Peak Detection Method Research Articles

Investigation on Pulse Wave Forward Peak Detection and Its Applications in Cardiovascular Health.

The contours of the pulse wave vary greatly, which affect the accuracy of pulse wave peak detection and the reliability of subsequent peak-based cardiovascular health analyses. We proposed an algorithm to reliably detect the peak of forward pulse wave (forward peak) and proposed to use it for improving the accuracy in cardiovascular health analysis. A method based on Gaussian fitting was proposed to detect the forward peak. Then, the forward peak was utilized for instantaneous heart rate (HR), heart rate variability (HRV), and augmentation index (a cardiovascular risk marker reflecting arterial stiffness) estimations. The accuracy of HR/HRV obtained by forward peak was compared with that obtained by other photoplethymogram (PPG) characteristic points previously reported, using electrocardiogram-derived HR/HRV as gold standard. The correlation between augmentation index and age was calculated. The performance was verified using PPG-based pulse wave data with different contours while they were recorded at different locations from subjects with a wide range of age. The proposed forward peak detection method had smaller estimation error when compared with the gold standard, than other PPG characteristic points in estimating HR/HRV. The augmentation index extracted from the proposed forward peak was significantly correlated with age (p < 0.01). The proposed algorithm can relatively reliably detect the forward peak and has a wide application prospect in cardiovascular health. Due to the convenience of PPG measurements, this proposed forward peak detection method has the potential to be widely used in the fields of wearable devices and telemedicine.

A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia.

Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.

Efficient detection of multiple FBG wavelength peaks using matched filtering technique

We propose an efficient method for FBG peak detection based on matched filtering technique. The matched filtering process is based on resonance point estimation between a standard reference spectral signal and a reflected spectrum of FBG. The desired peak wavelength and corresponding peak intensity are predicted by determining the cross-correlation between the FBG signal and derivative of the reference signal. The peak wavelength and intensity are found from the zero-crossing points of the cross-correlation function. The Mexican-hat wavelet function is chosen as the reference spectral signal due to its narrow shape. The proposed method has been experimentally verified for isolated as well as overlapped FBG spectrum. The proposed algorithm can suitably be used for multiple peak detection when several FBGs are cascaded and if the FBG signal is weak and noisy.

An Improved Algorithm for Peak Detection Based on Weighted Continuous Wavelet Transform

Peak detection is a crucial preprocessing step in the analysis of various spectral signals. The method based on the continuous wavelet transform is more practical and popular, and has better detection accuracy and reliability by identifying peaks across scales in the wavelet space and implicitly removing noise as well as the baseline. However, there are inevitably overlapping peaks in the measured spectra, and the formed composite ridges affect peak detection accuracy. Most peak detection methods have limited applicability to overlapping peaks. A weighted continuous wavelet transform (WCWT) peak detection algorithm is proposed to improve the adaptive ability of the peak detection method. This method yields more obvious spectral peak characteristics in low-scale regions. Composite ridges can be successfully truncated by setting a noise threshold based on the standard deviation of the spectral signal. In addition, the maximum value in the ridges was compressed and shifted to a smaller scale, which could determine the peaks more accurately. The method was applied to the peak detection of simulated spectra, Romanian database of Raman spectra, and real liquid electrode glow discharge spectra. The results show that the proposed method exhibits good peak detection performance.

Detection and Optimization of Traffic Networks Based on Voronoi Diagram

Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.

A comparative study on the peak detection methods used to interpret potential field data: a case study from Vietnam

Locating peaks of transformed anomalies provides an effective perspective to detect lateral changes in potential field data. Recently, a number of peak detection methods based on the curvature and parabola-based approaches have been introduced to interpret potential field data. As a result of differences in their calculations, the various methods produce slightly different results for any given source geometry, depth and contrast or magnetization. The purpose of this study is to compare the performance of these methods in locating peaks of transformed anomalies. To obtain optimum results, these methods have been tested on synthetic datasets and also real gravity data from the Red River Trough, Vietnam. The results show that the parabola-based method and its modified version are less effective for detecting peaks in the intersection regions of sources. In addition, although the curvature-based method does not have such a problem, it can bring the secondary maximum locations.

Automating LC–MS/MS mass chromatogram quantification: Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods.

Background and ObjectiveWhile there are many different methods for peak detection, no automatic methods for marking peak boundaries to calculate area under the curve (AUC) and signal-to-noise ratio (SNR) estimation exist. An algorithm for the automation of liquid chromatography tandem mass spectrometry (LC–MS/MS) mass chromatogram quantification was developed and validated. MethodsContinuous wavelet transformation and other digital signal processing methods were used in a multi-step procedure to calculate concentrations of 6 different analytes. To evaluate the performance of the algorithm, the results of the manual quantification of 446 hair samples with 6 different steroid hormones by two experts were compared to the algorithm results. ResultsThe proposed approach of automating LC–MS/MS mass chromatogram quantification is reliable and valid. The algorithm returns less non-detectables than human raters. Based on signal to noise ratio, human non-detectables could be correctly classified with a diagnostic performance of AUC = 0.95. ConclusionsThe algorithm presented here allows fast, automated, reliable, and valid computational peak detection and quantification in LC–MS/MS. We provide an open source reference implementation of the proposed algorithm on the Open Science Framewok (https://osf.io/rfqkx).

Inverse Histogram-Based Clustering Approach to Seafloor Segmentation from Bathymetric Lidar Data

A current hindrance to the scientific use of available bathymetric lidar point clouds is the frequent lack of accurate and thorough segmentation of seafloor points. Furthermore, scientific end-users typically lack access to waveforms, trajectories, and other upstream data, and also do not have the time or expertise to perform extensive manual point cloud editing. To address these needs, this study seeks to develop and test a novel clustering approach to seafloor segmentation that solely uses georeferenced point clouds. The proposed approach does not make any assumptions regarding the statistical distribution of points in the input point cloud. Instead, the approach organizes the point cloud into an inverse histogram and finds a gap that best separates the seafloor using the proposed peak-detection method. The proposed approach is evaluated with datasets acquired in Florida with a Riegl VQ-880-G bathymetric LiDAR system. The parameters are optimized through a sensitivity analysis with a point-wise comparison between the extracted seafloor and ground truth. With optimized parameters, the proposed approach achieved F1-scores of 98.14–98.77%, which outperforms three popular existing methods. Further, we compared seafloor points with Reson 8125 MBES hydrographic survey data. The results indicate that seafloor points were detected successfully with vertical errors of −0.190 ± 0.132 m and −0.185 ± 0.119 m (μ ± σ) for two test datasets.

Differential ATAC-seq and ChIP-seq peak detection using ROTS.

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data.

A feature engineering framework for online fault diagnosis of freight train air brakes

Automatic air brake systems are widely used in freight train braking to ensure railway operation safety. Various types faults pose an enormous threat to freight operations. Existing algorithms lack a unified framework for generating key features. In this research, we propose a novel feature engineering framework for the fault diagnosis of freight train air brakes. First, experimental data are collected through a three-car in-lab experimental platform. Second, a peak detection method combined with first-order difference function to partition and classify the air pressure time series into the braking phase and releasing phase. Third, a divided-and-integrated framework is designed for feature engineering. Feature selection is carried out via a modified reinforcement learning method. Finally, multiple machine learning algorithms are explored and the results indicate that random forest method shows the best performance. The proposed model achieves about 99% accuracy for car-level fault detection and over 94% accuracy for component-level fault diagnosis.

Spectrum peak detection algorithm based on trend accumulation without base deduction

谱峰的检测分析在色谱技术研究中具有十分重要的作用,但在色谱数据采集、传输的过程中,不同程度的噪声干扰给谱峰检测带来了极大的困难。目前传统的谱峰检测算法普遍通过基底扣除的方式对谱峰的形态进行预定义,将谱峰分为单峰、重叠峰等多个种类。针对不同种类的谱峰采用不同的检测方法,这就导致了传统的谱峰检测算法具有高复杂度、低自动化程度以及容易失真等缺点。因此,该文从另一个角度出发提出了一种新型的谱峰检测算法。该算法取消基底扣除以及谱峰分类这一步骤,直接在源数据曲线的基础上进行谱峰检测,主要分为离散差分、趋势累积以及遍历寻峰3个步骤。首先通过信号量表征数据升降趋势;然后进行数据趋势累积,根据累积总和定位谱峰,采用三点定位的方式,即峰起点、极值点和峰终点描述一个谱峰的位置;最后根据遍历排序的方式进行谱峰的筛选。此外,通过谱峰扣除的方式得到曲线基底部分。采用C语言设计编写了算法程序,并对多个动态比表面积分析仪测定的色谱图进行了检测分析,结果显示使用该算法可以精准区分谱峰部分与基底部分,受数据曲线毛刺、震荡等噪声干扰很小,谱峰的三点定位十分准确,且不受其复杂形态的影响,具有很强的普适性。与其他算法相比,该算法定位准确,结构清晰,具有较好的稳定性以及可靠性。该文报道了无基底扣除以及趋势累积等新型谱峰检测思想在吸脱附色谱曲线中的应用,证明了其在吸脱附色谱峰检测中的有效性和良好的应用前景。

A 12-bit 30 MS/s SAR ADC in 180 nm CMOS for PMT signal readout

Photomultiplier Tubes (PMTs) are widely used as the photon detector in high energy physics experiments for their fast response and high sensitivity. High precision charge measurement is usually required for PMT readout, and especially in cosmic ray experiments a large dynamic range is also demanded. This paper describes an Analog-to-Digital Converter (ADC) Application Specific Integrated Circuit (ASIC), which aims to work with a front-end analog ASIC to perform charge measurement based on the digital peak detection method. This ADC design is based on the power-efficient Successive Approximation Register (SAR) architecture, to achieve a 12-bit resolution with a sampling rate of up to 30 MS/s. Test results indicate that the Effective Number of Bits (ENOB) of this ADC is better than 9.2 bits with a sampling rate of 30 MS/s.

Improved position estimation method for locating an encrypted COSPAS‐SARSAT beacon

This study addresses a signal processing technique in a search and rescue system. An international disaster rescue communications support programme such as COSPAS- search and rescue satellite-aided tracking (SARSAT) estimates the position of a beacon based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) techniques. To backtrack signals from encrypted beacons without knowing the encryption codes, the cross ambiguity function (CAF) is proposed to estimate the TDOA and FDOA, in which high sampling rates are needed to increase the positioning accuracy of existing CAF algorithms, which entail high calculation loads. To overcome this, an interpolation method is proposed for peaks of Fourier transform capable of maintaining high accuracy at low calculation loads after a well-performed initial acquisition. The numerical simulation shows an improved estimation accuracy of the proposed algorithm compared with existing peak detection methods.

On the development of an efficient, low-complexity and highly reproducible method for systolic peak detection

Background and ObjectivesNumerous methods for systolic peak detection on PPG signals have been reported in the literature. However, such approaches can hardly be replicated and implemented in wearable applications because of their complicated methodologies and high computational requirements. In an attempt to address this issue, an efficient, low-complexity, and highly reproducible method for real-time systolic peak detection is proposed. MethodsThe method calculates the difference between the value of the current PPG sample and the maximum computed over a certain window that includes the current value. If such difference is negative, then the previous PPG value is labeled as a peak provided that the number of times that the difference is equal to zero reaches or exceeds the window size. To overcome some of the disturbances usually accompanying PPG signals, some well-documented, time-domain strategies were included. The performance of the method was assessed off- and online by using free-available and locally-acquired data sets. ResultsThe proposed method can perform faster than several other peak detection methods previously reported in the literature, including some that could not be implemented due to their detection parameters are not available. However, the method performance may be affected by motion artifact corruption, especially when the sampling rate increases. ConclusionGiven its low computational requirements, novel techniques for artifact detection and removal could be added to our method to improve its robustness. More accurate comparisons between results yielded by this and several other studies could be performed as long as detection parameters were properly reported.

Accurate Peak Detection for Optical Sensing with Reduced Sampling Rate and Calculation Complexity.

Fiber Bragg gratings (FBGs) are widely applied in optical sensing systems due to their advantages including being simple to use, high sensitivity, and having great potential for integration into optical communication systems. A common method used for FBG sensing systems is wavelength interrogation. The performance of interrogation based sensing systems is significantly determined by the accuracy of the wavelength peak detection processing. Direct maximum value readout (DMVR) is the simplest peak detection method. However, the detection accuracy of DMVR is sensitive to noise and the sampling resolution. Many modified peak detection methods, such as filtering and curve fitting schemes, have been studied in recent decades. Though these methods are less sensitive to noise and have better sensing accuracy at lower sampling resolutions, they also confer increased processing complexity. As massive sensors may be deployed for applications such as the Internet of things (IoT) and artificial intelligence (AI), lower levels of processing complexity are required. In this paper, an efficient scheme applying a three-point peak detection estimator is proposed and studied, which shows a performance that is close to the curve fitting methods along with reduced complexity. A proof-of-concept experiment for temperature sensing is performed. 34% accuracy improvement compared to the DMVR is demonstrated.

Wall-Shear-Stress-Based Conditional Sampling Analysis of Coherent Structures in a Turbulent Boundary Layer

Abstract Coherent structures are critical for controlling turbulent boundary layers due to their roles in momentum and heat transfer in the flow. Turbulent coherent structures can be detected by measuring wall shear stresses that are footprints of coherent structures. In this study, wall shear stress fluctuations were measured simultaneously in a zero pressure gradient turbulent boundary layer using two house-made wall shear stress probes aligned in the spanwise direction. The wall shear stress probe consisted of two hot-wires on the wall aligned in a V-shaped configuration for measuring streamwise and spanwise shear stresses, and their performance was validated in comparison with a direct numerical simulation result. Relationships between measured wall shear stress fluctuations and streamwise velocity fluctuations were analyzed using conditional sampling techniques. The peak detection method and the variable-interval time-averaging (VITA) method showed that quasi-streamwise vortices were inclined toward the streamwise direction. When events were simultaneously detected by the two probes, stronger fluctuations in streamwise velocity were detected, which suggests that stronger coherent structures were detected. In contrast to the former two methods, the hibernating event detection method detects events with lower wall shear stress fluctuations. The ensemble-averaged mean velocity profile of hibernating events was shifted upward compared to the law of the wall, which suggests low drag status of the coherent structures related with hibernating events. These methods suggest significant correlations between wall shear stress fluctuations and coherent structures, which could motivate flow control strategies to fully exploit these correlations.

A Novel Method for Laser Peak Detection with Subpixel Accuracy for the Rail Corrugation Measurement

In this article, a new wavelet‐based laser peak detection algorithm is proposed having subpixel accuracy. The algorithm provides an accurate and rapid measurement platform for the rail surface corrugation with no need to any image noise elimination. The proposed rail Corrugation Measurement System (CMS) is based on the laser triangulation principle, and the accuracy of such system is mainly affected by the laser peak detection in the captured image. The intensity of each row or column of the image is taken as a 1‐D discrete signal. Intensity distribution of a laser stripe in this signal follows a Gaussian pattern contaminated by the white noise. Against usual peak detection algorithms with need to prenoise‐filtering process, the proposed method based on the wavelet transform is able to perform these tasks efficiently and robustly. Present wavelet‐based methods for the peak detection are at pixel level, but for achieving high accuracy subpixel detection is proposed. Experiments show that the capability of the proposed method for laser peak detection is more accurate and faster than the filter‐based methods, especially for low S/N ratios. Also, this technique can be utilized for any application in laser peak detection with subpixel accuracy. A prototype system based on the proposed method for the rail corrugation measurement has been designed and manufactured. Results of the rail corrugation measurement guarantee capability of the proposed methodology for accurate measurement of the rail corrugation and its potential for industrial application.

Instantaneous frequency enhanced peak detection for sugarcane seed cutting

Peak detection methods are widely used in various areas. This study introduces a peak detection method used for seed cutting. The envelope of signal in discrete time domain was calculated by Hilbert transform. In order to increase the reliability in noise condition, the envelope was enhanced by utilising instantaneous frequency of signal, and then a multi-rule joint search algorithm marks the peaks, which stands for the location of a node. The proposed method worked in worse SNR condition in the simulation, and was verified in the experiment of detecting sugarcane nodes. Local maximums of accelerometer data are marked, showing the position of node rings on the sugarcane surface. The position data will be used for an action signal in cutting machines, or be used for the analysis of crop growth.

Radar-Based High-Accuracy Cardiac Activity Sensing

Noncontact vital-sign telemonitoring by radar systems is applied more widely. When considering the implementation of Doppler radar for more precise extraction of cardiac physiological parameters to adapt to clinical diagnosis of heart diseases, the most open heart rate (HR) detection methods based on the periodic information are far from enough. Here, this article presents a new simple, effective, and high-accuracy settlement to maintain cardiac activity sensing comprehensively and systematically for the first time, for the estimation of beat-to-beat intervals (BBIs) and cardiac timings. We propose the decoding peak detection (DPD) method to address heartbeat peaks’ extraction problem by decoding the most likely sequence of states from the single-band frequency envelogram (FEnv) of the radar signal, thus realizing HR variability analysis. Furthermore, a hidden semi-Markov model (HSMM) is utilized to detect cardiac timings in bandpass filtered radar data, which is equivalent to positions of the R-peak and end-T-wave carried in electrocardiogram (ECG). Experimental results show that the proposed method achieves an average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score of 93.19% ± 0.73% with the ECG as gold-standard, evaluated on a data set from six subjects. The accuracy of radar-based extracted BBIs is assessed by the mean relative error, which ranges from 0.51% to 1.06% by HSMM and 0.37% to 1.15% by DPD, respectively. Finally, a series of experiments are conducted to confirm the high agreement between radar signals and cardiac activities. Meanwhile, from the cardiac timings analysis by the proposed methods, we can find the advantage of radar-based detection at some specific timings compared with ECG-based, e.g., end-T-wave detection.

Asymmetric Stencil Approach for Latency Reduction of Real-Time Peak Detection Using AMPD Algorithm and FPGA Technology

In many signal processing applications, the detection of peaks is a substantial stage. However, the high false-positive peak identification rate is a crucial problem because of the complexity of the signals and multiple noise sources. For this reason, a modified Automatic Multiscale Peak Detection (AMPD) algorithm of any time serial data based on Field-Programmable Gate Array (FPGA) has been implemented by these authors. In addition, a kind of approximation with an asymmetric stencil is proposed to reduce the pipeline latency. In this paper, it is focused on evaluating the trade-off relationship between latency reduction effects and accuracy of peak point detection on a real-time peak detection method developed in the previous study using the AMPD algorithm and FPGA technology.