False Peaks Research Articles

A Novel Method of QRS Detection Using Time and Amplitude Thresholds With Statistical False Peak Elimination

Heartbeats are important aspects for the study of heart diseases in medical sciences as they provide vital information on heart disorders and abnormalities in heart rhythms. Each heartbeat provides a QRS complex in the electrocardiogram (ECG) which is centered at the R-peak. The analysis of ECG is hindered by low-frequency noise, high-frequency noise, interference from P and T waves, and changes in QRS morphology. This paper presents a new peak detection algorithm that can suppress the noise and adapt to changes in ECG signal morphology for better detection performance. The proposed algorithm is based on median and moving average (MA) filtering, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). The filters are first used in preprocessing to reduce unwanted noise and interference. The data is then divided into smaller segments and each segment is then analyzed using two distinct thresholds, a time axis (x-axis) threshold and an amplitude (y-axis) threshold. Next, the false peaks are eliminated resulting from any residue of noise using an average value of peak-to-peak interval. A post-processing stage is added to eliminate any peak that is detected twice and to search for missed low-amplitude peaks. The proposed method is tested on MIT-BIH arrhythmia and Fantasia databases and provides better results in comparison to several state-of-the-art methods in the field. The mean sensitivity, positive predictivity, and detection error rates for the proposed method are 99.82%, 99.88%, and 0.31%, respectively, for the MIT-BIH arrhythmia database and 99.92%, 99.90%, and 0.18%, respectively, for the Fantasia database.

Optimizing PWM Control for Efficiency and Reduction of False Turn-On Events in Synchronous Buck GaN Converters

Half-bridge GaN power converters are susceptible to false turn-on events, which can lead to shoot-through and potentially device-damaging currents. There are three main parameters that can be adjusted in PWM schemes to reduce the likelihood of false turn-on events: negative gate bias, gate resistance, and deadtime. However, these PWM parameters also affect converter efficiency in the inverse way, meaning less false turn-on events must be balanced with lowered efficiency. The novelty of this paper is to investigate the trade-off between reducing GaN false turn-on events (by reducing the transient peak of gate to source voltage) and maximizing the power converter efficiency, which has not been done in prior work. This paper investigates this trade-off using a synchronous buck converter over numerous operating points with variation of the three key PWM parameters. Six converter scenarios are considered with input voltage of 200/400V, switching frequency of 50/100kHz, and output power of 500W/1kW. For each scenario, negative gate bias is set to ™4.4V and −5V, gate on-resistance is set to $10\Omega $ and $12.5\Omega $ , and deadtime is varied at 60ns, 80ns, and 110ns. The results are organized into Pareto plots to find optimal points for efficiency and reduction of false turn-on events. The experimental results show that a further negative gate bias (−5V) most significantly reduces the false turn-on voltage peak and still achieves very high efficiency with appropriate selection of gate resistance and deadtime.

Detection and localization of multiple small damages in beam

Localizing small damages often requires sensors be mounted in the proximity of damage to obtain high Signal-to-Noise Ratio in system frequency response to input excitation. The proximity requirement limits the applicability of existing schemes for low-severity damage detection as an estimate of damage location may not be known a priori. In this work it is shown that spatial locality is not a fundamental impediment; multiple small damages can still be detected with high accuracy provided that the frequency range beyond the first five natural frequencies is utilized in the Frequency response functions (FRF) curvature method. The proposed method presented in this paper applies sensitivity analysis to systematically unearth frequency ranges capable of elevating damage index peak at correct damage locations. It is a baseline-free method that employs a smoothing polynomial to emulate reference curvatures for the undamaged structure. Numerical simulation of steel-beam shows that small multiple damages of severity as low as 5% can be reliably detected by including frequency range covering 5–10th natural frequencies. The efficacy of the scheme is also experimentally validated for the same beam. It is also found that a simple noise filtration scheme such as a Gaussian moving average filter can adequately remove false peaks from the damage index profile.

DOA estimation in the autocorrelation domain for coprime array

In this paper a new algorithm for direction of arrival (DOA) estimation in coprime arrays based on the autocorrelation domain (AD) method is proposed. The spatial autocorrelation sequence of the virtual uniform linear array (ULA) associated with the coprime array is built by averaging the diagonals of the spatially smoothed covariance matrix. The AD method estimates the DOAs by solving a generalized eigenvalue problem and refining the obtained solution. The main advantage of the introduced method is a spatial spectrum without false peaks. Simulation results indicate that the proposed approach is better than existing methods, especially for small number of snapshots.

Improving CLIP-seq data analysis by incorporating transcript information

BackgroundCurrent peak callers for identifying RNA-binding protein (RBP) binding sites from CLIP-seq data take into account genomic read profiles, but they ignore the underlying transcript information, that is information regarding splicing events. So far, there are no studies available that closer observe this issue.ResultsHere we show that current peak callers are susceptible to false peak calling near exon borders. We quantify its extent in publicly available datasets, which turns out to be substantial. By providing a tool called CLIPcontext for automatic transcript and genomic context sequence extraction, we further demonstrate that context choice affects the performances of RBP binding site prediction tools. Moreover, we show that known motifs of exon-binding RBPs are often enriched in transcript context sites, which should enable the recovery of more authentic binding sites. Finally, we discuss possible strategies on how to integrate transcript information into future workflows.ConclusionsOur results demonstrate the importance of incorporating transcript information in CLIP-seq data analysis. Taking advantage of the underlying transcript information should therefore become an integral part of future peak calling and downstream analysis tools.

MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses. To address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks. Specifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task of distinguishing reliably integrated peaks from poorly integrated ones. These classifiers were compared to using a residual standard deviation (RSD) cut-off in pooled quality-control (QC) samples, which aims to remove peaks with analytical error. The best performing classifier was found to be a combination of the AdaBoost algorithm and a set of 11 peak quality metrics previously explored in untargeted metabolomics and proteomics studies. As a complementary approach, applying our framework to peaks retained after filtering by 30% RSD across pooled QC samples was able to further distinguish poorly integrated peaks that were not removed from filtering alone. An R implementation of these classifiers and the overall computational approach is available as the MetaClean package at https://CRAN.R-project.org/package=MetaClean . Our work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC-MS metabolomics data.

REAL-TIME METHOD FOR ECG R-PEAK DETECTION COMBINING AUTOMATIC THRESHOLD AND DIFFERENTIATION

This paper proposed a novel real-time algorithm for electrocardiogram (ECG) signal analysis using the first derivative and automatic threshold to locate the R-peaks. First, the ECG signals are filtered by Butterworth low pass filter to reduce the high frequency noise. Then, the first 10[Formula: see text]s datasets of the first derivative of ECG signal are analyzed to search the maximum value. Three process thresholds are computed using this maximum value, which is used to avoid the missed and false peak detections. Thus, a threshold is automatically calculated using these searched maximum values, and divide the differentiated ECG to obtain two intersection points. Recording the time of these two intersection points, a time interval is formed for the differentiated ECG. Delaying this time interval for specific sampling periods, a new time interval is acquired for the corresponding ECG cycle. Finally, the local maximum in an ECG cycle is narrowed down in this new time interval such that the R-peak can be located. The MIT-BIH Arrhythmia Database of 48 ECG recordings is used to verify the proposed algorithm. On this dataset, the proposed algorithm yielded mean sensitivity of 99.17%, positive predictivity of 97.37% and average time error of 1.01[Formula: see text]ms.

Operational Transfer Path Analysis Based on Signal Decoupling

Abstract This study proposes an input decoupling analysis method based on the pulse test and the traditional operational transfer path analysis (OTPA) method for the OTPA transmission path analysis when the input is strongly coupled, and the algorithm is verified for analysis optimization of results. An experimental model of the dual excitation source is established in the laboratory. Experimental comparison studies show that the OTPA method, which is based on signal decoupling, inhibits the influence of cross-coupling among the source signals during OTPA analysis and effectively improves the accuracy of the transmission path test and analysis. The OTPA method is applied to analyze the transfer path of the vibration and noise of the carriages of the electric multiple unit (EMU) train (a high-speed train in China). The false peak frequency in the traditional OTPA method is eliminated, and the accuracy of the analysis is improved.

Application of an Improved Hough Transform and Image Correction Algorithm in ACC

Numerous studies have shown that ACC (Augmentative and Alternative Communication), as a tool in language and communication training for children with autism, has effectively improved and played a positive role. ACC’s Picture Exchange System (PECS) provides an important support for the correction and feature recognition of language and training teaching cards. When designing the picture exchange function of ACC, this paper proposes a modified Hough transform and image correction algorithm suitable for teaching cards to solve the problems of large data calculation, time-consuming calculation, large space occupation and false peaks caused by traditional Hough transform algorithm in detecting straight lines, which also optimizes the interactive experience of the teaching scene. Finally, we design two experiments to compare and analyse the classic Hough transform, random Hough transform and improved Hough transform. Experimental results show that the improved Hough transform algorithm has superior performance in terms of execution efficiency and accuracy of line detection.

MetaClean: A machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data

Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high–resolution mass spectrometry (LC–MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses. To address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks. Specifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task of distinguishing reliably integrated peaks from poorly integrated ones. These classifiers were compared to using a residual standard deviation (RSD) cut-off in pooled quality-control (QC) samples, which aims to remove peaks with analytical error. The best performing classifier was found to be a combination of the AdaBoost algorithm and a set of 11 peak quality metrics previously explored in untargeted metabolomics and proteomics studies. As a complementary approach, applying our framework to peaks retained after filtering by 30% RSD across pooled QC samples was able to further distinguish poorly integrated peaks that were not removed from filtering alone. An R implementation of these classifiers and the overall computational approach is available as the MetaClean package at https://CRAN.R-project.org/package=MetaClean . Our work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC–MS metabolomics data.

Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing.

Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.

Simulating, Predicting, and Minimizing False Peaks for Hadamard Transform Ion Mobility Spectrometry.

Multiplexing techniques, including the Hadamard transform, are widely used in the recovery of weak signals from high-level noise. Hadamard transform ion mobility spectrometry (HT-IMS), however, can suffer serious drawbacks due to false peaks. False peaks in HT-IMS are generally attributed to nonperfect gating behavior. This paper confirmed that the origin of false peaks in HT-IMS is not generally due to ion gating but rather to peak shifts by Coulombic repulsion of the ion packets inside the drift tube. The amplitudes of these false peaks are determined by the number of ions inside the ion packets. This phenomenon is simulated and confirmed by the convolution of the spectrum with a shifted s-sequence to reproduce the artifact peaks with the exact position, amplitude, and profile. Two approaches, including preoffset sequence modulation and post-data processing, were evaluated to mitigate the false peaks in HT-IMS, and both methods can work effectively.

A Novel Method for Analyzing Pore Size Distribution of Complex Geometry Shaped Porous Shale

This article proposes the differential BJH equation based on the principles of multilayer adsorption and capillary condensation, which was simplified by theoretical investigation and experiments. This work indicates that the differential function of isotherm and the differential function of pore size to relative pressure determine the pore size distribution of porous media. The differential BJH model can be used to explain the source of the false peak in pore size distribution and to calculate the pore size distribution of different shapes of pores in a porous media with a porous structure. It has an excellent application prospect in the characterization of complex pore structure represented by shale.

Subspace methods for self-calibration of ULAs with unknown mutual coupling: A false-peak analysis

Self-calibration can mitigate the adverse effect of array imperfections in direction-of-arrival (DoA) estimation. In this paper, we consider self-calibration of uniform linear arrays (ULAs) with unknown mutual coupling and focus on subspace-based methods. By modeling the effect of mutual coupling through complex symmetric Toeplitz matrices, we first analyze its influence to existing subspace methods. Our analytical results illustrate that mutual coupling can induce false peaks in spatial spectra of these methods, and more importantly, most of these peaks are predictable. Based on the analytical results, we then propose new spectra to suppress these predictable false peaks, and new self-calibration algorithms for the proposed spectra. Simulations are performed to validate our analytical results and to illustrate the advantages of the proposed algorithms.

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy.

Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set generated from top-down MS experiments requires multiple sequential data processing steps to successfully interpret the data for identifying and characterizing proteoforms. One critical step is the deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes. Multiple algorithms are currently available to deconvolute top-down mass spectra, resulting in different deconvoluted peak lists with varied accuracy compared to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. For the random forest algorithm, which had better predictive performance, the consensus peak lists on average could achieve a recall value (true positive rate) of 0.60 and a precision value (positive predictive value) of 0.78. It outperforms the single best algorithm, which achieved a recall value of only 0.47 and a precision value of 0.58. This machine learning strategy enhanced the accuracy and confidence in protein identification during database searches by accelerating the detection of true positive peaks while filtering out false positive peaks. Thus, this method shows promise in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.

Prevention of false peak detection of Brillouin gain spectrum by using peak tracking and trend analysis

AbstractDistributed optical fiber sensor based on a peak‐frequency change of a spectrum of Brillouin scattering light has been extensively studied since it can measure strain and/or temperature in various applications. The distributed optical fiber sensor observes the spectrum of backscattered light from the specified section according to a spatial resolution and determines strain and/or temperature based on the peak‐frequency change of the spectrum. However, the observed spectrum may have two maximum peaks that correspond to strain and/or temperature included in the specified section of the optical fiber. Overlapped noise by the interrogator system to two peaks on the Brillouin gain spectrum causes false detection of the maximum peak‐frequency. Although this false detection may occur, no workarounds has been discussed yet. This article proposes a workaround by method of “peak tracking and trend analysis” that considers the change of peak intensity to determine peak frequency of the spectrum and confirms its effect of preventing false peak detection of Brillouin gain spectrum.

Doppler Ultrasound for Heart Rate Assessment in a Porcine Model of Neonatal Asphyxia.

Objectives: Approximately 10% of newborn infants require resuscitation at birth. Accurate heart rate (HR) assessment guides resuscitation interventions, thereby reducing morbidities and mortality. While existing HR assessment methods have several limitations, the Doppler ultrasound (Doppler-US) might be a promising alternative. We aimed to evaluate accuracy and optimal use of Doppler-US for HR assessments during neonatal asphyxia in a pre-clinical model.Design: HR assessments were performed in 16 term newborn piglets that were anesthetized, intubated, and instrumented. Study I evaluated optimal transducer position, Study II compared aortic (AV) and pulmonary (PV) examination modes, and Study III examined accuracy during asphyxia, for HR assessment.Setting: Experimental setting.Subjects: Asphyxia-induced piglets.Interventions: Study I: Doppler-US (USCOM® 1A) HR was assessed on upper (A), middle (B), and lower (C) third of the sternum; study II: Doppler-US HR was assessed using AV and PV examination modes; study III: HR was assessed during asphyxia. Comparisons were made between Doppler-US and the clinical gold standard for HR assessments, electrocardiography (ECG).Measurements and Main Results: Study I: Mean (SD) Doppler-US HR at position A, B, and C showed no difference when compared to ECG HR. Study II: The mean (SD) Doppler-US HR using AV and PV modes also showed no difference when compared to ECG HR. Study III: Bland-Altman analysis revealed a mean difference (95% limits of agreement) between Doppler-US and ECG HR of 1.5 (−16 to 19) bpm. Additionally, motion artifacts produced false peaks and peak size was seen to decrease as bradycardia progressed.Conclusions: HR assessment using Doppler-US during asphyxia is accurate but has limitations and must be further evaluated prior to clinical use. Doppler-US can be positioned along the sternum and use either AV or PV mode for accurate assessments in a piglet model of neonatal asphyxia.

Vibration-based damage diagnosis of composite sandwich panels with bi-directional corrugated lattice cores

In this paper we design and fabricate a series of carbon fibre composite bi-directional corrugated sandwich panels (BCSPs) with and without defects. A modified damage index (MDI) based on modal flexibility curvature variation (MFCV) is proposed to identify the damage location of composite BCSPs numerically. A good agreement of natural frequencies and modal shapes is obtained between the numerical and the experimental results, which demonstrates that the adopted numerical models are reliable for the present structures. Results show that the proposed MDI method can accurately detect almost all of damages and effectively eliminate the influence of false peaks as much as possible, which demonstrate the present MDI method is better than the DI method for the damage location of the present structures, which could be useful for the non-destructive evaluation (NDE) of such kinds of composite sandwich structures.

Maximum Entropy Method without False Peaks with Exact Numerical Equation

The standard numerical maximum entropy method (MEM) still uses the Yule-Walker equation which contains rough approximation by Walker. The commonly used numerical equation contains additional modifications to reduce calculation cost. Nowadays, we have powerful computers and there is no reason to use the modified equation. We show that the drawbacks of MEM such as false peaks and peak splittings are from the modifications. They do not appear when using the exact numerical equation, even a given time series is fractional.

Directional Surface Wave Spectra from Point Measurements of Height and Slope

AbstractWe describe here a method for recovering directional ocean surface wave spectra obtained from height and slope measurements made over a small area, the iterative deconvolution method (IDM). We show that IDM is a more reliable method for estimating directional wave spectra than more common spectral estimation techniques by comparing it with the widely used maximum entropy method (MEM). IDM is based on the observation that pitch–roll buoys produce directional spectra that are the true spectra convolved with an angular windowing function and are therefore much broader than the true spectra. We test IDM against simulated data and find that it does a better job of retrieving the known input spectra than does MEM, which often produces false double peaks or incorrect angular widths. We compare IDM recoveries to spectra obtained using a nonstandard processing technique, the wavelet directional method (WDM) on data from a compact array of wave staffs on Lake Ontario. We find that IDM produces directional wave spectra very nearly identical to those obtained using WDM, verifying both techniques. Finally, we processed standard NDBC buoy directional spectra and showed that IDM recovers ocean wave spectra that narrow in the Strait of Juan de Fuca and that follow a changing wind in the expected manner. Neither of these phenomena are reliably obtained using MEM due to its tendency to produce false bimodal peaks and peaks that are too narrow.