Signal Detection Method Research Articles

Signal detection and assessment supporting syndromic surveillance during the UEFA EURO 2024

Abstract Background Mass gatherings like the UEFA European Football Championship in Germany pose a challenge for public health, elevating the risk of infectious disease outbreaks. The Robert Koch Institute (RKI) is performing intensified surveillance during this event, including data from emergency departments (ED). So far, no standardised process for signal detection, their assessment and communication exists for ED surveillance. Methods We reviewed the literature to identify an appropriate signal detection method for ED surveillance. We tested different aggregation levels of the ED data, to ensure sufficient quality with sparse categories. After consulting with international experts, we defined a standardized process for signal assessment. We developed an interactive dashboard to investigate anomalies by further analysing the ED data. Results We used the Farrington Flexible algorithm, but expanded its functionality to perform daily instead of weekly monitoring. Aggregating to federal state level or in five age categories provided enough daily data for the algorithm. During the assessment, identified signals will be automatically scored based on pre-defined criteria (e.g. excess number of cases, reoccurrence in previous days). Signals with a high score will be forwarded to an epidemiological assessment, where they are manually evaluated based on standardised questions (e.g. expected seasonality, disease severity). Surveillance experts at RKI discuss them further and decide the course of communication and possible further measures. We developed an interactive dashboard in which signal detection and assessment is visualized. Conclusions Syndromic surveillance using ED data is used as an information source for early warning during a mass sporting event in Germany. Signal detection enables the identification of potential outbreaks, while the standardized assessment process ensures an efficient way to prioritize and steer communication efforts across public health services. Key messages • Syndromic surveillance using signal detection has previously proven valuable to detect potential outbreaks of infectious diseases during mass gatherings. • A standardised signal assessment process including an automatic and manual component ensures a sustainable and efficient way to prioritize possible incidents and steer public health communication.

Adverse events associated with carbamazepine: a pharmacovigilance study using the FDA Adverse Event Reporting System

ABSTRACT Introduction Carbamazepine (CBZ) is a commonly used antiseizures medications (ASM), but its adverse drug reactions (ADRs) can impact the successful management of epilepsy. There are currently limited systematic studies on ADRs related to CBZ, necessitating further investigation. Areas covered Using the FDA Adverse Event Reporting System (FAERS) database , we extracted reports where CBZ was the primary suspect, conducting subgroup analyses stratified by sex and age. Four risk signal detection methods ROR, PRR, BCPNN, and EGBM were employed to systematically analyze the ADRs associated with CBZ. Expert opinion In the epilepsy population, ADRs related to CBZ often involve examinations, hereditary disorders, and infections. Subgroup analysis showed differences in ADR signals among male, female, elderly, and young patients. Human Herpesvirus 6 Infection and Dermatitis Exfoliative were consistent CBZ-induced ADRs, unaffected by age or sex. The study also identified previously overlooked ADRs such as bone metabolism abnormalities, ocular toxicity, and ototoxicity. Many ADRs linked to CBZ remain underreported. It is crucial to enhance monitoring and information dissemination about CBZ use in epileptic patients. Adjusting medication regimens for high-risk individuals, potentially incorporating acupuncture, traditional Chinese medicine, or alternative anti-seizure medications, should be considered when necessary.

A Clustering Ensemble Method for Drug Safety Signal Detection in Post-Marketing Surveillance.

Post-marketing surveillance refers to the process of monitoring the safety of drugs once they reach the market, after the successful completion of clinical trials. In this work, we investigate a computational approach using data mining tools to detect safety signals from post-market safety databases, or in other words, to identify adverse events (AEs) with disproportionately high reporting rates compared to other AEs, associated with a particular drug or a drug class. Essentially, we view this as a problem of cluster analysis-based anomaly detection on post-market safety data, where the goal is to 'unsupervisedly' detect the anomalous or the signal AEs. Our findings demonstrate the potential of using a clustering ensemble method to detect drug safety signals. It employs multiple clustering or anomaly detection algorithms, followed by a performance comparison of the candidate algorithms based on a collection of appropriate measures of goodness of clustering results. The method is general enough to include any number of clustering or anomaly detection algorithms and goodness measures, and performs better than any of the candidate algorithms in identifying the signal AEs. Extensive simulation studies illustrate that the ensemble method detects the AE signals from synthetic post-market safety datasets pretty accurately across the different scenarios explored. Based on the cases reported to the FDA Adverse Event Reporting System (FAERS) between 2013 and 2022, we further demonstrate that the ensemble method successfully identifies and confirms most of the adverse events that are known to occur most frequently in reaction to antiepileptic drugs and -lactam antibiotics.

Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from O(K3) to O(K2) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate.

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Integration of FAERS, DrugBank and SIDER Data for Machine Learning-based Detection of Adverse Drug Reactions

AbstractTraditionally, disproportionality analysis (DPA) methods are employed for signal detection in pharmacovigilance, but these methods utilize only a limited portion of the data available from spontaneous event reports (SERs). This research aims to enhance signal detection by applying machine learning (ML) methods that can leverage additional data. We create a dataset by integrating SER data from the FDA Adverse Event Reporting System (FAERS) with biological and chemical data from DrugBank, and information on known adverse drug reactions (ADRs) from Side Effect Resource (SIDER). The known ADRs from SIDER are used to label the dataset for ML training. Using the AutoML library TPOT, ML models are trained on this dataset. Our findings indicate that ML models, even when trained with the same features as DPA methods, achieve higher recall and precision. Moreover, incorporating additional features related to drugs and events significantly boosts the performance of ML models. Analysis using the explainable AI (XAI) technique SHAP reveals that the drug name, event name, and fifth-level ATC code are the most influential features for model predictions. These ML models offer a promising alternative or supplement to conventional DPA methods for signal detection in pharmacovigilance.

Post-marketing safety of tralokinumab: a real-world pharmacovigilance study based on the FDA adverse event reporting system

ABSTRACT Background Tralokinumab is a fully human IgG4 monoclonal antibody targeting IL-13, used for treating atopic dermatitis. This study analyzed tralokinumab-related adverse drug events by mining the Food and Drug Administration Adverse Event Reporting System (FAERS) database to provide a safety reference for clinical application. Methods Adverse drug event reports from Q1 2022 to Q2 2024 were extracted from the FAERS database. After standardizing the data, various signal detection methods were used for analysis, including ROR, PRR, BCPNN, and MGPS. Results A total of 1,820 reports of adverse events (AEs) with tralokinumab as the primary suspected drug were identified. 70 preferred terms (PTs) met the criteria across four signal detection methods, involving 11 system organ classes (SOCs). These included known adverse reactions like conjunctivitis and injection site reactions, and signals not previously reported in clinical trials, such as eye pruritus, dry eye, eye swelling, pneumonia pneumococcal, and cutaneous T-cell lymphoma. Most AEs occurred within one month of initiating tralokinumab treatment. Conclusions Based on the FAERS database, this study comprehensively and systematically analyzed AE signals in tralokinumab treatment. The results enhance the understanding of tralokinumab’s safety and serve as valuable references for reducing the risk of adverse reactions during clinical use.

Fe7S8 nanosheets- catalyzed colorimetric and fluorescence dual ratio sensing for high selectivity detection of ascorbic acid

By using Fe7S8 nanosheets (NSs) as catalyst, O-phenylenediamine (OPD) as chromogenic substrate, H2O2 as oxidant, and ascorbic acid (AA) as regulator, a dual ratio colorimetric and fluorescence sensing system for determining AA was constructed. The inherent peroxidase like activity of Fe7S8 NSs enables it to catalyze the oxidation of OPD in the existence of H2O2, resulting in the formation of yellow-colored 2, 3-diaminophenazine (DAP) with a characteristic absorption peak at 450 nm and a fluorescence emission peak at 560 nm. However, the presence of AA could suppress the oxidation of OPD and induce the generation of quinoxaline derivative (QXD) 3-(dihydroxyethyl) furo [3,4-b] quinoxaline-1-one with absorption and fluorescence emission peaks located at 345 and 435 nm, respectively. Thus, employing the absorption of QXD at 345 nm and emission at 435 nm as the reporting signals, and the absorption of DAP at 450 nm and emission at 560 nm as the reference signals, a ratio colorimetric (A345/A450) and ratio fluorescence (F435/F560) dual signal detection method for AA was developed. Under the optimized conditions, the value of A345/A450 was found to be linear with AA concentration in the ranges of 1.0–40 μM and 40–100 μM, and the value of F435/F560 was strongly correlated with AA concentration in the ranges of 1.0–30 μM and 30–100 μM. When applied for the assay of AA in human serum samples, an acceptable recovery ranging from 93.1 to 106.6 % was obtained, confirming the potential of the sensing system in practical applications.

Multi-frequency weak signal detection based on Liu-like chaotic synchronization system and its hardware circuit implementation

Considering the shortcomings of traditional chaotic systems in weak signal detection methods, such as the high threshold sensitivity requirement and the narrow detection frequency domain. This study proposes a novel three-dimensional chaotic synchronization system, and the dynamical of the system are exhaustively characterized using equilibrium points, phase diagrams, Lyapunov exponential spectra, and bifurcation diagrams. This method involves weak signal detection by means of chaotic synchronization control. Synchronization of a chaotic system using a backstepping synchronization method is used to detect weak signals by analyzing the synchronization error after the introduction of weak signals in a strong noise background. The chaotic system is implemented by hardware circuits, and the simulation of chaotic synchronization control and detection of weak signals from the perspective of circuits is carried out by circuit simulation software. Additionally, the frequency range within which the system is capable of weak signal detection is tested through extensive simulation experiments. Finally, multi-frequency signals detection experiments are performed. The experimental results demonstrate that the system can accurately detect the frequency of weak signals address the limitations of narrow-band detection and multi-frequency signal detection is possible. Meanwhile, the circuit structure proposed in this paper is simple and has some value for engineering applications.

A novel anomaly detection method for magnetic flux leakage signals via a feature-based unsupervised detection network

High-precision anomaly detection, as the key technology of magnetic flux leakage (MFL) signal detection, is a challenging task. It is difficult to detect anomalies in MFL signals due to the variety of anomalies and the characteristics of the anomalies are easily submerged in the variation of the natural signals. To address the above issues, a feature-based unsupervised detection network (FUDet) is designed, which accomplishes the unsupervised anomaly detection task through feature discrimination and feature reconstruction. Firstly, a bidirectional discrimination module is proposed, which can input normal and anomaly feature distributions to mine the characteristics of samples, so as to enhance the ability of the model to recognize anomaly signals. Secondly, a dynamic noise generation module is designed to generate different feature distributions for each input that are consistent with the characteristics of MFL signals. This module creates an adversarial effect with the discriminator, allowing it to identify more subtle feature differences through training. Finally, a reconstruction classification module is designed to naturally reconstruct the non-normal features and normal features into normal signals, which can be used to detect anomalies by comparing the difference between the input signals and the reconstructed signals. Experimentally, the method is proved to outperform the P-AUROC of the state-of-the-art method by 3.1% under MFL signals and achieves outstanding results in MFL signal anomaly detection.

A High-Precision, Ultra-Short Baseline Positioning Method for Full Sea Depth

To fulfill the demand for high-precision underwater acoustic positioning at full sea depth, an ultra-short baseline (USBL) positioning method with the square array based on the least squares estimating signal parameters via rotational invariance techniques (LS-ESPRIT) algorithm is presented in this paper. A combination of beam tracking and beamforming is employed to improve the accuracy of direction-of-arrival (DOA) estimation and, consequently, enhance overall positioning accuracy. In order to mitigate the issue of position jumping resulting from phase ambiguity in traditional four-element cross arrays, we have improved the stability of the positioning algorithm by utilizing a multi-element square array and employing the LS-ESPRIT algorithm for DOA estimation. Furthermore, the signal detection method integrating the correlation coefficient and ascending/descending chirp signals is employed to enhance the reliability of the location algorithm. Simulation analysis and experimental results demonstrate that the proposed algorithm effectively enhances positioning accuracy and improves the problem of jumping in positioning results.

Applying causal inference and Bayesian statistics to understanding vaccine safety signals using a simulation study

Community perception of vaccine safety influences vaccine uptake. Our objective was to assess current vaccine safety monitoring by examining factors that may influence the availability of post-vaccination survey data, and thereby the specificity and sensitivity of existing signal detection methods. We used causal directed acyclic graphs (DAGs) and a Bayesian posterior predictive analysis (PPA) signal detection method to understand biological and behavioural factors which may influence signal detection. The DAGs informed the data simulated for scenarios in which these factors were varied. The influence of biological factors such as severity of adverse reactions and behavioural factors such as healthcare-seeking behaviour upon survey participation was found to drive signal detection. Where there was a low prevalence of moderate to severe reactions, false signals were detected when there was a strong influence of reaction severity on both survey participation and seeking medical attention. These findings provide implications for future vaccine safety monitoring.

Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data

Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

Considerations for practical use of tree-based scan statistics for signal detection using electronic healthcare data: a case study with insulin glargine

ABSTRACT Background Hypothesis-free signal detection (HFSD) methods such as tree-based scan statistics (TBSS) applied to longitudinal electronic healthcare data (EHD) are increasingly used in safety monitoring. However, challenges may arise in interpreting HFSD results alongside results from disproportionality analysis of spontaneous reporting. Research design and methods Using the anti-diabetes drug insulin glargine (Lantus®) we apply two different tree-based scan designs using TreeScan™ software on retrospective EHD and compare the results to one another as well as to results from a disproportionality analysis using SRD. Results The self-controlled tree temporal scan method produced the larger number of alerts relative to propensity-score matched approach; however, far fewer alerts were observed when analyses were limited to EHD in inpatient/emergency room settings only. Very few reference adverse events were observed using TBSS methods on EHD relative to disproportionality methods in SRD. Conclusion Differences in detected alerts between TBSS methods and between TBSS and disproportionality analysis of SRD are likely attributable to differences in data, comparator, and study design. Our results suggest that HFDS methods like TBSS applied to EHD may complement more traditional approaches such as disproportionality analysis of SRD to provide a more complete picture of product safety in the post-approval setting.

Buzzfindr: Automating the detection of feeding buzzes in bat echolocation recordings.

Quantification of bat communities and habitat heavily rely on non-invasive acoustic bat surveys the scope of which has greatly amplified with advances in remote monitoring technologies. Despite the unprecedented amount of acoustic data being collected, analysis of these data is often limited to simple species classification which provides little information on habitat function. Feeding buzzes, the rapid sequences of echolocation pulses emitted by bats during the terminal phase of prey capture, have historically been used to evaluate foraging habitat quality. Automated identification of feeding buzzes in recordings could benefit conservation by helping identify critical foraging habitat. I tested if detection of feeding buzzes in recordings could be automated with bat recordings from Ontario, Canada. Data were obtained using three different recording devices. The signal detection method involved sequentially scanning narrow frequency bands with the "Bioacoustics" R package signal detection algorithm, and extracting temporal and signal strength parameters from detections. Buzzes were best characterized by the standard deviation of the time between consecutive pulses, the average pulse duration, and the average pulse signal-to-noise ratio. Classification accuracy was highest with artificial neural networks and random forest algorithms. I compared each model's receiver operating characteristic curves and random forest provided better control over the false-positive rate so it was retained as the final model. When tested on a new dataset, buzzfindr's overall accuracy was 93.4% (95% CI: 91.5%- 94.9%). Overall accuracy was not affected by recording device type or species frequency group. Automated detection of feeding buzzes will facilitate their integration in the analytical workflow of acoustic bat studies to improve inferences on habitat use and quality.

MIMO Signal Detection Based on IM-LSTMNet Model

Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.

Data mining and safety analysis of dual orexin receptor antagonists (DORAs): a real-world pharmacovigilance study based on the FAERS database.

Using the Food and Drug Administration Adverse Event Reporting System (FAERS) database, four signal detection methods were applied to mine adverse drug events (ADEs) related to use of dual orexin receptor antagonists (DORAs) to provide reference for safe clinical use. Data collected from Q3rd 2014 to Q4th 2023 were obtained from the FAERS database. According to the preferred terminology (PT) and systematic organ classification (SOC) of MedDRA v.26.0, the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN) were used to detect ADE signals. A total of 11,857 DORAs-related adverse reactions were detected, reported with suvorexant, lemborexant, and daridorexant as the main suspected drugs was 8717584, and 2556, respectively. A higher proportion of females than males were reported (57.27% vs. 33.04%). The top 20 positive PT signals from three DORAs showed that "sleep paralysis" ranked first. "Brain fog" was stronger following daridorexant but was not detected for the other two drugs, and "sleep sex" and "dyssomnia" were stronger in suvorexant but not in the other two drugs. Additionally, some PTs occurred that were not included in drug instructions, such as "hangover" and "hypnagogic hallucination." In this study, four algorithms (ROR, PRR, BCPNN, and MGPS) were used to mine the safety signals of DORAs. We identified some potential ADE signals that can promote the rational use of DORAs and improve their safety.

Improved echo signal detection for pseudo-random single-photon counting laser ranging

In this paper, a new echo signal detection method, to the best of our knowledge, for pseudo-random single-photon counting ranging (PSPCR) LiDAR systems is proposed, which is applied for long distances, low repetition rates, and system cost reduction. First, in order to achieve a comparable temporal resolution as that in time-correlated single-photon counting (TCSPC) systems, we extend the pseudo-random code to discriminate the minimal time slot in time correlation. Second, we use the full width at half maxima (FWHM) in the duration of each pseudo-random code for correlation to reduce the impact of pulse width variation and timing jitters on ranging accuracy. Third, we study the bias and errors caused by using synchronous signals as the “START” signal, and propose to use the time of flight (ToF) at half energy to reduce the walk error. Simulation results show that, compared with existing PSPCR methods, the proposed method improves ranging accuracy with a lower repetition rate and lower peak and average power—centimeter-level ranging accuracy over tens of kilometers can be achieved using a laser with a repetition rate of 400 kHz, peak power of up to 1 kW, and average power of up to 1 W.

Research on electromagnetic pulse signal detection method based on intelligent compressed sensing technology

Abstract Electromagnetic pulse signal measurement is crucial for national security, scientific research, technological advancement, and emergency preparedness, playing a vital role in various aspects of modern society. In this work, an electromagnetic pulse signal detection approach utilizing compressed sensing technology is suggested. By unitizing three parallel low-frequency analog-to-digital converters, the high-frequency acquisition waveform of the measured electromagnetic pulse signal can be recovered. The compressed sensing effect of the proposed system on electromagnetic pulse signal is tested by simulation, which verifies the feasibility of this method.