Signal Detection Method Research Articles

Trends in Adverse Event Reports and Signal Detection of Adverse Event Following Vaccination

As vaccines are administered to many people, the management of adverse reactions to vaccines should be a priority. This study aims to detect signals of adverse events following immunization to provide information on the events requiring attention. The Korean Adverse Event Reporting System database from 2010 to 2019 was used. Time series, status, and signal analyses were performed. The number of adverse events following immunization from 2010 to 2019 was determined. A comparison group was established considering the main vaccination targets for each vaccine. Signal information was detected that satisfies all four signal detection methods: proportional reporting ratio, reporting odds ratio, information component, and TreeScan. From 2010 to 2019, the number of adverse events following immunization reported was 37,688. The vaccines having the most reported adverse events were influenza (17,290 cases, 45.9%). Because of identifying the signal information of the top-10 vaccines based on adverse events, 74 adverse events were identified in 6 vaccines. The results are expected to contribute to the prevention of adverse events by providing information on adverse events following immunization requiring attention.

Regression Analysis of Factors Based on Cluster Analysis of Acute Radiation Pneumonia due to Radiation Therapy for Lung Cancer.

We conducted in this paper a regression analysis of factors associated with acute radiation pneumonia due to radiation therapy for lung cancer utilizing cluster analysis to explore the predictive effects of clinical and dosimetry factors on grade ≥2 radiation pneumonia due to radiation therapy for lung cancer and to further refine the effect of the ratio of the volume of the primary foci to the volume of the lung lobes in which they are located on radiation pneumonia, to refine the factors that are clinically effective in predicting the occurrence of grade ≥2 radiation pneumonia. This will provide a basis for better guiding lung cancer radiation therapy, reducing the occurrence of grade ≥2 radiation pneumonia, and improving the safety of radiotherapy. Based on the characteristics of the selected surveillance data, the experimental simulation of the factors of acute radiation pneumonia due to lung cancer radiation therapy was performed based on three signal detection methods using fuzzy mean clustering algorithm with drug names as the target and adverse drug reactions as the characteristics, and the drugs were classified into three categories. The method was then designed and used to determine the classification correctness evaluation function as the best signal detection method. The factor classification and risk feature identification of acute radiation pneumonia due to radiation therapy for lung cancer based on ADR were achieved by using cluster analysis and feature extraction techniques, which provided a referenceable method for establishing the factor classification mechanism of acute radiation pneumonia due to radiation therapy for lung cancer and a new idea for reuse of ADR surveillance report data resources.

Analysis of Adverse Reactions in Elderly Patients Based on Quantitative Methods of Signal Detection

A well-known problem in pharmacotherapy is an increased risk of adverse drug reactions (ADRs) in older as compared to younger patients. The Russian pharmacovigilance database includes a significant number of spontaneous reports of suspected ADRs in patients aged 65 and older. An increase in ADRs reporting makes it difficult to identify potential safety signals based on qualitative approaches only, which necessitates the use of statistical methods for signal detection based on disproportionality. The aim of the study was to assess the applicability of quantitative methods for signal detection and analysis of ADR risks in the elderly using the Russian spontaneous report database. Materials and methods: the study covered the reports on patients 65 years of age and older, which were submitted to the spontaneous report database from January 2008 until June 2018. The procedure recommended by the European Medicines Agency was used to identify potential statistical safety signals which were determined based on the following criteria: Reporting Odds Ratio, ROR—lower bound of the 95% confidence interval >1, number of cases ≥2; Proportional Reporting Ratio, PRR ≥ 2, Chi-square value χ 2 ≥ 4, number of cases ≥3, lower bound of the 95% confidence interval >1, number of cases ≥3. Results: 2231 potential statistical signals were identified. Of these, the vast majority of combinations of suspected drugs and ADRs were associated with known drug risks, and were not new safety signals for these drugs. The largest proportion of statistical signals was attributed to the following pharmacological groups: antiplatelet agents, cephalosporins, non-steroidal anti-inflammatory drugs, fluoroquinolones, angiotensin-converting enzyme inhibitors, metabolic agents, and indirect anticoagulants. Conclusion: the results obtained indicate the applicability and effectiveness of statistical methods based on disproportionality of reporting for the analysis of the Russian spontaneous report database in order to identify potential drug safety issues.

Iterative signal detection to mitigate ISI and MUI for diffusion-based molecular communications

Molecular communication via diffusion (MCvD) where bio-molecules carry information is a most promising and viable solution for communication between nanoscale devices. However, slow moving molecules cause inter-symbol-interference (ISI) which is one of the major impediments in MCvD systems. In this work, a novel iterative block-wise non-coherent signal detection method is proposed to mitigate the ISI at the receiver. Since the proposed method does not require the channel state information at the receiver, this reduces the molecular communication system’s complexity. The bit error rate (BER) performance of the proposed iterative detection method is evaluated in the presence of both ISI and counting noise. Substantial improvement in MC system’s BER performance is observed by using the proposed detection method as compared to conventional approach. Furthermore, effectiveness of the proposed scheme is also analyzed in the presence of multiuser interference.

Prescription sequence symmetry analysis in pharmacoepidemiology: a systematic review

Objective: To systematically evaluate and analyze the original research of prescription sequence symmetry analysis (PSSA), summarize its research progress and methodological details, and provide a reference for the future use of this method. Methods: The keywords related to PSSA were used for literature retrieval from Chinese databases (CNKI, Wanfang, and VIP) and English databases (PubMed, Embase, and Cochrane). Original articles that were related to PSSA and published before June 30, 2020 were selected. Information form was developed by Excel. Stata was used for the statistics analysis. Results: There were 45 eligible articles included in the research. Since 2013, the number of studies using PSSA has increased rapidly. These studies were mainly conducted in Japan (n=11, 24.44%), China (n=10, 22.22%), Denmark (n=9, 20.00%), and Australia (n=8, 17.78%). Medical claim database was used most commonly when PSSA was implemented. The included studies involved 16 types of drugs, of which the number of studies of psychotropic drugs and statins was highest (n=8, 17.78%), and adverse reactions of almost all human systems were involved. In terms of methodology, 35 (77.78%) and 43 (95.56%) studies clearly reported the run-in period and interval period, of which 14 (31.11%) and 9 (20.00%) respectively gave the method or reason for determining the duration. In addition, 16 articles (35.56%) and 18 articles (40.00%) reported sensitivity analysis and subgroup analysis results, respectively. Conclusions: PSSA, one of the effective methods for safety signal detection in healthcare databases, has developed rapidly, but the methodological details and result reporting need to be improved. In China, PSSA research is still in its infancy, and it is necessary to pay attention to the quality of research and promote methodological exploration.

Variational-autoencoder signal detection for MIMO-OFDM-IM

This paper proposes a deep learning (DL) based method for signal detection in multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) systems using variational autoencoder (VAE). A framework of variational optimization is constructed and the learning deep neural network structure with fully connected layers is also designed. The network is trained offline by exploiting the dataset generated by simulations, and then the well-trained model is applied for signal detection in MIMO-OFDM-IM system. The derivation on the loss function for network training is presented from the perspective of variational inference. Also, a regularization parameter is introduced in order to achieve better performance. Numerical results are provided and the performance demonstrates the merits of the proposed method by comparison with the traditional detection algorithms. Finally, complexity measured by the runtime of the program utilizing existing detectors and the proposed method are also presented. The bit error rate (BER) performance which is close to the existing hand-crafted detectors can be obtained with lower runtime using the proposed DL network.

One size does not fit all: a summary of signal detection methods

The selection of an appropriate signal detection method is pivotal in the identification process of safety signals in pharmacovigilance. Nevertheless, the early detection of safety signals is even more important to prevent the occurrence of another thalidomide tragedy in humans. Spontaneous reports, follow-up studies, scientific literature, preclinical & clinical studies, are valuable sources of adverse events; but on the other hand, these reported adverse events are extremely diverse, hence comprehending this can result in formulating the right signal detection and evaluation strategies. Broadly, signal detection methods fall into two categories: qualitative and quantitative, each having its significance; while the quantitative methods help to handle the voluminous data during signal detection, the qualitative one does its part to pick the rare signals. Hence, there is no single universal method that would be a perfect fit to identify safety signals from all data sources or for all types of adverse events. Further, the signal detection process involves a series of steps right from signal detection to its final assessment & submission, to regulatory authorities confirming a signal as a 'possible safety alert'. Finally, the completed task of finding a confirmed safety alert would be meaningless if it does not reach the end-users of the drug concerned. Therefore, effective communication to health care professionals, patients including clinical trial subjects, pharmaceutical companies, and other stakeholders is equally important.

Chaos control and chaos synchronization of a multi-wing chaotic system and its application in multi-frequency weak signal detection

In this paper, first, a nonlinear feedback controller for achieving chaos control of a novel multi-wing chaotic system is presented. The nonlinear feedback controller has two parts. The first part is used to compensate an equilibrium point for the multi-wing chaotic system. The second part is a linear state feedback controller. The nonlinear feedback controller can globally asymptotically stabilize the multi-wing chaotic system to the equilibrium point. Stability conditions are given by using the Barbashin–Krasovskii theorem. Then, a linear state feedback controller for achieving chaos synchronization of the multi-wing chaotic system is presented. The linear state feedback controller can asymptotically stabilize the chaos synchronization error system to the origin. Stability conditions are given by using the passivity-based theory. Finally, a multi-frequency weak signal detection method is presented based on chaos control of the multi-wing chaotic system. The detection method can detect the frequencies of the weak signal and does not need to determine the critical point.

Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source.

Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

Adaptive Unsaturated Bistable Stochastic Resonance Multi-Frequency Signals Detection Based on Preprocessing

Stochastic resonance (SR) has been widely used for extracting single-frequency weak periodic signals. For multi-frequency weak signals, empirical mode decomposition (EMD) can adaptively decompose the complex signal, but this method also suffers from mode mixing, which affects the accuracy of detection. SR can convert part of the noise energy into signal energy, which compensates for the defects of EMD. According to the advantages of SR and EMD, we constructed a multi-frequency signals detection method using adaptive unsaturated bistable SR based on EMD (EMD-AUBSR). In this study, we avoid the inherent saturation of SR by reconstructing the potential function and improve the multi-frequency signals detection ability by adding the preprocessing element. For strong background noise, the experimental results show that this proposed can effectively detect multi-frequency weak signals and decrease signal aliasing, whereas EMD alone cannot.

A new Duffing detection method for underwater weak target signal

On the basis of the double coupled Duffing oscillator, a differential double coupled Duffing oscillator method, called the proposed method 1, is proposed. The large scale state is judged by the differential timing sequence diagram, and its detection result is more intuitive. To overcome the frequency limitation of differential double coupled Duffing oscillator in detecting unknown frequency periodic signal, according to the proposed method 1, based on the intermittent chaos phenomenon, the differential double coupled Duffing oscillator variable step size detection method, called the proposed method 2, is proposed. This method can be used to detect Gaussian noise background periodic signal and K-distributed noise background periodic signal. Compared with the double Duffing method and the double coupled Duffing method, its average signal-to-noise ratio (SNR) threshold is reduced by 4.94 dB and 1.68 dB respectively, and the detection result is more intuitive to judge through the differential timing sequence diagram. The accuracy and universality of the proposed method 2 are proved by the detection of three measured underwater weak target signals. Experiments show that it has some characteristics such as intuitive detection result, high sensitivity, low SNR threshold, few oscillators, low system complexity and so on.

A meta-epidemiological study found lack of transparency and poor reporting of disproportionality analyses for signal detection in pharmacovigilance databases

ObjectivesTo review and appraise methods and reporting characteristics of pharmacovigilance disproportionality analyses. Study Design and SettingWe randomly selected 100 disproportionality analyses indexed in Medline found during a systematic literature search. We then extracted and synthetized methodological and reporting characteristics using seven key items: (1) title transparency; (2) protocol pre-registration; (3) date of data extraction and analysis; (4) outcome, population, exposure and comparator definitions; (5) adjustment and stratification of results; (6) method and threshold for signal detection; (7) secondary and sensitivity analyses. ResultsWe found that methods used to generate disproportionality signals were extremely heterogeneous; there were nearly as many unique analyses as studies. The authors used various populations, methods, signal detection thresholds, adjustment or stratification variables, generally without justification for their choice or pre-specification in protocols. Moreover, 78% of studies failed to report methods for case, adverse drug reactions or comparator selection and 32 studies did not define the threshold for signal generation. ConclusionOur survey raises major concerns regarding all aspects of disproportionality analyses that could lead to misleading results and generate unjustified alarms. We advocate for a strong and transparent rationale for variable selection, choice of population and comparators pre-specified in a protocol and assessed by sensitivity analyses.

Review on tree-based scan statistic in drug and vaccine safety monitoring

To summarize the development and application of tree-based scan statistic (TreeScan), explain the methodology and provide a reference for future use of this method by reviewing the original pharmacoepidemiological and vaccine studies using the TreeScan. Medline, Embase and Web of Science databases were used for the retrieval of eligible studies using keywords related to TreeScan. A total of 15 eligible studies were included, in which 9 studies explored the adverse events of drugs and 6 studies focused on the safety of vaccines. Three types of models (Poisson probability model, Bernoulli probability model and tree-temporal scan statistic model) of TreeScan were used. The major differences among the three models were 1) whether predefined control was used according to research question, 2) whether the time from exposure to onset of adverse events was considered. Several studies explored its ability by comparing with other methods for adverse event detection or by using known adverse events. This review shows that TreeScan is an effective method for the safety signal detection of drugs or vaccines, which develops rapidly and globally. It is very necessary to promote its use in drug safety monitoring and other related fields in China.

Research on weak signal detection of integral average digital lock-in amplifier

In the process of detecting the amplitude and phase of the weak signal based on digital lock-in amplifier (DLIA), a low-pass filter (LPF) with an extremely low cut-off frequency is required. Even so, the effect is often not good. The detection results of amplitude and phase of weak signal are distorted and fluctuate sharply. To eliminate the difficulty of LPF design, and to reduce the distortion and output fluctuation of detection results of DLIA, this paper proposes a weak signal detection method based on integral average digital lock-in amplifier (IADLIA), which can be used for weak signal detection under strong noise. This method optimizes the structure of the traditional DLIA based on the principle of cross-correlation detection. And then this paper analyzes the influence of the cut-off frequency and signal-to-noise ratio of the LPF on the detection accuracy and output stability of the weak signal amplitude and phase. Finally, the corresponding weak-signal amplitude at the bearing rotating frequency is accurately detected by the proposed method, and the accurate identification of bearing faults is realized. The experimental results show that the IADLIA is characterized by stable output, strong anti-interference ability and high output accuracy. The proposed method can accurately detect the amplitude and phase information of the weak signal in a strong noise environment, get rid of the structure of LPF, and the overall performance is better than the traditional DLIA.

A PRACH Preamble Generation and Detection Model for 5GNR Systems

The basic idea of 5th generation New Radio (5GNR) is to have very high data rate and to make it work efficiently for all Internet of Things (IOT) applications like healthcare, Automotive, Industrial etc. applications. This paper provides the Orthogonal Frequency Division Multiple Access (OFDM) baseband signal generation and detection method for Physical Random-Access Channel (PRACH). The proposed model provides four scenarios of preamble detection i.e., Preamble detection probability, Miss-detection probability, False alarm probability and null. We achieved the target of 99% of Probability of Detection and less than 0.1% of False-alarm probability at certain SNR as specified according to 3gpp standard requirements when tested in Additive White Gaussian Noise (AWGN) channel and Extended Typical Urban (ETU) channel.

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations’ data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

Rotating machinery weak fault signal detection method based on QPSO and stochastic resonance

Due to the weak early vibration signal of rotating machinery, which is nonlinear and non-stationary, the traditional filtering method has some limitations. To this end, a weak signal detection method combining Quantum Particle Swarm Optimization (QPSO) and Stochastic Resonance (SR) is proposed. First, according to the adiabatic approximation theory, make the original signal meet the SR requirements, and optimize the structural parameters of the SR through QPSO; secondly, the signal-to-noise ratio is used as the objective function to make the input signal and the output signal achieve the best match; finally, simulation and experimental data are used to verify the feasibility of the proposed method. Comparative analysis shows that the proposed method increases the amplitude by 46% compared with the characteristic frequency obtained by the PSO optimized SR model.

Psychological behavior signal analysis for sports person during their activity session

The signal analysis helps us derive useful knowledge from biological processes to analyze, describe, and understand their origin mechanisms. However, biomedical signals are not immune and have time-consuming statistics. The major challenges of signal analysis of sportsperson are reliability and accuracy. Sports psychology uses psychological skills to discuss the optimum success and well-being of sports athletes, the developmental and social dimensions of the sport and sports facilities, and structural problems. The signal detection tool is used to detect the best combination of long-term practice predictors for active, sedentary adults’ signal. This paper proposed the wearable assisted signal detection method (WASDM) to find the sportspersons’ behavior signal analysis. This method performs an IoT based heart rate monitoring using a wearable device named intelligent bracelet mounted on the sportsperson to track the variations in his/her human heart rate. The wearable signal detector method analysis the heart rate abnormality and predicts health status, followed by an alarm to the physician and the respective personnel while performing activity session. In this research, various machine learning algorithms have been tried to perform signal analysis and prediction and compared their results to suggest the best in this application scenario. Finally, the experimental analysis shows better outcomes for the sportspersons’ psychological behavior signal analysis than the conventional methods.

A continuous phase-frequency modulation technique and adaptive pump noise cancellation method of continuous-wave signal over mud telemetry channel

Continuous wave telemetry is one of the critical downhole-to-surface transmission techniques of downhole information for logging while drilling (LWD). This paper aims to present a highly effective modulation technique for a continuous wave telemetry called Continuous Phase-Frequency Keying (CPFK) and an adaptive coherent detection method for the CPFK signal. The modulation equation of the CPFK signal is established according to the acceleration/deceleration time of the motor in a continuous wave generator, the coherent detection method is given for the demodulation of CPFK signal, and then the adaptive mud pump noise cancellation algorithm is proposed for identifying CPFK signals based on probability analysis and PSO (Particle Swarm Optimization) algorithm. The results of simulation analysis show that the proposed CPFK modulation mode is feasible and the adaptive mud pump noise cancellation algorithm can realize the demodulation and decoding of CPFK signals. It has been found from the analysis that pump noise is the main noise affecting the demodulation of CPFK signals, accurate removal of pump noise can effectively improve the frequency band utilization of CPFK signal and reduce the bit error probability of the adaptive mud pump noise cancellation algorithm.

Periodic non-sinusoidal time-delay stochastic resonance weak fault diagnosis method and its application

Stochastic resonance is the use of nonlinear systems to synchronize an original signal with noise. This method is commonly used to extract useful signals by reducing noise and has been widely used for mechanical weak fault diagnosis. This paper analyzes the characteristics of a periodic non-sinusoidal potential function, considers the shape of the model, and introduces a time-delay. The steady-state probability density function, effective potential function, and signal-to-noise ratio are then analyzed. As a result, a signal detection method for periodic non-sinusoidal time-delay stochastic resonance (PNTSR) is proposed. Experimental and engineering data are used to explain the PNTSR through the simulation. It is found that the PNTSR method has better fault detection results when compared to the classic bi-stable stochastic resonance method.