Signal Detection Method Research Articles

An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis.

R-wave detection is a prerequisite for the extraction and recognition of ECG signal feature parameters. In the analysis and diagnosis of exercise electrocardiograms, accurate and real-time detection of QRS complexes is very important for the prevention and monitoring of heart disease. This paper proposes a lightweight R-wave real-time detection method for exercise ECG signals. After real-time denoising of the exercise ECG signal, the median line is used to correct the baseline, and the first-order difference processing is performed on the differential square signal. Max-Min Threshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal. The abovementioned method was verified by using the measured data in the MIT-BIH ECG database of the Massachusetts Institute of Technology and the exercise plate experiment. The R-wave detection rates were 99.93% and 99.98%, respectively. Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring.

The TVICMs method for weak signal detection based on a nonlinear stochastic delay differential system

A nonlinear stochastic delay differential system with symmetry SD oscillator is investigated to detect weak signal. Firstly, the stochastic Melnikov function and the chaos threshold are obtained by using the stochastic mean square criterion. Secondly, the stochastic center manifold and the stochastic averaging method are used to obtain the one-dimensional Itoˆ equation and the corresponding FPK equation. The stationary probability density of the FPK equation is studied and the corresponding nonlinear stochastic dynamical behaviors are analyzed. Finally, the highlight of this paper is to detect the weak signal with ‘transient vacancy in chaotic motions’ (TVICMs) method successfully and extract the features information such as frequency and amplitude on the premise of unknown features information. Astonishingly, numerical simulation shows that the time delay can enhance the detection capability of the system and the effectiveness of the new method.

Human-autonomous devices for weak signal detection method based on multimedia chaos theory

The detection of weak signals has been widely used in communication, radar and other fields. The detection of weak signals in the background of strong noise is an important research hotspot of modern information theory, and prompts people to explore and study new theories and new methods of weak signal detection. The device takes Holmes-type Duffing mapping as the research object, and uses the Lyapunov exponent as the criterion for chaos identification. The chaotic critical value of the equation is changed from chaotic state to periodic state. Whether it contains the detection algorithm of the target signal, the signal detection for the unknown frequency and the Holmes-type Duffing system is improved by the sliding mode variable structure control method in the control theory. The simulation results show that the improved chaotic Duffing system can effectively suppress the noise and detect the frequency of the weak signal through the power spectrum of the system.

Electrochemical assay for analysis of circulation tumor cells based on isolation of thecell with magnetic nanoparticles and reaction of DNA with molybdate.

A universal strategy was developed for the analysis of circulating tumor cells (CTCs) based on reaction of DNA in the cells with molybdate. Initially, CTCs were enriched and isolated from samples by magnetic nanoparticles. Then, after killing the isolated cells by heat treatment, the cell membrane was raptured, and the DNA molecules contained in the cells were released. The following reaction of the released DNA molecules with molybdate can form redox molybdophosphate, resulting in electrochemical current. This electrochemical assay can be applied to thedetection of different CTCs as long as the CTCs can be isolated from thesamples, with a universal signal detection method, without additional signal amplification strategies. Breast cancer cell MCF-7 was chosen as a model CTC for thisstudy. At aworking potential of 0.2V vs. Ag/AgCl electrode, the electrochemical current is linearly related to theMCF-7 cell concentration from 5 to 1000 cellsmL-1 witha limit of detection of 2 cellsmL-1. The assay was successfully appliedfor detection of MCF-7 in human blood samples. This electrochemical assay can be applied for detection of different CTCs and alsofor simultaneous detection of CTCs. Graphical abstract A universal strategy was developed for the analysis of circulating tumor cells (CTCs) based on reaction of DNA contained in the cells with molybdate.

A Novel Detection Method for Weak Harmonic Signal with Chaotic Noise

A Novel Detection Method for Weak Harmonic Signal with Chaotic Noise

Evaluating medical device adverse event signals using a likelihood ratio test method

ABSTRACT Signal detection methods have been used extensively in post-market surveillance to identify elevated risks of adverse events. However, these statistical methods have not been widely used in detecting AE signals for medical devices. In this paper, we focused on the use of a likelihood ratio test (LRT)-based method in identifying adverse event (AE) signals associated with left ventricular assist devices (LVADs) using Medical Device Reporting (MDR) data. Among 110,927 adverse event entries identified in MDR data for LVADs, the LRT method detected 18 AE signals which included seven bleeding-related AEs such as hemolysis, thrombosis, hematuria, thrombus, blood loss, and hemorrhage. The LRT method was also applied to longitudinal data from 2007 to 2019 where a monotone alpha-spending function was used to ensure the control of type I error at each look and overall for trend analysis. Furthermore, the LRT method was compared to proportional reporting ratios (PRRs), Bayesian confidence propagation neural network (BCPNN), and simplified Bayes methods and found to be the most conservative method when examining the total number of detected signals, given its ability to control type-I error and the false discovery rate.

Application of a likelihood ratio test based method for safety signal detection to left ventricular assist devices

ABSTRACT Effective post-market safety surveillance of medical devices is critical for public health. However, many current statistical methods for safety signal detection do not control for type I error when assessing multiple device and adverse event (AE) combinations. This can result in increased false signals, underscoring the need for more robust statistical methods. Moreover, the duration of medical device use can be an important factor to consider in safety surveillance. In this study, we adapted a likelihood ratio test (LRT) based method, which was initially developed and applied to drugs, to identify safety signals for left ventricular assist devices (LVAD). Among patients with chronic, advanced left ventricular failure, we analyzed AE data for HeartWare and HeartMate II patients during a two-year period and further incorporated person-years (henceforth exposure-time). The novel modified LRT and conventional Z-test with p-values adjusted by the Benjamini-Hochberg (BH) procedure were used to explore safety signals by comparing HeartWare and HeartMate II patients in the presence of multiple adverse events. Both methods identified greater incidence of stroke among HeartWare as compared to HeartMate II patients without exposure-time (p = .025 for LRT and p = .027 for Z-test with BH) and with exposure-time (p = .002 for LRT and p = .005 for Z-test with BH). By using improved statistical methods for safety signal detection, potential safety issues can be identified and addressed in a more timely manner to enhance public safety.

RTS Noise Detection and Voltage Effect on RTS in HgCdTe Focal-Plane Arrays

An automated random telegraph signal (RTS) detection and characterization method and a blinking noise and slow drift separation method adapted to focal-plane arrays have been developed and applied to study the evolution of the number of RTS pixels and the amplitude of the blinking signal as functions of the reverse bias voltage and temperature. The results show that the physical characteristics of the RTS follow Arrhenius laws and increase with bias. The origin of this increase in the amplitude as a function of the reverse bias is discussed.

Detecting Medicine Safety Signals Using Prescription Sequence Symmetry Analysis of a National Prescribing Data Set.

Medicine safety signal detection methods employed by the medicine regulator in Australia (Therapeutic Goods Administration [TGA], Department of Health) rely predominantly on analysis of spontaneous adverse event (AE) reports, sponsor notifications or information shared by international agencies. The limitations of these methods and the availability of large administrative health data sets has given rise to greater interest in the use of administrative health data to support pharmacovigilance (PV). We explored whether prescription sequence symmetry analysis (PSSA) of Pharmaceutical Benefits Scheme (PBS) data can enhance signal detection by the TGA, using the AE, heart failure (HF) as a case study. We applied the PSSA method to all single-ingredient medicines dispensed under the PBS between 2012 and 2016, using furosemide initiation as a proxy for new-onset HF. A signal was considered present if the lower limit of the 95% confidence interval for the adjusted sequence ratio was > 1. We excluded medicines known to cause HF, indicated for HF treatment or indicated for diseases that may contribute to HF. Of the 654 tested medicines, 26 potential new HF signals were detected by PSSA. Five signals had additional support for the possible association provided by biological plausibility, consistency and disproportionate reporting of cases of HF to the TGA and the World Health Organization; and clinical impact. PSSA was able to identify potential signals for further evaluation. With the increasing availability of different administrative health data sources, the strengths and weaknesses of methods used to analyse these data for the purpose of regulatory PV should be evaluated.

Iterative Signal Detection for Diffusion-Based Molecular Communications

Inter-symbol interference (ISI) due to slow-moving molecules is one of the key limitations affecting throughput of a diffusion based molecular communication (MC). In this paper, we propose novel ISI mitigation approaches for diffusion based MC systems. The first approach uses weighted sum (WS) to enhance the received signal-to-ISI plus noise ratio (SINR) by exploiting the apriori characteristics of the diffusion channel impulse response. In the second approach, an iterative method estimates the ISI in a frame using the block-wise data symbol detection and subtracts this estimated ISI before the signal detection. Both the proposed methods improve the MC system’s performance as compared to the conventional signal detection method. Further, we have analyzed the combined WS and iterative method, resulting in further improvement in the performance. Furthermore, the effect of various system parameters such as frame duration, diffusion coefficient, and the distance between transmitter and receiver is analyzed.

ФАЗОЧАСТОТНОЕ ОБНАРУЖЕНИЕ СЕЙСМИЧЕСКИХ СИГНАЛОВ

Актуальность. В настоящее время при поиске нефтяных и газовых месторождений большое внимание уделяется более активному вовлечению в разведочный процесс слабо исследованных территорий, а также доисследованию природных резервуаров в районах промышленного освоения месторождений. Для этих целей широко применяются методы сейсморазведки, в результате чего формируются огромные массивы данных, подлежащие последующей обработке и интерпретации. Одной из задач, решаемой на этапе обработки, является надежное обнаружение сейсмических сигналов, особенно важное значение эта задача имеет при прослеживании волн, так как от достоверности обнаружения зависит качество построения отражающих границ. Поэтому построение эффективных методов и алгоритмов обнаружения сигналов на основе анализа ФЧХ, обладающих более высокой степенью устойчивости к помехам, является весьма актуальной задачей. Цель: на основе оптимальной и субоптимальной обработки ФЧХ сейсмических волн, получаемых при поиске нефтяных и газовых месторождений, построить методы обнаружения сигналов и исследовать их эффективность на моделях сейсмоимпульсов путем вычисления вероятностных характеристик ошибок обнаружения. Методы и средства исследования: методы статистического анализа и синтеза алгоритмов, теория случайных процессов, дискретное преобразование Фурье, методы обработки и интерпретации сейсмических данных, математическое моделирование и вычислительный эксперимент. Результаты. На основе метода максимального правдоподобия построена оптимальная процедура обнаружения сейсмических сигналов по их фазочастотным характеристикам. Получены аналитические выражения для вероятностей ошибок обнаружения. Показано, что оптимальное фазочастотное обнаружение обеспечивает результаты, близкие к результатам абсолютно оптимального метода обнаружения сигналов. Для практической реализации разработанного метода предложена субоптимальная обработка ФЧХ (равновесная обработка), не требующая информации о распределении энергии сигнала в частотной области. Проведены исследования помехоустойчивости метода обнаружения с равновесной обработкой, и получены оценки потерь в зависимости от числа использованных частотных компонент в спектре сигнала. Показано место применения разработанных методов обнаружения при решении задач прослеживания сейсмических волн.

Automated detection and classification of birdsong: An ensemble approach

Abstract The avian dawn chorus presents a challenging opportunity to test autonomous recording units (ARUs) and associated recogniser software in the types of complex acoustic environments frequently encountered in the natural world. To date, extracting information from acoustic surveys using readily-available signal recognition tools (‘recognisers’) for use in biodiversity surveys has met with limited success. Combining signal detection methods used by different recognisers could improve performance, but this approach remains untested. Here, we evaluate the ability of four commonly used and commercially- or freely-available individual recognisers to detect species, focusing on five woodland birds with widely-differing song-types. We combined the likelihood scores (of a vocalisation originating from a target species) assigned to detections made by the four recognisers to devise an ensemble approach to detecting and classifying birdsong. We then assessed the relative performance of individual recognisers and that of the ensemble models. The ensemble models out-performed the individual recognisers across all five song-types, whilst also minimising false positive error rates for all species tested. Moreover, during acoustically complex dawn choruses, with many species singing in parallel, our ensemble approach resulted in detection of 74% of singing events, on average, across the five song-types, compared to 59% when averaged across the recognisers in isolation; a marked improvement. We suggest that this ensemble approach, used with suitably trained individual recognisers, has the potential to finally open up the use of ARUs as a means of automatically detecting the occurrence of target species and identifying patterns in singing activity over time in challenging acoustic environments.

Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.

A model of co-saliency based audio attention

Inspired by biological perceptual characteristics in human auditory systems and the mechanisms of saliency detection, we study the relevance constraint between time-frequency characteristics of sound signals and the multiple spectrogram and propose a co-saliency detection method for multiple sound signals in this paper. Then, according to the auditory characteristics of the human ear, the distinctive saliency features from the acoustic channel and the image channel are fused. Finally, an auditory saliency map is obtained to complete the detection of significant sounds. The saliency features of the acoustic channel include the features calculated in the in the temporal and spectral domains of signal, which the temporal saliency features could be represented by the local maximum points in the Power Spectral Density (PSD) curve, and the spectral features could be represented by local maximum points in Mel Frequency Cepstrum Coefficient (MFCC) curve of sound signal. The saliency features of acoustic channel and cross-scale fusion with the contrast cue of spectrogram, whose result is more in line with the human auditory attention mechanism. Finally, combined with the corresponding cue which could reflect the distribution between multiple spectrograms, it could reflect the characteristics of global repeatability, and reflect high frequency of occurrence. Experimentally, the auditory Co-Saliency map verifies the accuracy and robustness of proposed method in this paper. It shows that the proposed method is superior to other traditional detection methods for auditory saliency, and can implement intelligent automatic detection to sound signals.

Implementation and comparison of two text mining methods with a standard pharmacovigilance method for signal detection of medication errors

BackgroundMedication errors have been identified as the most common preventable cause of adverse events. The lack of granularity in medication error terminology has led pharmacovigilance experts to rely on information in individual case safety reports’ (ICSRs) codes and narratives for signal detection, which is both time consuming and labour intensive. Thus, there is a need for complementary methods for the detection of medication errors from ICSRs. The aim of this study is to evaluate the utility of two natural language processing text mining methods as complementary tools to the traditional approach followed by pharmacovigilance experts for medication error signal detection.MethodsThe safety surveillance advisor (SSA) method, I2E text mining and University of Copenhagen Center for Protein Research (CPR) text mining, were evaluated for their ability to extract cases containing a type of medication error where patients extracted insulin from a prefilled pen or cartridge by a syringe. A total of 154,209 ICSRs were retrieved from Novo Nordisk’s safety database from January 1987 to February 2018. Each method was evaluated by recall (sensitivity) and precision (positive predictive value).ResultsWe manually annotated 2533 ICSRs to investigate whether these contained the sought medication error. All these ICSRs were then analysed using the three methods. The recall was 90.4, 88.1 and 78.5% for the CPR text mining, the SSA method and the I2E text mining, respectively. Precision was low for all three methods ranging from 3.4% for the SSA method to 1.9 and 1.6% for the CPR and I2E text mining methods, respectively.ConclusionsText mining methods can, with advantage, be used for the detection of complex signals relying on information found in unstructured text (e.g., ICSR narratives) as standardised and both less labour-intensive and time-consuming methods compared to traditional pharmacovigilance methods. The employment of text mining in pharmacovigilance need not be limited to the surveillance of potential medication errors but can be used for the ongoing regulatory requests, e.g., obligations in risk management plans and may thus be utilised broadly for signal detection and ongoing surveillance activities.

Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network.

The symmetric positive definite (SPD) matrix has attracted much attention in classification problems because of its remarkable performance, which is due to the underlying structure of the Riemannian manifold with non-negative curvature as well as the use of non-linear geometric metrics, which have a stronger ability to distinguish SPD matrices and reduce information loss compared to the Euclidean metric. In this paper, we propose a spectral-based SPD matrix signal detection method with deep learning that uses time-frequency spectra to construct SPD matrices and then exploits a deep SPD matrix learning network to detect the target signal. Using this approach, the signal detection problem is transformed into a binary classification problem on a manifold to judge whether the input sample has target signal or not. Two matrix models are applied, namely, an SPD matrix based on spectral covariance and an SPD matrix based on spectral transformation. A simulated-signal dataset and a semi-physical simulated-signal dataset are used to demonstrate that the spectral-based SPD matrix signal detection method with deep learning has a gain of 1.7–3.3 dB under appropriate conditions. The results show that our proposed method achieves better detection performances than its state-of-the-art spectral counterparts that use convolutional neural networks.

Variable scale-convex-peak method for weak signal detection

A variable scale-convex-peak method is constructed to identify the frequency of weak harmonic signal. The key of this method is to find a set of optimal identification coefficients to make the transition of dynamic behavior topologically persistent. By the stochastic Melnikov method, the lower bound of the chaotic threshold continuous function is obtained in the mean-square sense. The intermediate value theorem is applied to detect the optimal identification coefficients. For the designated identification system, there is a valuable co-frequency-convex-peak in bifurcation diagram, which indicates the state transition of chaos-period-chaos. With the change of the weak signal amplitude and external noise intensity in a certain range, the convex peak phenomenon is still maintained, which leads to the identification of frequency. Furthermore, the proposition of the existence of reversible scaling transformation is introduced to detect the frequency of the harmonic signal in engineering. The feasibility of constructing the hardware and software platforms of the variable scale-convex-peak method is verified by the experimental results of circuit design and the results of early fault diagnosis of actual bearings, respectively.

A signal detection method based on matrix information geometric dimensionality reduction

Traditional signal detection methods have achieved satisfactory performance in many contexts of signal processing. However, these methods based on mathematical statistics show a drawback in dealing with the low SNR cases, which limits their practicability. To this end, inspired by image processing techniques, we first make use of the short time Fourier transform to generate sufficient 2-D spectrograms of the received data. Then we extract high dimensional features of these spectrogram to construct high dimensional covariance matrices, transforming into a binary classification problem lying on a symmetric positive definite (SPD) manifold. In addition, by reducing the dimensionality directly on the SPD manifold, these spectrograms are mapped into a more discriminative SPD manifold, which improves the separability between the two classes. The simulation experiment results demonstrate that our method achieve satisfactory signal detection performance in the task of signal detection under K distribution data, even in the case of SNR = -10.

Strict parity symmetric prolate spheroidal wave functions signal construction and low complexity detection method

Aiming at solving the problem of high signal processing and detection complexity of prolate spheroidal wave functions (PSWFs), we propose a time domain strict parity symmetry PSWFs signal construction method, based on the characteristics of PSWFs with the same parity symmetry after linear operation. According to the characteristics that the PSWFs of the same parity symmetry have the same orthogonality in the half-symbol period and the whole symbol period, we propose a PSWFs signal detection method based on the parity characteristics, which groups processing of odd symmetric and even symmetric signals and detects PSWFs signal in the half-symbol period signal to reduce the number of signal solutions for participating in the operation. Compared with the coherent detection method, theoretical and numerical analyses show that the proposed signal detection method can reduce the computational complexity from $O(NQ)$ to $O(NQ/2)$ (i.e., about 50%) without reducing the system error performance.

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

The non-contact detection of buried ferromagnetic pipeline is a long-standing problem in the field of inspection of outside pipelines, and the extraction of magnetic anomaly signal is a prerequisite for accurate detection. Pipeline defects can cause the fluctuation of magnetic signals, which are easily submerged in wide-band background noise without external excitation sources. Previously, Variational Mode Decomposition (VMD) was used to separate modal components; however, VMD is based on narrow-band signal processing algorithm and the calculation is complex. In this article, a method of pipeline defect signal based on Variational Specific Mode Extraction (VSME) is employed to extract the signal of a specific central frequency by signal modal decomposition, i.e., the specific mode is weak magnetic anomaly signal of pipeline defects. VSME is based on the fact that a wide-band signal can be converted into a narrow-band signal by demodulation method. Furthermore, the problem of wide-band signal decomposition is expressed as an optimal demodulation problem, which can be solved by alternating direction method of multipliers. The proposed algorithm is verified by artificially synthesized signals, and its performance is better than that of VMD. The results showed that the VSME method can extract the magnetic anomaly signal of pipeline damage using experimental data, while obtaining a better accuracy.