Analysis Of Signal Characteristics Research Articles

Epilepsy lesion localization method based on brain function network

ObjectiveIn the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning.MethodTo overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification.ResultsThe proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others.ConclusionOur method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

EMD-based noise reduction study of steel cored conveyor belt containing slag signal

This paper proposes a noise reduction technique that combines empirical mode decomposition (EMD) and wavelet thresholding to eliminate the noise interference caused by metal slag in the steel cord conveyor belt damage signal. Initially, the original signal undergoes decomposition by EMD into high- and low-frequency intrinsic mode function (IMF) components based on metal slag signal feature analysis. The wavelet threshold de-noising method is then applied to filter out the high-frequency noise while preserving the low-frequency IMF components. The IMF components containing slag noise are discarded, and the filtered signal and the low-frequency IMF components are used for signal reconstruction to obtain an optimal noise reduction effect. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier

Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Acoustic signal detection technology has significant advantages in detecting the leakage and bursting of heat exchange pipes in boilers. To address the current lack of sound signal data for steam medium leakages and the problem of large errors in the complex sound field environment of power plants, we performed an innovative experimental comparative analysis of leakage acoustic signals under a dual medium of compressed air and steam to provide a reference for detecting leakage acoustic signals. During the experiment, the time and frequency domains were analyzed by changing the pressure of the leakage pipeline and aperture of the leakage hole, and the spectrogram and power spectrum of the leakage acoustic signal were obtained using fast Fourier transform and autocorrelation analysis. The results showed that the signal value of the leakage medium increased with increasing pipeline pressure and leakage aperture and that the energy of the steam leakage acoustic signal was greater than that of compressed air under the same pressure and aperture because of its larger specific heat capacity. In addition, the complete empirical mode decomposition of adaptive noise (CEEMDAN) algorithm was introduced into the denoising decomposition of the leakage sound signal in the furnace, and the average error of the time delay value of the leakage acoustic signal calculated using the CEEMDAN algorithm was observed to be within 5%.

Application of EMD combined with wavelet algorithm for filtering slag noise in steel cord conveyor belt

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

ECG Multi-Emotion Recognition Based on Heart Rate Variability Signal Features Mining.

Heart rate variability (HRV) serves as a significant physiological measure that mirrors the regulatory capacity of the cardiac autonomic nervous system. It not only indicates the extent of the autonomic nervous system's influence on heart function but also unveils the connection between emotions and psychological disorders. Currently, in the field of emotion recognition using HRV, most methods focus on feature extraction through the comprehensive analysis of signal characteristics; however, these methods lack in-depth analysis of the local features in the HRV signal and cannot fully utilize the information of the HRV signal. Therefore, we propose the HRV Emotion Recognition (HER) method, utilizing the amplitude level quantization (ALQ) technique for feature extraction. First, we employ the emotion quantification analysis (EQA) technique to impartially assess the semantic resemblance of emotions within the domain of emotional arousal. Then, we use the ALQ method to extract rich local information features by analyzing the local information in each frequency range of the HRV signal. Finally, the extracted features are classified using a logistic regression (LR) classification algorithm, which can achieve efficient and accurate emotion recognition. According to the experiment findings, the approach surpasses existing techniques in emotion recognition accuracy, achieving an average accuracy rate of 84.3%. Therefore, the HER method proposed in this paper can effectively utilize the local features in HRV signals to achieve efficient and accurate emotion recognition. This will provide strong support for emotion research in psychology, medicine, and other fields.

Individual Feature Selection of Rolling Bearing Impedance Signals for Early Failure Detection

Condition monitoring of technical systems has increasing importance for the reduction of downtimes based on unplanned breakdowns. Rolling bearings are a central component of machines because they often support energy-transmitting elements like shafts and spur gears. Bearing damages lead to a high number of machine breakdowns; thus, observing these has the potential to reduce unplanned downtimes. The observation of bearings is challenging since their behavior in operation cannot be investigated directly. A common solution for this task is the measurement of vibration or component temperature, which is able to show an already occurred bearing damage. Measuring the electrical bearing impedance in situ has the ability to gather information about bearing revolution speed and bearing loads. Additionally, measuring the impedance allows for the detection and localization of damages in the bearing, as early research has shown. In this paper, the impedance signal of five fatigue tests is investigated using individual feature selection. Additionally, the feature behavior is analyzed and explained. It is shown that the three different bearing operational time phases can be distinguished via the analysis of impedance signal features. Furthermore, some of the features show a significant change in behavior prior to the occurrence of initial damages before the vibration signals of the test rig vary from a normal state.

Research on Liquid Flow Rate Detection of Mixed Fluid Based on Vibration Signal Characteristic Analysis of Gas Liquid Two-Phase Flow

The online detection of liquid flow rate in gas-liquid two-phase flow has become an important factor in ensuring the safe operation of gas wells. In this paper, the real-time measurement of liquid holdup in gas-liquid two-phase flow is carried out by analyzing the characteristics of vibration signals excited by gas-liquid two-phase flow impacting on the pipe wall. Firstly, an acceleration sensing detection and processing system is constructed to obtain the vibration signals excited by gas-liquid two-phase flow impacting on the pipe wall. Then, the pure airflow vibration signals at different flow velocities and the gas-liquid two-phase flow excitation vibration signals at different liquid flow rates are tested, respectively, and the time-frequency characteristics analysis based on STFT is implemented. The practice shows the following: firstly, the frequency band of 6.5−15 Hz is identified as the characteristic frequency band of liquid flow rate in gas-liquid two-phase flow. Secondly, the liquid holdup is positively correlated with the vibration energy in its characteristic frequency band. Thirdly, a mathematical model of the relationship between liquid flow rate and vibration energy is constructed. Finally, the measurement error of liquid holdup is within 10%. This research method has laid a good foundation for the subsequent detection of characteristic parameters of each phase in gas-solid-liquid complex multiphase flow fluids, and it has certain application and promotion value.

EMD-Based Energy Spectrum Entropy Distribution Signal Detection Methods for Marine Mammal Vocalizations

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.

Clustering analysis of acoustic emission signals in the monitoring of stone monuments: case of the freeze‒thaw deterioration of tuffs

Acoustic emission (AE) technology is a promising technique for monitoring cultural monuments due to its characteristic ability to reflect status changes and perceive the development process of deterioration and damage even before their visual appearance. This study was established on the motivation of providing basic data and a methodology that can improve the signal processing, characteristics analysis and classification for the AE technique in the long-term in-situ monitoring of deterioration processes, starting from the freeze‒thaw deterioration of tuff monuments at the Chengde site. AE monitoring was carried out with an indoor freeze–thaw deterioration experiment. As a result, a set of procedures and related methodology is proposed based on the hit-based AE waveform parameters for denoising and classification of monitored AE signals by applying hierarchical cluster analysis, k-means clustering, distribution statistics, etc. The clustering results show that some signals may indicate deterioration and signals with certain characteristics are more likely to occur at a particular deterioration phase. Signals characterized by the significant absolute energy (ABE) are presumed to be related to the propagation of cracks to the outer layer. Signals characterized by a higher indirect parameter RA (Rise time divided by peak amplitude) value may connect with the opening/closing of microcracks in the earlier phase of deterioration prior to the exposure of visible surface cracks. The peak frequency (PF) is likely to decrease as the deterioration proceeds.

Nonlinear dynamic characteristics of suction-vortex-induced pressure fluctuations based on chaos theory for a water jet pump unit

To improve the effective use of ocean energy by water jet propulsion pump, it is necessary to study the underwater suction vortex (USV), which affects the propulsion and stealth performance of water jet propulsion pump. This paper analyses the time-frequency and nonlinear dynamic characteristics of the pressure fluctuation induced by USV through high-speed photography and high-frequency pressure fluctuation tests. The nonlinear characteristics of pressure fluctuations under the conditions with and without USV are compared and analyzed based on the chaotic time series analysis theory. The results show that the chaotic characteristics of the pressure fluctuation signal are unaffected by the occurrence of USV. However, the singular attractor structure of the pressure fluctuation signals induced by USV is highly complex, and the largest Lyapunov exponent increases. Therefore, the pressure fluctuation signals induced by USV have stronger chaotic characteristics. Note that the saturation correlation dimension of the pressure fluctuation signal in the period without USV is larger than that with USV, mainly because the influence factor of the pressure fluctuation signal in the USV period is relatively single, which is primarily induced by USV. The nonlinear characteristics analysis of the pressure fluctuation signal can effectively identify the presence of USV.

SEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training.

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field.

Pulse Signal Analysis Based on Deep Learning Network.

Pulse signal is one of the most important physiological features of human body, which is caused by the cyclical contraction and diastole. It has great research value and broad application prospect in the detection of physiological parameters, the development of medical equipment, and the study of cardiovascular diseases and pulse diagnosis objective. In recent years, with the development of the sensor, measuring and saving of pulse signal has become very convenient. Now the pulse signal feature analysis is a hotspot and difficulty in the signal processing field. Therefore, to realize pulse signal automatic analysis and recognition is vital significance in the aspects of the noninvasive diagnosis and remote monitoring, etc. In this article, we combined the pulse signal feature extraction in time and frequency domain and convolution neural network to analyze the pulse signal. Firstly, a theory of wavelet transform and the ensemble empirical mode decomposition (EEMD) which is gradually developed in recent years have been used to remove the noises in the pulse signal. Moreover, a method of feature point detection based on differential threshold method is proposed which realized the accurate positioning and extraction time-domain values. Finally, a deep learning method based on one-dimensional CNN has been utilized to make the classification of multiple pulse signals in the article. In conclusion, a deep learning method is proposed for the pulse signal classification combined with the feature extraction in time and frequency domain in this article.

PySio: A New Python Toolbox for Physiological Signal Visualization and Feature Analysis.

In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing and feature analysis, Python has recently dethroned MATLAB with the rise of data science, machine learning and artificial intelligence. Hence, there is a compelling need for a Python package for physiological feature analysis and extraction to achieve compatibility with downstream models often trained in Python. Thus, we present a novel visualization and feature analysis Python toolbox, PySio, to enable rapid, efficient and user-friendly analysis of physiological signals. First, the user should import the signal-of-interest with the corresponding sampling rate. After importing, the user can either analyze the signal as it is, or can choose a specific region for more detailed analysis. PySio enables the user to (i) visualize and analyze the physiological signals (or user-selected segments of the signals) in time domain, (ii) study the signals (or user-selected segments of the signals) in frequency domain through discrete Fourier transform and spectrogram representations, and (iii) investigate and extract the most common time (energy, entropy, zero crossing rate and peaks) and frequency (spectral entropy, rolloff, centroid, spread, peaks and bandpower) domain features, all with one click. Clinical relevance- As the physiological signals originate directly from the underlying physiological events, proper analysis of the signal patterns can provide valuable information in personalized treatment and wearable technology applications.

Feasibility of video/audio monitoring in the analysis of motion and treatment effects on night-time seizures – Interventional study

The aim of the studyThis pilot study assessed the ability of a video/audio-based seizure monitoring system to evaluate (I) baseline frequency and severity of nocturnal seizures with motor features in patients with drug-resistant epilepsy (DRE) and (II) the individual effect of brivaracetam (BRV) treatment on number, duration and movement intensity of these seizure types. Algorithmic feature analysis was developed for assessment of qualitative changes in movement intensity measurements within seizure types before and after BRV intervention. Materials and methodsNight-time motor seizures of recruited patients were recorded in two separate four-week monitoring periods. The first period defined a prescreening phase (n = 13 patients) to establish a baseline, and the second period defined the intervention phase (n = 9 patients), with BRV initiated during the second week of the second monitoring period. All recorded nights were analyzed by an expert video reviewer, and all unequivocal seizures were classified by an epileptologist.Seizure frequencies using both seizure diaries and video monitoring were compared.The effect of BRV on both seizure duration and movement intensity was assessed by numerical comparison of visual features calculated from motion characteristics of the video, as well as spectral features from the recorded audio. The statistical significance of changes in seizure duration and intensity before and after the intervention were investigated by Wilcoxon rank-sum test and visual inspection of Kernel density estimation. Results8 patients marked seizures in their seizure diaries during the prescreening phase. During the three-week follow-up, three patients achieved > 50% seizure decrease, four patients did not respond to treatment, and two patients experienced worsening of seizures. Five patients were able to document 40–70% of their seizures compared to the video/audio monitoring system. According to the signal feature analysis the intervention decreased movement intensity with clear clinical significance in three patients, whereas statistically significant differences in features appeared in 8 out of 9 patients. ConclusionsThe novel video/audio monitoring system improved the evaluation of treatment effect compared to the seizure diaries and succeeded in providing a comparative intra-patient assessment of the movement intensity and duration of the recorded seizures.

Current signal characteristics analysis of transmission system in high-speed train under abnormal vibration conditions

Abnormal vibration monitoring of high-speed trains is usually performed by analysing the vibration acceleration of specific structures to identify characteristic signals. However, due to the harsh operating environment caused by multiple internal and external sources of compound excitation, vibration monitoring of high-speed trains faces numerous problems, such as easy damage to vibration sensors, limited installation space for testing equipment, and difficulty in vibration testing of rotating parts. This paper takes high-speed train transmission system as the research object, combines multi-body system dynamics and electric transmission control theory to establish an electromechanical model of high-speed train transmission system, and carries out simulation research for three typical working conditions of high-speed train, which are wheel flat, wheel polygon and hunting motion. By analysing the transmission response of wheel–track relationship, gearing and motor current under three operating conditions based on Motor Current Signal Analysis (MCSA), it can be concluded that the motor current signal can be used instead of the vibration signal to identify the above three abnormal operating conditions by analysing the characteristic frequencies in the motor stator current. While MCSA is commonly used to diagnose faults such as tooth surface pitting and tooth root cracking inside gearboxes, the results of this paper show that motor current signals can also be used to monitor any abnormal operating conditions of high-speed trains that can cause changes in motor load torque.

Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis

In the process of reconstruction and expansion of gas pipeline, it is easy to destroy in-service gas pipeline and cause safety accidents. In order to realize the detection of in-service pipelines, based on the characteristics of low sound pressure level and easy attenuation of acoustic signals of gas pipelines, the detection and identification method of gas pipelines based on acoustic signal feature analysis was studied by using Hilebert–Huang transform algorithm and optimized Back Propagation (BP) neural network. This method takes the gas pipeline flow noise signals obtained by numerical simulation and experimental verification as the research object, and the underwater acoustic signals are collected for comparative analysis. Empirical Mode Decomposition (EMD) was used to decompose the two signals, and the time-domain waveform of Intrinsic Mode Functions (IMF) component was obtained, and the characteristic parameters of peak value and peak frequency were determined. The energy characteristic parameters of Hilbert marginal spectrum were calculated, and the characteristic database of gas pipeline flow noise signal was obtained. The optimized BP neural network was used for pattern recognition. The results show that the identification rate of gas pipeline acoustic signal can reach 97.5% by using this method, which verifies the effectiveness of the gas pipeline detection and identification method in this paper.

Study on insulation defect discharge features of dry-type reactor based on audible acoustic

The audible signal (20 Hz to 20 kHz) generated during the operation of a dry-type reactor is one of the key features to evaluate its operation state. However, at present, the time/frequency domain features of the audible signal under typical insulation defects are not clear, which makes it difficult to effectively carry out the fault diagnosis of the dry-type reactor based on audible signals. Based on this, the audible signal features under typical insulation defect discharge of the dry-type reactor are studied in this paper. Three typical insulation defect discharge models (metal protrusion defect, pollution defect, and metal foreign bodies defect) are simulated; then, the discharge acoustic signals are collected under the voltage of 8 kV. Analysis of acoustic signal features shows that the acoustic signal of metal protrusion defect discharge occurs in the positive half cycle of power frequency with the cycle of 20 ms. Both pollution defect discharge and metal foreign bodies defect discharge show intermittent discharge; during the discharge, the discharge acoustic signal of pollution defect has features similar to that of metal protrusion defect, while the discharge of metal foreign bodies defect shows randomness with a larger amplitude of discharge acoustic signal (up to 110 dB). Based on the frequency domain feature vectors’ extraction, it is found that the odd harmonic component of the metal protrusion defect discharge acoustic signal accounts for more than twice the even-order harmonic, and the high-frequency energy (12.5–25 kHz) is the main component. The discharge acoustic signal of the metal foreign bodies defect is mainly low-frequency energy, and the high-frequency energy proportion of pollution defect discharge acoustic signal is similar to that of low-frequency energy. In addition, the variance of high-frequency energy ratio (Rhf) of the metal foreign bodies defect discharge acoustic signal is 5.25 and 21 times that of the metal protrusion defect and pollution defect. This study provides some technical support for further detection and assessment of the operation state of the dry-type reactor based on audible signals.

Computerized Analysis of Antepartum Cardiotocography: A Review

Abstract Cardiotocography measures the human fetal heart rate and uterine activity using ultrasound. While it has been a mainstay in antepartum care since the 1960s, cardiotocograms consist of complex signals that have proven difficult for clinicians to interpret accurately and as such clinical inference is often difficult and unreliable. Previous attempts at codifying approaches to analyzing the features within these signals have failed to demonstrate reliability or gain sufficient traction. Since the early 1990s, the Dawes-Redman system of automated computer analysis of cardiotocography signals has enabled robust analysis of cardiotocographic signal features, employing empirically-derived criteria for assessing fetal wellbeing in the antepartum. Over the past 30 years, the Dawes-Redman system has been iteratively updated, now incorporating analyses from over 100,000 pregnancies. In this review, we examine the history of cardiotocography, signal processing methodologies and feature identification, the development of the Dawes-Redman system, and its clinical applications.

Gear typical fault modeling and fault signal characteristics analysis

Gear typical fault modeling and fault signal characteristics analysis