of our study was to define a glioma infiltration using threshold based method between astrogliosis and tumoral tissue in FET-PET. RESULTS Astrogliosis presented similar uptake to grade 2 glioma samples. Standard threshold method (1,6x uptake in contralateral brain) in PET aquisiton 40-60 minutes post injection is effective but the accuracy decrease outside contrast enhancement within FLAIR to 56% sensitivity and 41% specificity. Adding second aquisition increased sensitivity within FLAIR to 71%. Finally, using tumor-to-plexus ratio, it is possible to differentiate astrogliosis and grade 2, and achieve sensitivity of 73% and specificty of 84% within FLAIR or 90% sensitivity with 61% specificity. ROC curves are significantly different related to method used with best results specific to new strategy. CONCLUSION Determining glioma extent using standard threshold method in PET or FLAIR is limited. Improving is possible with dual aquisition and/or different tumor to background ratio. This may largely improve delineation of glioma for surgery or radiotherapy.

Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits.

Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate (Lassosum, sparse LDpred2, and the standard clumping and thresholding) methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs.

Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels Using Localization With a Classifier and Thresholding

Surface cracks are a common sight on public infrastructure nowadays. Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods. Those methods are used to segment surface cracks from their background, making them easier to localize. However, a common issue is that to create a well-functioning algorithm, the training data needs to have detailed annotations of pixels that belong to cracks. Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels. First, we use the classifier to create a rough crack localization map by using its class activation maps and a patch based classification approach and fuse this with a thresholding based approach to segment the mostly darker crack pixels. The classifier assists in suppressing noise from the background regions, which commonly are incorrectly highlighted as cracks by standard thresholding methods. Then, the pseudo labels can be used in an end-to-end approach when training a standard CNN for surface crack segmentation. Our method is shown to yield sufficiently accurate pseudo labels. Those labels, incorporated into segmentation CNN training using multiple recent crack segmentation architectures, achieve comparable performance to fully supervised methods on four popular crack segmentation datasets.

Extracellular voltage thresholds for maximizing information extraction in primate auditory cortex: implications for a brain computer interface

Objective. Research by Oby (2016 J. Neural. Eng. 13 036009) demonstrated that the optimal threshold for extracting information from visual and motor cortices may differ from the optimal threshold for identifying single neurons via spike sorting methods. The optimal threshold for extracting information from auditory cortex has yet to be identified, nor has the optimal temporal scale for representing auditory cortical activity. Here, we describe a procedure to jointly optimize the extracellular threshold and bin size with respect to the decoding accuracy achieved by a linear classifier for a diverse set of auditory stimuli. Approach. We used linear multichannel arrays to record extracellular neural activity from the auditory cortex of awake squirrel monkeys passively listening to both simple and complex sounds. We executed a grid search of the coordinate space defined by the voltage threshold (in units of standard deviation) and the bin size (in units of milliseconds), and computed decoding accuracy at each point. Main results. The optimal threshold for information extraction was consistently near two standard deviations below the voltage trace mean, which falls significantly below the range of three to five standard deviations typically used as inputs to spike sorting algorithms in basic research and in brain-computer interface (BCI) applications. The optimal binwidth was minimized at the optimal voltage threshold, particularly for acoustic stimuli dominated by temporally dynamic features, indicating that permissive thresholding permits readout of cortical responses with temporal precision on the order of a few milliseconds. Significance. The improvements in decoding accuracy we observed for optimal readout parameters suggest that standard thresholding methods substantially underestimate the information present in auditory cortical spiking patterns. The fact that optimal thresholds were relatively low indicates that local populations of cortical neurons exhibit high temporal coherence that could be leveraged in service of future auditory BCI applications.

How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images

The topic of microbial hotspots in soil requires not only visualizing their spatial distribution and biochemical analyses, but also statistical approaches to identify these hotspots and separate them from the surrounding activities (background). We hypothesized that each hotspot type (e.g. enzyme activities in the rhizosphere, root exudation, localization of herbicide accumulation) is a result of local process driven by biotic and/or abiotic factors, and the process rates in the hotspots are much faster than those in the soil background. We further hypothesized that the background and hotspot activities in soil belong to different statistical distributions. Consequently, hotspot determination should be based on statistical separation of activities significantly higher than the background. We analyzed for the statistical distributions of grey values on three groups of published images: 1) 14C images of carbon input by roots into the rhizosphere, 2) 14C glyphosate accumulation in the plant, and 3) zymogram of leucine aminopeptidase activity in rooted soil. The two Gaussian distributions were fit (the first representing the background, the second the hotspots) to the distribution of grey values in the images, the parameters (means and standard deviations, SD) of the fitted distributions were calculated, and the background was removed. Thus, we identified hotspots as areas outside of the Mean+2SD image intensity (corresponding to the upper ~ 2.5% of activity, being over 97.5% of background values) and finally, visualized images of solely hotspot locations. Finally, these results were compared with previously used decisions on hotspot intensity thresholding (i.e. Top-25% and 17 standard thresholding approaches in ImageJ) and discussed the advantages of the Mean+2SD as well as Mean+3SD approaches. These advantages include: i) simple unification of the thresholding approach for several imaging methods with various principles of activity distribution, ii) identification of hotspots with various activity levels, iii) analysis of “time-specific” hotspots in temporal sequences of images. Compared with 17 standard thresholding methods, we concluded that objectively elucidating and separating the hotspots should be based on statistical distribution analysis, e.g. using the Mean+2SD or Mean+3SD approaches. This simple Mean+2SD approach delivered suitable results for three groups of images and so, helps to understand the processes responsible for the highest activities and elucidate hotspots.

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Abstract. Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products.

An Argo mixed layer climatology and database

AbstractA global climatology and database of mixed layer properties are computed from nearly 1,250,000 Argo profiles. The climatology is calculated with both a hybrid algorithm for detecting the mixed layer depth (MLD) and a standard threshold method. The climatology provides accurate information about the depth, properties, extent, and seasonal patterns of global mixed layers. The individual profile results in the database can be used to construct time series of mixed layer properties in specific regions of interest. The climatology and database are available online at http://mixedlayer.ucsd.edu. The MLDs calculated by the hybrid algorithm are shallower and generally more accurate than those of the threshold method, particularly in regions of deep winter mixed layers; the new climatology differs the most from existing mixed layer climatologies in these regions. Examples are presented from the Labrador and Irminger Seas, the Southern Ocean, and the North Atlantic Ocean near the Gulf Stream. In these regions the threshold method tends to overestimate winter MLDs by approximately 10% compared to the algorithm.

Fiducial markers vs. PET/CT for esophageal cancer GTV delineation for radiotherapy treatment planning using a standard SUV threshold and background uptake method.

70 Background: Often in esophageal targets that move with respiration, endoscopically implanted fiducial markers are placed under ultrasound guidance within 1 cm from the superior and inferior tumor border. Our group has previously reported the stability of this method. 3D 18F-FDG PET/CT is obtained for initial staging of esophageal cancer and has altered the tumor volume in many sites. The correlation of these two methods for GTV delineation is currently undefined. Methods: Twenty-one patients with esophageal cancer were selected for this retrospective, IRB-approved analysis. Each patient underwent fiducial placement (by one of six endoscopists) at the inferior and superior borders of the tumor and received PET/CT prior to Radiotherapy (RTx). PET/CT images were imported into an image analysis software system for measurements. The distance between fiducial markers and MTV defined by 2.5 SUV contour (method 1) and MTV using a threshold above mean liver uptake (method 2) was calculated. The fiducials were located on CT. The distance between the centroid and MTV border was measured. The Concordance Correlation Coefficient (CCC) was calculated to determine the correlation between results from the two methods. Results: In method 1 the median distance between MTV (2.5 SUV) and fiducials was 1.0 cm (0.0 cm - 2.8 cm) and 1.0 cm (0.0 cm - 6.9 cm) for inferior and superior borders, respectively. In method 2 median distance between MTV and fiducials was 1.14 cm (0.0 cm - 2.62 cm) and 1.64 cm (0.33 cm – 6.84 cm) for inferior and superior borders, respectively. The CCC indicated poor agreement between methods for both distances (0.57 and 0.87 for inferior fiducial distance and superior fiducial distance, respectively). There was no correlation between MTV-to-fiducial distances greater than 2 cm and the endoscopist that performed the fiducial implantation. Conclusions: Although the methods of defining the MTV had poor strength of agreement, both methods demonstrated a robust correlation between inferior fiducial marker placement and inferior PET/CT uptake. The superior fiducial marker and MTV border illustrated discordance – unrelated to the endoscopist.

Global gray-level thresholding based on object size.

In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes. © 2016 International Society for Advancement of Cytometry.

Concentrations of Available Heavy Metals in Mediterranean Agricultural Soils and their Relation with Some Soil Selected Properties: A Case Study in Typical Mediterranean Soils

The characterization of the content of trace metals in soils is an instrument in many programs of environmental protection, including the establishment of regional-level standards to detect sites affected by contamination. The objectives of the present study were to study the available levels of Cd, Cr, Cu, Ni, Pb, and Zn in surface horizons of agricultural soils in a typical European Mediterranean region, to establish the geochemical baseline concentration (GBC), background level (BL), and reference value (RV) of each of these available metals, and to investigate their possible correlations with soil properties. To establish the GBC and RV values, we used the “standard threshold method”. Topsoil samples (0–20 cm) were collected from 630 sites, and extracted with Diethylene Triamine Pentaacetic Acid (DTPA) to determine their available heavy metal concentrations. The GBC values established were: 0.04 to 0.90 mg kg−1, 0.70 to 2.50 mg kg−1, 0.10 to 6.30 mg kg−1, 0.30 to 7.90 mg kg−1, 0.29 to 4.50 mg kg−1, and 0.18 to 2.50 mg kg−1 for Cd, Cr, Cu, Ni, Pb, and Zn, respectively. Soil properties were found to be correlated with the available heavy metal content, suggesting that the enhanced mobility of heavy metals are related to anthropic activities.

Southern Ocean jets and how to find them: Improving and comparing common jet detection methods

AbstractThis study undertakes a detailed comparison of different methods used for detecting and tracking oceanic jets in the Southern Ocean. The methods under consideration are the gradient thresholding method, the probability density function (PDF) method, and the contour method. Some weaknesses of the gradient thresholding method are discussed and an enhancement (the WHOSE method), based on techniques from signal processing, is proposed. The WHOSE method is then compared to the other three methods. Quantitative comparison is undertaken using synthetic sea‐surface height fields. The WHOSE method and the contour method are found to perform well even in the presence of a strong eddy field. In contrast, the standard gradient thresholding and PDF methods only perform well in high signal‐to‐noise ratio situations. The WHOSE, PDF, and the contour methods are then applied to data from the eddy‐resolving Ocean General Circulation Model for the Earth Simulator. While the three methods are in broad agreement on the location of the main ACC jets, the nature of the jet fields they produce differ. In particular, the WHOSE method reveals a fine‐scale jet field with complex braiding behavior. It is argued that this fine‐scale jet field may affect the calculation of eddy diffusivities. Finally, recommendations based on this study are made. The WHOSE and gradient thresholding methods are more suitable for the study of jets as localized strong currents, useful for studies of tracer fluxes. The contour and PDF methods are recommended for studies linking jets to hydrographic fronts.

Improved Wavelet Threshold for Gray Scale Image Denoising

During the image acquisition and communication the image is corrupted by noise. This is a classical problem in the field of signal or image processing. A new compromise threshold method that improves performance is presented, based on the standard compromise threshold method, yet more flexible and easier to treat mathematically. The improved adaptive wavelet compromise method avoids the discontinuity of the hard-threshold method, at the same time increase the correlation judgment process, improve image details loss problem. Simulation results show that the improved method is better at the adaptability, at the same time increase the correlation judgment process, improve image details loss problem.

Alternative thresholding methods for fMRI data optimized for surgical planning

Current methods for thresholding functional magnetic resonance imaging (fMRI) maps are based on the well-known hypothesis-test framework, optimal for addressing novel theoretical claims. However, these methods as typically practiced have a strong bias toward protecting the null hypothesis, and thus may not provide an optimal balance between specificity and sensitivity in forming activation maps for surgical planning. Maps based on hypothesis-test thresholds are also highly sensitive to sample size and signal-to-noise ratio, whereas many clinical applications require methods that are robust to these effects. We propose a new thresholding method, optimized for surgical planning, based on normalized amplitude thresholding. We show that this method produces activation maps that are more reproducible and more predictive of postoperative cognitive outcome than maps produced with current standard thresholding methods.

Acute Stroke: Automatic Perfusion Lesion Outlining Using Level Sets

To develop a user-independent algorithm for the delineation of hypoperfused tissue on perfusion-weighted images and evaluate its performance relative to a standard threshold method in simulated data, as well as in acute stroke patients. The study was approved by the local ethics committee, and patients gave written informed consent prior to their inclusion in the study. The algorithm identifies hypoperfused tissue in mean transit time maps by simultaneously minimizing the mean square error between individual and mean perfusion values inside and outside a smooth boundary. In 14 acute stroke patients, volumetric agreement between automated outlines and manual outlines determined in consensus among four neuroradiologists was assessed with Bland-Altman analysis, while spatial agreement was quantified by using lesion overlap relative to mean lesion volume (Dice coefficient). Performance improvement relative to a standard threshold approach was tested with the Wilcoxon signed rank test. The mean difference in lesion volume between automated outlines and manual outlines was -9.0 mL ± 44.5 (standard deviation). The lowest mean volume difference for the threshold approach was -25.8 mL ± 88.2. A significantly higher Dice coefficient was observed with the algorithm (0.71; interquartile range [IQR], 0.42-0.75) compared with the threshold approach (0.50; IQR, 0.27- 0.57; P , .001). The corresponding agreement among experts was 0.79 (IQR, 0.69-0.83). The perfusion lesions outlined by the automated algorithm agreed well with those defined manually in consensus by four experts and were superior to those obtained by using the standard threshold approach. This user-independent algorithm may improve the assessment of perfusion images as part of acute stroke treatment. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13121622/-/DC1.

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantisation is easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, non-invertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantisation level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantisation and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone.

A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models.

This paper presents a novel computer vision algorithm to analyze 3D stacks of confocal images of fluorescently stained single cells. The goal of the algorithm is to create representative in silico model structures that can be imported into finite element analysis software for mechanical characterization. Segmentation of cell and nucleus boundaries is accomplished via standard thresholding methods. Using novel linear programming methods, a representative actin stress fiber network is generated by computing a linear superposition of fibers having minimum discrepancy compared with an experimental 3D confocal image. Qualitative validation is performed through analysis of seven 3D confocal image stacks of adherent vascular smooth muscle cells (VSMCs) grown in 2D culture. The presented method is able to automatically generate 3D geometries of the cell's boundary, nucleus, and representative F-actin network based on standard cell microscopy data. These geometries can be used for direct importation and implementation in structural finite element models for analysis of the mechanics of a single cell to potentially speed discoveries in the fields of regenerative medicine, mechanobiology, and drug discovery.

A Novel Algorithm of Fractal-Wavelet Image Denosing

Image denosing is the first preprocessing step in dealing with image processing where the overall system quality should be improved. So it is a key issue in all image processing researches. Over the past years, fractal-wavelet transforms were introduced in an effort to reduce the blockiness and computational complexity that are inherent in fractal image compression. The essence of fractal image denosing is to predict fractal code of a noiseless image from its noisy observation. From the predicted fractal code, we can generate an estimate of the original image. In the paper, we show how well fractal-wavelet denosing predicts parent wavelet subtrees of the noiseless image. The performance of various fractal-wavelet denosing schemes is compared to that of some standard wavelet thresholding methods. From the several of experimental results, these fractal-based image denosing methods are quite competitive with standard wavelet thresholding methods for image denosing.

Kurtosis-based noise estimation and multiscale energy to denoise ECG signal

In this work, a novel wavelet-based denoising method for electrocardiogram signal is proposed. A threshold is derived by considering energy contribution of a wavelet subband, noise variance which is based on a novel Gaussian measure, Kurtosis, and number of samples. The robust noise estimator, median absolute deviation, is scaled by a normalized wavelet subband Kurtosis instead of conventional statistical quantile function for Gaussian distribution. Signal distortion is evaluated using percentage root mean square difference (PRD), wavelet weighted percentage root mean square difference (WWPRD), and wavelet energy-based diagnostic distortion (WEDD) measures. The results are compared with existing standard thresholding methods. The lowest PRD, WWPRD, and WEDD values are achieved as 9.523, 17.743, and 4.000% for lead-V2, lead-V3, and lead-II signal, respectively. For validation, spatially nonhomogeneous functions like Blocks, Bumps, HeaviSine, and Doppler with noise are evaluated.