Peak Detection Method Research Articles

Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning

Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. This model processes a triad of cardio cycles—the preceding, current, and following cycles—to capture temporal dependencies and hidden features related to arrhythmias. Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. Using the MIT-BIH database, our approach achieved an accuracy of 99.43%, with F1-scores approaching 100% for major arrhythmia classes. The integration of these methods enhances both the performance and transparency of arrhythmia detection systems.

A Robust and Sensitive New Peak Detection and Identification Method for Mass Spectrometry-Based Differential Analysis in Biologics Characterization.

New peak detection (NPD) is a significant component of the multiattribute method (MAM) for MS use to facilitate the detection of quality attributes exhibiting abnormal ratio changes, vanishing attributes, or newly emerging attributes. However, challenges remain to get a balanced sensitivity and minimize false positives in NPD. In this study, we have developed a robust NPD and identification method to enhance sensitivity 10-fold (0.5% spike-in) compared to previously reported work while maintaining controlled false positives via a statistics-driven experimental design utilizing three control samples and a product-specific peptide library. This method not only enables MAM to replace conventional analytical methods for quality attribute control, but also provides a new and objective way of performing differential analysis of LC-MS-based experiments at different stages of the biopharmaceutics process development.

Annual Cropping Intensity Dynamics in China from 2001 to 2023

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions.

Speckle Vibrometry for Contactless Instantaneous Heart Rate and Respiration Rate Monitoring on Mechanically Ventilated Patients.

Objective: Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods: In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results: Compared with electrocardiography, the present work achieves limits of agreement of [-2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [-0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions: These results showcased Speckle Vibrometry's potential in vital sign monitoring in a clinical setting. Significance: This is the first human clinical study for Speckle Vibrometry.

Peak detection in intracranial pressure signal waveforms: a comparative study

BackgroundThe monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms.MethodsPeak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise.ResultsThe quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le 10$$\\end{document} ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data.ConclusionWhile machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.

Polypy: A Framework to Interpret Polymer Properties from Mass Spectroscopy Data.

Mass spectroscopy (MS) is a robust technique for polymer characterization, and it can provide the chemical fingerprint of a complete sample regarding polymer distribution chains. Nevertheless, polymer chemical properties such as polydispersity (Pd), average molecular mass (Mn), weight average molecular mass (Mw) and others are not determined by MS, as they are commonly characterized by gel permeation chromatography (GPC). In order to calculate polymer properties from MS, a Python script was developed to interpret polymer properties from spectroscopic raw data. Polypy script can be considered a peak detection and area distribution method, and represents the result of combining the MS raw data filtered using Root Mean Square (RMS) calculation with molecular classification based on theoretical molar masses. Polypy filters out areas corresponding to repetitive units. This approach facilitates the identification of the polymer chains and calculates their properties. The script also integrates visualization graphic tools for data analysis. In this work, aryl resin (poly(2,2-bis(4-oxy-(2-(methyloxirane)phenyl)propan) was the study case polymer molecule, and is composed of oligomer chains distributed mainly in the range of dimers to tetramers, in some cases presenting traces of pentamers and hexamers in the distribution profile of the oligomeric chains. Epoxy resin has Mn = 607 Da, Mw = 631 Da, and polydispersity (Pd) of 1.015 (data given by GPC). With Polypy script, calculations resulted in Mn = 584.42 Da, Mw = 649.29 Da, and Pd = 1.11, which are consistent results if compared with GPC characterization. Additional information, such as the percentage of oligomer distribution, was also calculated and for this polymer matrix it was not possible to retrieve it from the GPC method. Polypy is an approach to characterizing major polymer chemical properties using only MS raw spectra, and it can be utilized with any MS raw data for any polymer matrix.

Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

A novel rule mining method for knowledge discovery of relationships among indoor air quality, HVAC operation and occupants’ activities

This paper presents a novel data mining method to discover the occurrence time, co-occurrence and sequential association rules among indoor air quality (IAQ), Heating, Ventilation and Air Conditioning (HVAC) operation and occupants' activities. The novelty of this work includes a peak detection method to discover the pollutants' peaks and a rule mining method with extended rule selection criteria that considers both the time lags between events and relationships among multiple events. The peak detection method aimed to use moving windows and criteria to find the candidate peaks and the non-maximum suppression to filter the overlapped candidate peaks. The proposed rule mining method is based on the Apriori and Apriori-All with the rule selection criteria extended by labeling the occurrence time to events to find the rules among various occurrence times and multiple events and by limiting the time lags among the occurrences of the events to discover the sequential rules. Two datasets, one from 70 houses and one from a single commercial building, were used to evaluate the performance of this proposed data-driven method. The results showed that 93.19 % of the peak detection results matched the peaks detected from the data records. The CO2 peaked frequently at 12:00, 17:00 and from 19:00 to 24:00. The occurrence of occupants' activities could lead to pollutants’ concentrations and pollutants peaks within 2 hours in the residential buildings. The occurrence of a high Wifi connection number was frequently followed by a CO2 peak within 2 hours in the commercial building. These findings can support the development of control strategies for building HVAC systems to achieve better indoor air quality control.

An Empirical Approach to Rerouting Visible Light Pathways Using an Adjustable-Angle Mirror to Sustain Communication between Vehicles on Curvy Roads

In this paper, a novel method is demonstrated to sustain vehicle-to-vehicle (V2V) communication on curvy roads via the arrangement of the lateral position of a self-angle-adjustable mirror–reflective road sign (SAAMRS) and light-direction-sensing wide-angle complementary photodiodes (CPDs). Visible light communication (VLC) between vehicles attracts attention as a complementary technology to radio-frequency-based (RF-based) communication technologies due to its wide, license-free spectrum and immunity to interferences. However, V2V VLC may be interrupted on curvy roads due to the limited field of view (FOV) of the receiver or the line of sight (LOS) being interrupted. To solve this problem, an experiment was developed using an SAAMRS along with wide-angle light-direction-sensing CPDs that used a precise peak detection (PPD) method to sustain communication between vehicles in dynamic environments by rerouting the incident light with the highest signal intensity level to the receiver vehicle on curvy roads. We also used real images of curvy roads simulated as polynomials to calculate the necessary rotation angles for the SAAMRS and regions where communication exist. Our experimental results overlapped almost completely with our simulations, with small errors of approximately 4.8% and 4.4% for the SAAMRS angle and communication region, respectively.

A novel SPM error measurement method based on peak detection of nodes centers in a 2D orthogonal lattice as standard

Abstract This paper presented a novel scanning probe microscope (SPM) error measurement method by using a 2D micro-scale orthogonal lattice standard and peak detection (PD) method of the pitch center coordinates based on cross-correlation/convolution (CC) filtering of images raster-scanned by SPMs. The geometric errors of motion and drifting rate in x-y plane are measured. Geometric errors include positional deviations E x x and E y y , straightness deviations E x y and E y x along x- and y-axis respectively, and the orthogonality deviation E o x y between the two axes. The calibration factors C x and C y were calculated based on the number of pixels scanned on the x-axis and y-axis, scanning range, average pitch of standard lattice from metrological verification certification, and average lattice pitch calculated by using PD method. Through case study, the errors of an AFM was measured using an orthogonal lattice standard with a nominal pitch of 10 μm, resulting in C x and C y values of 0.925 and 1.050, respectively, an orthogonal deviation E o x y of 0.015°, and the maximum drift rate of the x-axis and y-axis is −366.21 nm min−1 and −317.38 nm min−1, respectively. The new error measurement method of SPMs provides technical reference for the development and performance improvement of SPM instruments.

Acquisition and Analysis of Cardio Graphic Signal Using Various Transforms

In the modern days, cardiac problems among various age group people have increased by a large ratio. Electro cardiograph is widely used to monitor the electrical activities of the heart. With the help of ECG, we can able to easily detect and classify the irregular activities of the heart. Hence in this study, we have acquired ECG signals from ten different subjects of various age groups using the BIOPAC system. All the acquired ECG signals are pre-processed using the infinite impulse response bandpass filter. Then the most vital R wave is detected by using the peak detection method present in-built in the BIOPAC system. This R wave detection plays a very vital role in diagnosing heart rhythm irregularities, calculating the heart rate variability, recognizing the heart rate variability, and measuring the heart rate. The pre-processed ECG signal is processed using highly advanced windowing techniques to extract the power spectrum and frequency-based values of the ECG signal. The results of the Fourier transform and spectral density of the processed ECG signals are displayed in this study. The features extracted from ECG signals acquired from ten different subjects have been tabulated in this study.

Mapping of the central sulcus using non-invasive ultra-high-density brain recordings

Brain mapping is vital in understanding the brain’s functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain–computer interfacing (BCI), rehabilitation, and mental health.

Innovative non-contact r-r intervals estimation using viterbi algorithm with Squared Branch Metric (VSBM)

Non-contact heartbeat detection by using Doppler sensor is a critical component of remote health monitoring systems, enabling continuous and unobtrusive monitoring of an individual’s cardiovascular health. In this paper, we reported an innovative approach for non-contact heartbeat detection using the Viterbi algorithm, leveraging the distribution of the difference of two adjacent R-R intervals (RRIs). RRIs represent the time between successive peaks in the electrocardiogram (ECG) signal and are fundamental in analyzing heart rate variability, mental stress conditions and heart diseases. Numerous non-contact Doppler sensor-based methods have been proposed for heartbeat detection, leveraging the evaluation of RRIs without physical device attachment. However, challenges arise from unwanted peaks introduced by respiration and slight body movements, even when the subject remains motionless with normal breathing. This study presented an innovative approach for selecting heartbeat peaks utilizing the Viterbi algorithm with the squared difference of two adjacent RRIs as the Branch metric (BM). Our preliminary experiments reveal that the difference between two adjacent RRIs closely follows a Gaussian distribution. Building upon this observation, we considered the Viterbi algorithm with Squared Branch Metric (VSBM) to estimate the heartbeat accurately. To assess the accuracy of our peak selection method, we conducted experiments comparing it with two existing peak detection methods: (i) Doppler output after Low-Pass Filter (LPF)-based method and (ii) Spectrogram-based method. Our results demonstrate that the proposed VSBM method is effective to detect the heartbeat accurately for each peak detection method. Furthermore, we compared the performance of “Spectrogram + VSBM” outperforms the “Doppler output after LPF + VSBM” method by the Root-Mean-Square Error (RMSE) of RRIs.

Microseismic activity characteristics and range evaluation of hydraulic fracturing in coal seam

Accurately evaluating hydraulic fracturing (HF) activity characteristics and impact range is critical to optimizing the HF scheme design. This study proposes an adaptive threshold peak detection method and a wavelet transform-support vector machine (WT-SVM) polarity analysis method based on microseismic (MS) monitoring data from a coal mine in Guizhou, China. The results show that the adaptive threshold peak detection method effectively improves the accuracy and stability of the P-wave initial arrival pickup for low signal-to-noise ratio (SNR) signals. The main frequency band of the MS signal of HF is focused on 150–300 Hz. The improved particle swarm optimization algorithm was used by introducing a random-weight strategy to determine the high-density impact areas of the HF within approximately 20 m on both sides of the fracturing borehole. Density cloud map analysis revealed a small area of the blank zone at the junction of the fracturing section, which provided a basis for fracturing parameter design and scheme optimization. The directional characteristics of the MS waveform were quantitatively described by polarity analysis, where the dip angle distribution ranged from 39° to 49°, the strike angle ranged from 61° to 74°, the slip angle was concentrated near 44°, and the stretch angle ranged from 61° to 74°. The WT-SVM proposed in this study overcame the effects of low SNR signals and the limitations of a small-area station network; 62% of the shear ruptures and 38% of the tensile ruptures of the HF were obtained. Shear cracks were the primary expression of the HF rupture type.

Design of fault location algorithm based on online distributed travelling wave for HV power cable.

To tackle the challenge of localization failure due to traveling wave dispersion during a mid-line fault in a long high-voltage cable, this study conducts an in-depth analysis of the refraction characteristics of the cumulative three-phase sheath currents at cross commutation points and direct grounding points, facilitated by detailed theoretical derivation. We introduce the novel concept of a 'first traveling wave attenuation index,' which quantifies the peak of the initial faulted traveling wave. We strategically place distributed traveling wave detection devices solely in the first direct grounding box of each cross-interconnected main section, effectively segmenting the line into five distinct zones. These zones are identified based on the unique transient polarities exhibited by the first traveling wave of fault current at each impedance mismatch point along the cable. To overcome the issue of weak traveling wave signals collected at the first end measurement point, we propose an innovative peak detection method for the first wave. This method harnesses the power of empirical wavelet transform (EWT) and multi-resolution singular value decomposition (MRSVD), providing a significant boost in ranging accuracy compared to traditional wavelet methods. Simulation results validate the efficacy of our proposed fault location method, which accurately pinpoints the fault section by contrasting the polarity of the first wave peak of the sheath current detected at each measurement point. Notably, our method ensures safety and convenience as the equipment does not require direct contact with high voltage.

A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions

Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. First, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.

Impact of probe volume and peak detection methods on lidar rotor effective wind speed and turbulence intensity estimations

Lidar simulation techniques are a suitable and increasingly reliable alternative for testing lidar measuring strategies and illustrating their response when combined with modelled wind fields. In this work, two simulation tools are combined to assess the uncertainty in the derivation of the rotor effective wind speed and the wind speed variance from a forward-looking nacelle-mounted continuous wave lidar wind speed estimations. These uncertainties are analysed for a variety of atmospheric turbulence levels and lidar measuring strategies. A synthetic turbulence generator is used to create the reference wind fields. Subsequently, a lidar simulator operated in a continuous-wave mode is used to scan the synthetic wind fields and perform a sensitivity analysis by comparing first- and second-order statistics against reference values. The lidar simulator is enhanced with three Doppler peak detection methods, namely the maximum, the median and the centroid, to extract radial wind speeds from the velocities found within the probe volume. The results show that probe volume and peak detection methods influence the uncertainty of the wind speed variance. The uncertainty in time-averaged and instantaneous rotor effective wind speed estimations is not sensitive to the lidar spatial averaging or peak detection methods investigated. Finally, we saw that the turbulence intensity influences the derived lidar quantities and is the main driver of the variations in rotor effective wind speed uncertainty estimations.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

Comment on "Dying in the Sun: Direct evidence for elevated UV-B radiation at the end-Permian mass extinction".

Liu etal. present evidence of increased abundance of UV-B-absorbing compounds in fossilized sporomorphs at the end-Permian mass extinction based on Fourier transform infrared (FTIR) spectroscopy. Their approach assumes that UV-B-absorbing compounds are present in the fossilized sporomorphs spanning the extinction interval and that they can be quantified using FTIR. However, further analysis reveals that the signal that they aim to focus on is weak and poorly resolved against nonrandom background interference most likely associated with water vapor. We also show that the peak detection methods that they use are inappropriate for use on these fossil sporomorphs because their methods select only 3.9% of the spectra at the target waveband of interest. The reconstruction that they present is based on baseline variations in the spectra and cannot be confidently attributed to variations in UV-B-absorbing compounds. "Direct" evidence for UV-B radiation at the end-Permian mass extinction cannot be claimed to have been observed in this record.

Fatigue Estimation Using Peak Features from PPG Signals

Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. Gaining insight into one’s fatigue status over time empowers effective management and risk reduction associated with other ailments. Utilizing photoplethysmography (PPG) signals brings advantages due to their easy acquisition and physiological insights. This study crafts a specialized preprocessing and peak detection methodology for PPG signals. A novel fatigue index stems from PPG signals, focusing on the dicrotic peak’s position. This index replaces subjective data from the brief fatigue index (BFI)-Taiwan questionnaire and heart rate variability (HRV) indices derived from PPG signals for assessing fatigue levels. Correlation analysis, involving sixteen healthy adults, highlights a robust correlation (R > 0.53) between the new fatigue index and specific BFI questions, gauging subjective fatigue over the last 24 h. Drawing from these insights, the study computes an average of the identified questions to formulate the evaluated fatigue score, utilizing the newfound fatigue index. The implementation of linear regression establishes a robust fatigue assessment system. The results reveal an impressive 91% correlation coefficient between projected fatigue levels and subjective fatigue experiences. This underscores the remarkable accuracy of the proposed fatigue prediction in evaluating subjective fatigue. This study further operationalized the proposed PPG processing, peak detection method, and fatigue index using C# in a computer environment alongside a PPG device, thereby offering real-time fatigue indices to users. Timely reminders are employed to prompt users to take notice when their index exceeds a predefined threshold, fostering greater attention to their physical well-being.