Peak Detection Performance Research Articles

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.

Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks.

Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors. Recently, this issue has been addressed by deep 1-D convolutional neural networks (CNNs) that have achieved state-of-the-art performance levels in Holter monitors; however, they pose a high complexity level that requires special parallelized hardware setup for real-time processing. On the other hand, their performance deteriorates when a compact network configuration is used instead. This is an expected outcome as recent studies have demonstrated that the learning performance of CNNs is limited due to their strictly homogenous configuration with the sole linear neuron model. This has been addressed by operational neural networks (ONNs) with their heterogenous network configuration encapsulating neurons with various nonlinear operators. In this study, to further boost the peak detection performance along with an elegant computational efficiency, we propose 1-D Self-Organized ONNs (Self-ONNs) with generative neurons. The most crucial advantage of 1-D Self-ONNs over the ONNs is their self-organization capability that voids the need to search for the best operator set per neuron since each generative neuron has the ability to create the optimal operator during training. The experimental results over the China Physiological Signal Challenge-2020 (CPSC) dataset with more than one million ECG beats show that the proposed 1-D Self-ONNs can significantly surpass the state-of-the-art deep CNN with less computational complexity. Results demonstrate that the proposed solution achieves a 99.10% F1-score, 99.79% sensitivity, and 98.42% positive predictivity in the CPSC dataset, which is the best R-peak detection performance ever achieved.

Convolutional neural network-based respiration analysis of electrical activities of the diaphragm

The electrical activity of the diaphragm (Edi) is considered a new respiratory vital sign for monitoring breathing patterns and efforts during ventilator care. However, the Edi signal contains irregular noise from complex causes, which makes reliable breathing analysis difficult. Deep learning was implemented to accurately detect the Edi signal peaks and analyze actual neural breathing in premature infants. Edi signals were collected from 17 premature infants born before gestational age less than 32 weeks, who received ventilatory support with a non-invasive neurally adjusted ventilatory assist. First, a local maximal detection method that over-detects candidate Edi peaks was used. Subsequently, a convolutional neural network-based deep learning was implemented to classify candidates into final Edi peaks. Our approach showed superior performance in all aspects of respiratory Edi peak detection and neural breathing analysis compared with the currently used recording technique in the ventilator. The method obtained a f1-score of 0.956 for the Edi peak detection performance and {R}^{2} value of 0.823 for respiratory rates based on the number of Edi peaks. The proposed technique can achieve a more reliable analysis of Edi signals, including evaluation of the respiration rate in premature infants.

Quantitative Method for Liquid Chromatography–Mass Spectrometry Based on Multi-Sliding Window and Noise Estimation

LC-MS/MS uses information on the mass peaks and peak areas of samples to conduct quantitative analysis. However, in the detection of clinical samples, the spectrograms of the compounds are interfered with for different reasons, which makes the identification of chromatographic peaks more difficult. Therefore, to improve the chromatographic interference problem, this paper first proposes a multi-window-based signal-to-noise ratio estimation algorithm, which contains the steps of raw data denoising, peak identification, peak area calculation and curve fitting to obtain accurate quantitative analysis results of the samples. Through the chromatographic peak identification of an extracted ion chromatogram of VD2 in an 80 ng/mL standard and the spectral peak identification of data from an open-source database, the identification results show that the algorithm has a better peak detection performance. The accuracy of the quantitative analysis was verified using the LC-HTQ-2020 triple quadrupole mass spectrometer produced by our group for the application of steroid detection in human serum. The results show that the algorithm proposed in this paper can accurately identify the peak information of LC-MS/MS chromatographic peaks, which can effectively improve the accuracy and reproducibility of steroid detection results and meet the requirements of clinical testing applications such as human steroid hormone detection.

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 of the Complete ECG Waveform with Woven Textile Electrodes.

Wearable physiological monitoring systems are becoming increasingly prevalent in the push toward autonomous health monitoring and offer new modalities for playful and purposeful interaction within human computer interaction (HCI). Sensing systems that can be integrated into garments and, therefore, daily activities offer promising pathways toward ubiquitous integration. The electrocardiogram (ECG) signal is commonly monitored in healthcare and is increasingly utilized as a method of determining emotional and psychological state; however, the complete ECG waveform with the P, Q, R, S, and T peaks is not commonly used, due to the challenges associated with collecting the full waveform with wearable systems. We present woven textile electrodes as an option for garment-integrated ECG monitoring systems that are capable of capturing the complete ECG waveform. In this work, we present the changes in the peak detection performance caused by different sizes, patterns, and thread types with data from 10 human participants. These testing results provide empirically-derived guidelines for future woven textile electrodes, present a path forward for assessing design decisions, and highlight the importance of testing novel wearable sensor systems with more than a single individual.

Diffusion enhancement model and its application in peak detection

It is a challenge for peak detection algorithms to detect some low-amplitude peaks and overlapped peaks contaminated by noise. Among of existing peak detection algorithms, the continuous wavelet transform (CWT)-based algorithm is the best. When the Mexican Hat wavelet is selected as the mother wavelet, the CWT of a signal is essentially equivalent to using the Gaussian function to smooth the 2nd derivative of the signal. Therefore, a natural idea is to combine the peak enhancement step and peak-preserving diffusion into peak detection process to improve the performance of peak detection. In the proposed algorithm, the Gaussian smoothing in the CWT-based algorithm is replaced with the peak-preserving diffusion filtering.As an assessment of the proposed algorithm, a simulated spectrum with low-amplitude peaks and overlapped peaks was generated and used to test the enhancement performance. Then 100 groups of simulated proteomics data sets in [1] were used to assess the proposed algorithm. In these data sets, the true peaks are known in each spectrum. Thus, the false discovery rate (FDR) is easy to find. Five typical peak detection programs were chosen to compare the proposed algorithm. The FDR and sensitivity is employed to compare the performance of these algorithms. Result shows that the proposed algorithm can improve the performance of peak detection.

Normal–Gamma–Bernoulli peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data

Compared to other analytical platforms, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC–MS. Therefore, the normal–exponential–Bernoulli (NEB) model is generalized by gamma distribution and a new peak detection algorithm using the Normal–Gamma–Bernoulli (NGB) model is developed. Unlike the NEB model, the NGB model has no closed-form analytical solution, hampering its practical use in peak detection. To circumvent this difficulty, three numerical approaches, which are fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2), are introduced. The applications to simulated data and two real GC×GC–MS data sets show that the NGB-D1 method performs the best in terms of both computational expense and peak detection performance.

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

오실로메트릭 측정법을 사용한 심박주기 검출 성능 개선

본 연구에서는 혈압측정으로부터 얻어지는 오실레이션(Oscillation) 신호를 이용하여 심박주기를 검출하고, 심박주기의 정확성을 개선하는 알고리듬을 개발하였다. 이를 위한 혈압측정의 방법으로 오실로메트릭(Oscillometric) 측정법을 사용하였으며, 오실레이션 신호의 피크들로부터 전 후 기울기들의 평균이 교차하는 지점을 실제 피크로 인정하고 심박주기를 계산하였다. 제안된 방법은 그래프 상에서 심박주기의 극심한 편차와 오류를 줄이는 성능을 나타냈다. This study presents a method for heart rate detection using oscillation wave signal and tries to enhance the performance of peak detection. For this objective, the method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful technology for separating complex samples. The typical goal of GC×GC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GC×GC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method.

A proteomic study of microgravity cardiac effects: feature maps of label-free LC-MALDI data for differential expression analysis

We present a computational analysis of Mass Spectrometry (MS) data based on a proteomic study of mice cardiac tissue samples whose measured changes in peptide and protein abundance were obtained under normal (control or CTRL) and simulated microgravity conditions (hind-limb unloading or HLU). A pipeline consisting of experimental and computational steps has been designed to achieve, respectively, pre-fractionation to simplify mouse heart protein extracts and data synthesis by feature consensus maps. Both acid and neutral protein fractions obtained from differential solubility have been digested, and peptide mixtures have been resolved by LC-MALDI. The computational tools employed to analyze the MS data and reduce their complex dimensionality have included spectra alignment, denoising and normalization to obtain good-quality peak detection performance. In turn, features could be identified and further refined by searching patterns across repeated measurements obtained under differential conditions (HLU-CTRL, acid-neutral protein extracts). The assessment of reproducibility aspects for evaluating the efficacy of label-free comparative proteomic analysis, combined with the goal of identifying from HLU-CTRL consensus maps candidate proteins with differential expression, led to a computationally efficient approach delivering proteins related to the basic heart functions, cardiac stress and energy supply.

Nonlinear preprocessing method for detecting peaks from gas chromatograms

BackgroundThe problem of locating valid peaks from data corrupted by noise frequently arises while analyzing experimental data. In various biological and chemical data analysis tasks, peak detection thus constitutes a critical preprocessing step that greatly affects downstream analysis and eventual quality of experiments. Many existing techniques require the users to adjust parameters by trial and error, which is error-prone, time-consuming and often leads to incorrect analysis results. Worse, conventional approaches tend to report an excessive number of false alarms by finding fictitious peaks generated by mere noise.ResultsWe have designed a novel peak detection method that can significantly reduce parameter sensitivity, yet providing excellent peak detection performance and negligible false alarm rates from gas chromatographic data. The key feature of our new algorithm is the successive use of peak enhancement algorithms that are deliberately designed for a gradual improvement of peak detection quality. We tested our approach with real gas chromatograms as well as intentionally contaminated spectra that contain Gaussian or speckle-type noise.ConclusionOur results demonstrate that the proposed method can achieve near perfect peak detection performance while maintaining very small false alarm probabilities in case of gas chromatograms. Given the fact that biological signals appear in the form of peaks in various experimental data and that the propose method can easily be extended to such data, our approach will be a useful and robust tool that can help researchers highlight valid signals in their noisy measurements.

A 'first stage' central performance drop in a Gabor luminance-modulation detection task

In an experiment, 20 participants had to detect a backward masked Gabor luminance-modulation target imposed on a field of uniform luminance at varying eccentricities along the horizontal meridian. Different spatial frequencies were used as target modulations. Results for a 7.0 c/deg target patch showed peak detection performance at the center of the visual field and a steady decrease toward the periphery. For 1.0 c/deg, 0.75 c/deg, and 0.5 c/deg target patches, in contrast, the peak was several degrees off retinal center and decreased steadily toward the center. Findings not only confirmed the familiar sensitivity loss toward peripheral areas for high spatial frequencies, but also indicated a sensitivity loss toward central areas for low spatial frequencies. It is concluded that they further support Gurnsey et al.'s (1996) 'mismatch hypothesis' extending its scope to also include 'first-stage' stimuli.