Signal Detection Method Research Articles

A Review of Statistical Methods for Safety Surveillance.

The data-mining statistical methods used for disproportionality analysis of drug-adverse event combinations from large drug safety databases such as the FDA's Adverse Event Reporting System (FAERS), consisting of spontaneous reports on adverse events for postmarket drugs, are called passive surveillance methods. However, the statistical signal detection methods for longitudinal data, as the data accrue in time, are called active surveillance methods. A review of the most commonly used passive surveillance statistical methods and the relationships among them is presented with unified notations. These methods are applied to the 2006-2012 FAERS data; the number of drug signals of disproportionate rates (SDRs) detected by each of these methods with the common SDRs from all of these methods, for the adverse event myocardial infarction, are given. Finally, there is a brief discussion on the recently developed active surveillance methods.

Under Sampling Stochastic Resonance for Detecting Weak Signal

For stochastic resonance can enhance the signal-to-noise ratio, it is widely applied to detect weak signal in a strong noise background. Aiming at the issue of the traditional stochastic resonance only applicable to deal with small parameters signals, a weak signal detection method based on under sampling stochastic resonance was proposed. Stochastic resonance was successfully expanded into the applications of the large parameters signals on the basis of scale-transformation and retrieve technology in the under sampling stochastic resonance. The improved stochastic resonance model was put forward and simulation results have proved the validity of the method. Large parameters weak signal mixed with strong noise is detected accurately. This method is effective for future application.

Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS)

ObjectiveTargeted drugs dramatically improve the treatment outcomes in cancer patients; however, these innovative drugs are often associated with unexpectedly high cardiovascular toxicity. Currently, cardiovascular safety represents both a challenging issue for drug developers, regulators, researchers, and clinicians and a concern for patients. While FDA drug labels have captured many of these events, spontaneous reporting systems are a main source for post-marketing drug safety surveillance in ‘real-world’ (outside of clinical trials) cancer patients. In this study, we present approaches to extracting, prioritizing, filtering, and confirming cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS). Data and methodsThe dataset includes records of 4,285,097 patients from FAERS. We first extracted drug–cardiovascular event (drug–CV) pairs from FAERS through named entity recognition and mapping processes. We then compared six ranking algorithms in prioritizing true positive signals among extracted pairs using known drug–CV pairs derived from FDA drug labels. We also developed three filtering algorithms to further improve precision. Finally, we manually validated extracted drug–CV pairs using 21 million published MEDLINE records. ResultsWe extracted a total of 11,173 drug–CV pairs from FAERS. We showed that ranking by frequency is significantly more effective than by the five standard signal detection methods (246% improvement in precision for top-ranked pairs). The filtering algorithm we developed further improved overall precision by 91.3%. By manual curation using literature evidence, we show that about 51.9% of the 617 drug–CV pairs that appeared in both FAERS and MEDLINE sentences are true positives. In addition, 80.6% of these positive pairs have not been captured by FDA drug labeling. ConclusionsThe unique drug–CV association dataset that we created based on FAERS could facilitate our understanding and prediction of cardiotoxic events associated with targeted cancer drugs.

Satellite signal detection method based on modification compression operator

The modification of compression operator on the basis of polynomial approximation with analytic connection has been proposed. The approximation of two discrete sequences between determinated bases with analytical relation has been reviewed. The characteristics of meander signal have been built with a help of classic and proposed methods

Research on Weak Signal Detection by Adding Noise to Cascaded Stochastic Resonance Step by Step

In view of the fact that after excitated by weak signal under background of noise the action of transition between potential wells of bistable system cannot occur or occasionally happen, this paper puts forward a method of weak signal detection by adding noise to cascaded stochastic resonance step by step. In this method the output signal of the former bistable system whose transition between potential wells occasionally occurs is converted into a local oscillation within an unilateral potential well step by step, then DC component is removed, a moderate intensity of Gaussian color noise is stacked and then by utilizing energy transfer functions of bistable system, gradually the signal is enabled to overcome the critical value of the latter bistable system and transition of frequency synchronization between potential wells is produced for signals to be tested. The simulation results of weak sinusoidal signal and periodic impact signals detected under the background of noise show the feasibility of this method.

Resonance method of geophysical signal detection by laser gravitational antennas

We consider the role of an additional mirror (recycling) in the design of long-base gravitational wave laser interferometers for the detection of weak infra-low-frequency geophysical signals by the resonance method

Performance of LTE ADVANCED Uplink in a Flat Rayleigh Channel

This paper describes the performance of LTE advanced uplink transmission in a flat Rayleigh channel. The uplink is simulated using a modified version of the Vienna uplink link level matlab code simulator. This modified version supports two transmission antennas instead of one. Moreover, it includes two extra processes; layer mapping and precoding. In addition, the demodulation reference signal is presented and employed to allow channel estimation. In this paper, the structure of the LTE advanced system is described. Furthermore, we present generation of the demodulation reference signal. Four combinations of two distinct channel estimation and two signal detection methods are used to provide the simulation results of performance evaluation in term of the BER and throughput curves for selected scenarios.

Design of Low-Noise and High-Density Signal Intelligent Detection System

Using motion state of the equipment transducer to determine the presence of a weak signal is a common method of signal detection, whose core is to determine the system's phase change. There a many traditional ways to judge phase transition, but most of which have computational complexity and need a large amount of data which make them difficult to apply engineering practices. In order to solve these problems, this paper presents a detection method based on Lyapunov exponent classification with a small amount of data. This approach has some advantages such as requiring fewer observed values, small calculation amount, and able to automatically determine the phase transition without subjective factors involved etc. Experiments show that this method has stable performance, high effectiveness, strong practicality and promotion.

Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications.

A Signal Detection Method for Harmonic Signal Submerged in Complex Noise Background

This paper presents a method to detect weak harmonic signal embedded in chaotic noise. Using different correlation characteristic of harmonic and chaotic signal ,we can transform the sample signal to a new data sequence which has new harmonic . The new harmonic frequency is m times of the original harmonic and beyond the center bandwidth of noise. Then use wavelet packet decomposition to analysis the energy distribution of harmonic and chaotic signals and extract the component which the harmonic energy concentrated on, In the end, a multiple signal classification (MUSIC) algorithm is employed to estimate harmonic frequencies . The method suit for the complex background noise (strong chaotic noise and gaussian noise).

Optical Detection of Buried Explosive Hazards

Visual detection of soil disturbances is an effective, but imperfect method for detecting buried explosive threats such as landmines and improvised explosive devices (IEDs). Building upon prior studies of optical detection, this study uses signal detection methods to measure observer sensitivity to images of soil disturbances asking “How detectable are disturbed soil signatures recorded in visible (VIS), short-wave infrared (SWIR), and thermal infrared (TIR) bands?” “How effective is each band for detection?” and “How is signature salience in each band effected by extended exposure in the natural environment?” Young adults viewed photos showing either soil disturbed by landmine burial or adjacent undisturbed surfaces and made yes/no decisions about the presence of a disturbance. Stimuli spanned a six-week time period over which VIS, SWIR, and TIR images were collected. Results show that (a) substantial signal strength lasts over the six-week period, (b) generally, SWIR and VIS show consistently strong performance for large anti-tank mines and (c) the soil signatures for the small, anti-personnel mine stay remarkably strong in SWIR. TIR sensitivity lags SWIR and VIS, but shows promising hit rates for anti-tank mine signatures under some conditions. Generally, results show that optical detection, particularly using the SWIR and VIS bands, shows promise for explosive hazards detection, at least under the conditions observed.

Detection of Network Intrusion Signal in Deep Camouflage Based on Chaotic Synchronization

For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

Design of Low-Noise, High-Density Signal Intelligent Detection System

Using motion state of the equipment transducer to determine the presence of a weak signal is a common method of signal detection, whose core is to determine the system's phase change. There a many traditional ways to judge phase transition, but most of which have computational complexity and need a large amount of data which make them difficult to apply engineering practices. In order to solve these problems, this paper presents a detection method based on Lyapunov exponent classification with a small amount of data. This approach has some advantages such as requiring fewer observed values, small calculation amount, and able to automatically determine the phase transition without subjective factors involved etc. Experiments show that this method has stable performance, high effectiveness, strong practicality and promotion.

Range resolution improvement by a reversed m-sequence phase coded lidar system

The traditional phase coded lidar systems require the collection of every returned laser pulse and are restricted in range resolution by sampling frequency and subpulse width. This paper proposes a reversed m-sequence acquisition method for signal detection. The detector accumulates N − 1 bits of the emitted laser sequence to deduce the Nth bit of the sequence. The indoor experiment achieved 2 μs resolution with the sampling period of 28 μs by employing a 15-bit m-sequence. This method achieves the acquisition of an m-sequence with narrow subpulse width whereas the sampling frequency is kept low. This paper also presents the phase coded imaging lidar simulation, which achieved 1.5 m range resolution with a 50 kHz sampling frequency by employing a 2047-bit m-sequence. The simulation showed an approach to implement phase coded imaging lidar in practical application.

Understanding Who Benefits at Each Step in an Internet-Based Diabetes Self-Management Program

Efforts to predict success in chronic disease management programs have been generally unsuccessful. To identify patient subgroups associated with success at each of 6 steps in a diabetes self-management (DSM) program. Using data from a randomized trial, recursive partitioning with signal detection analysis was used to identify subgroups associated with 6 sequential steps of program success: agreement to participate, completion of baseline, initial website engagement, 4-month behavior change, later engagement, and longer-term maintenance. The study was conducted in 5 primary care clinics within Kaiser Permanente Colorado. Different numbers of patients participated in each step, including 2076, 544, 270, 219, 127, and 89. All measures available were used to address success at each step. Intervention. Participants were randomized to receive either enhanced usual care or 1 of 2 Internet-based DSM programs: 1) self-administered, computer-assisted self-management and 2) the self-administered program with the addition of enhanced social support. Two sets of potential predictor variables and 6 dichotomous outcomes were created. Signal detection analysis differentiated successful and unsuccessful subgroups at all but the final step. Different patient subgroups were associated with success at these different steps. Demographic factors (education, ethnicity, income) were associated with initial participation but not with later steps, and the converse was true of health behavior variables. Analyses were limited to one setting, and the sample sizes for some of the steps were modest. Signal detection and recursive partitioning methods may be useful for identifying subgroups that are more or less successful at different steps of intervention and may aid in understanding variability in outcomes.

Multi component LFM signal detection and parameter estimation based on EEMD–FRFT

This paper studied multi component LFM signal detection and parameter estimation under the noise circumstance of various signal-to-noise ratios. Based on the analysis of fractional Fourier transform detection and parameter estimation on simple component LFM signal, this paper proposed the method of multi component LFM signal detection and parameter estimation based on EEMD–FRFT (Ensemble Empirical Mode Decomposition–Fractional Fourier transform), and this method was that with the EEMD algorithm, from the frequency domain decompose the analyzable signal to narrow-bandwidth components, whose center frequency changed from high to low, then accurately estimate the parameter and detect the signal of each component out of the pseudo-component with FrFT. This method solved the problem of mode aliasing of signal decomposition; meanwhile, the problem of detecting the multi component LFM signal would be simplified as the problem of one-dimensional search in small scope, which could reduce the amount of operation and improved the detection accuracy. A simulation computation for multi component LFM signal of various SNR (signal-to-noise ratios) was made and the result showed that the error of parameter estimation was less than 5% in the case of SNR not less than −10dB.

The Impact of Duration of Treatment on Reported Time-to-Onset in Spontaneous Reporting Systems for Pharmacovigilance

Within pharmacovigilance, knowledge of time-to-onset (time from start of drug administration to onset of reaction) is important in causality assessment of drugs and suspected adverse drug reactions (ADRs) and may indicate pharmacological mechanisms involved. It has been suggested that time-to-onset from individual case reports can be used for detection of safety signals. However, some ADRs only occur during treatment, while those that do occur later are less likely to be reported. The aim of this study was to investigate the impact of treatment duration on the reported time-to-onset. Case reports from the WHO Global ICSR database, VigiBase, up until February 5th 2010 were the basis of this study. To examine the effect of duration of treatment on reported time-to-onset, angioedema and hepatitis were selected to represent short and long latency ADRs, respectively. The reported time-to-onset for each of these ADRs was contrasted for a set of drugs expected to be used short- or long-term, respectively. The study included 2,980 unique reports for angioedema and 1,159 for hepatitis. Median reported time-to-onset for angioedema in short-term treatments ranged 0-1 days (median 0.5), for angioedema in long-term treatments 0-26 days (median 8), for hepatitis in short-term treatments 4-12 days (median 7.5) and for hepatitis in long term treatments 19-73 days (median 28). Short-term treatments presented significantly shorter reported time-to-onset than long-term treatments. Of note is that reported time-to-onset for angioedema for long-term treatments (median value of medians being 8 days) was very similar to that of hepatitis for short-term treatments (median value of medians equal 7.5 days). The expected duration of treatment needs to be considered in the interpretation of reported time-to-onset and should be accounted for in signal detection method development and case evaluation.

Is the Diminished Attentional Blink for Salient T2 Stimuli Driven by a Response Bias?

The attentional blink (AB) effect refers to the finding that the second of two quickly presented masked targets (T1 and T2) is less likely to be identified when the temporal lag between the targets is short than when it is long (Raymond, Shapiro, & Arnell, 1992). Interestingly, the AB effect is diminished for salient T2 stimuli. The common explanation is that salient stimuli are processed more efficiently than other stimuli. A contrasting hypothesis is that it is due to a bias to report such stimuli. Recently, Tibboel, Van Bockstaele, and De Houwer (2011) introduced a signal detection method that allowed them to reject the response bias explanation for the effect of arousing words on the AB. Presently, we used the same method to examine the diminished AB for participants’ own names (Experiment 1) and stimuli that form a coherent category (Experiment 2). Both experiments confirmed that the effect of these stimuli on the AB reflects more efficient processing rather than a response bias.

Dual-scale cascaded adaptive stochastic resonance for rotary machine health monitoring

Effective extraction of weak signals submerged in strong noise that are indicative of structural defects has remained a major challenge in fault diagnosis for rotary machines. Unlike traditional techniques that focus on noise filtering and reduction, stochastic resonance (SR) takes a noise-assisted approach to detecting weak signals. This paper presents a new adaptive method for weak signal detection, termed Dual-scale Cascaded Adaptive Stochastic Resonance (DuSCASR), which can quantify the frequency content of a weak signal without prior knowledge. Simulations and experiments have confirmed the effectiveness of the method in bearing fault diagnosis at the incipient stage, with high precision and robustness.

Choosing Thresholds for Statistical Signal Detection with the Proportional Reporting Ratio

Identification of potential drug safety problems using statistical screening algorithms in routinely collected databases of adverse drug reactions (ADRs) requires decision rules based on thresholds of the chosen parameters. Choosing higher or lower thresholds changes both the sensitivity of the screening and the number of false alarms produced, and thus has an impact on the effectiveness of the detection process. The aim of this study was to evaluate the impact on the effectiveness of signal detection activities of choosing different warning thresholds for the proportional reporting ratio (PRR) and for the count of reports of any drug-event combination. Signal detection methods were tested within the EudraVigilance database of suspected ADRs. Using an established set of known ADRs, the number that could be detected and the changes in time gained for earlier investigation of the signal were calculated over a range of signal detection thresholds. These figures were set against the number of false positive signals produced by the statistical signal detection algorithms. Higher thresholds for the lower confidence bound of the PRR produced fewer false positives but this benefit was offset by important losses of sensitivity in the detection of ADRs. By contrast, increases in the threshold for the count of a specific drug-event combination produced fewer false positives with little loss of either sensitivity or time gained for investigation of adverse events. A threshold of five compared with the current European Medicines Agency threshold of three gave a reduction of 25 % in false positive signals in return for a loss of 12 % in true signals detected early. Changes in the standard threshold for the count of drug-event combinations can result in a substantial improvement in efficiency of the signal detection process. Initially this change might be applied only to products with a well-established safety profile.