Average Signal Research Articles

Association between metformin use with circumpapillary retinal nerve fibre layer thickness and capillary vessel density in glaucoma

Background/aimsTo investigate the association between use of metformin and circumpapillary retinal nerve fibre layer (cpRNFL) thickness, as well as whole image capillary density (wiCD), in patients with glaucoma.MethodsThis cross-sectional study...

A nano-imprinted graphene oxide-cellulose composite as a SERS active substrate.

Cellulose is a sustainable material capable of forming optically active nanoarrays on its surface. We created a composite of cellulose acetate (CA) and graphene oxide (GO), by mixing GO (0.1 mg mL-1) into CA. This was then imprinted with nanoscale surface features that form Bragg-like modes in resonance with the excitation laser when a thin layer of silver is vapor deposited onto the surface of the substrate. The addition of GO leads to improved surface-enhanced Raman scattering (SERS) signal strengths, obtaining an average SERS signal increase of 1.4-fold following the inclusion of GO. The combination of photonic and electromagnetic effects with charge transfer-based processes that support the SERS chemical mechanism and the possible presence of electromagnetic hot spots from the roughened surface results in an enhanced SERS signal strength when GO is added. This work shows the potential for nanoimprinted graphene oxide/cellulose acetate composites as flexible sensor platforms to detect target molecules.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Patients with Traumatic Brain Injury.

Resting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI). We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in patients with TBI. This is a retrospective study of comatose patients with TBI who were admitted to a trauma center (October 2013 to January 2022). Demographics, basic clinical data, imaging characteristics, and EEGs were collected. We calculated the following using 10-min rsEEGs: power spectral density, permutation entropy (complexity measure), weighted symbolic mutual information (wSMI, global information sharing measure), Kolmogorov complexity (Kolcom, complexity measure), and heart-evoked potentials (the averaged EEG signal relative to the corresponding QRS complex on electrocardiography). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, and rsEEG data via a support vector machine. We studied 113 of 134 (84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50years, p = 0.01). Patients who recovered also had higher Kolcom (U = 1688, p = 0.01), increased beta power (U = 1,652 p = 0.003) with higher variability across channels (U = 1534, p = 0.034) and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04), and higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = 0.026; U = 1639, p = 0.024) than those who did not recover. The area under the receiver operating characteristic curve for rsEEG was higher than that for clinical data (using age, motor response, pupil reactivity) and higher than that for the Marshall computed tomography classification (0.69 vs. 0.66 vs. 0.56, respectively; p < 0.001). We describe the rsEEG signature in recovery of consciousness prior to discharge in comatose patients with TBI. rsEEG measures performed modestly better than the clinical and imaging data in predicting recovery.

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state.

Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis

This work explores the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behavior of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behavior. This study is centered on a small WF of five WTs and uses the observation that the same signals from different WTs in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, averaged signals from the SCADA system are used and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations are explored. The signals are encoded using the Discrete Cosine Transform, which reduces the problem’s dimensions. A hierarchical tree is built in each time window. Clusters are formed by pruning the tree using a threshold interpretable in terms of distance. It is unnecessary to work with an a priori known number of clusters. A protocol for enumerating the clusters based on the tree’s shape is then established, making it easier to follow the evolution of the clusters over time. The capability to automatically identify WTs whose signals differ from the group’s behavior can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

Optimal processing of surface facial EMG to identify emotional expressions: A data-driven approach

Surface facial electromyography (EMG) is commonly used to detect emotions from subtle facial expressions. Although there are established procedures for collecting EMG data and some aspects of their processing, there is little agreement among researchers about the optimal way to process the EMG signal, so that the study-unrelated variability (noise) is removed, and the emotion-related variability is best detected. The aim of the current paper was to establish an optimal processing pipeline for EMG data for identifying emotional expressions in facial muscles. We identified the most common processing steps from existing literature and created 72 processing pipelines that represented all the different processing choices. We applied these pipelines to a previously published dataset from a facial mimicry experiment, where 100 adult participants observed happy and sad facial expressions, whilst the activity of their facial muscles, zygomaticus major and corrugator supercilii, was recorded with EMG. We used a resampling approach and subsets of the original data to investigate the effect and robustness of different processing choices on the performance of a logistic regression model that predicted the mimicked emotion (happy/sad) from the EMG signal. In addition, we used a random forest model to identify the most important processing steps for the sensitivity of the logistic regression model. Three processing steps were found to be most impactful: baseline correction, standardisation within muscles, and standardisation within subjects. The chosen feature of interest and the signal averaging had little influence on the sensitivity to the effect. We recommend an optimal processing pipeline, share our code and data, and provide a step-by-step walkthrough for researchers.

Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images

The non-uniformity inherent in infrared detectors’ readout circuits often manifests as stripe noise, significantly impacting the interpretability and utility of infrared images in remote sensing applications. This paper introduces a novel three-step approach designed to overcome the challenges posed by stripe noise, offering a balance between real-time performance, detail preservation, and noise suppression. The proposed method involves subtracting the average of image columns from the noisy image, adding the wavelet denoised average signal to the subtraction result, and finally correcting the resulting image using an image-guidance mechanism. This unique three-step process ensures effective noise removal while preserving image details. The incorporation of wavelet transform leverages the sparsity of noise in the wavelet domain, enhancing denoising without introducing blurring. In a further refinement, the third step utilizes an image-guidance mechanism to recover small details with increased precision. This comprehensive approach addresses both stripe noise and non-uniformity, offering an easy, efficient, and fast technique for image correction. A comprehensive set of experiments, which involves comparisons with state-of-the-art algorithms, serves to substantiate the effectiveness and superior performance of the proposed method in real-world remote sensing and infrared images. Various examples, encompassing both real and artificial noise, are presented to showcase the robustness and applicability of our approach.

Simulation study of compressed ultrafast 3D imaging based on interferometry

Observing the 3D (three-dimensional) surface vibration in 100 MHz is challenging due to low speed of CCD/CMOS camera. In order to obtain the ultrafast dynamic 3D shape information, a compressed ultrafast 3D imaging framework based on interferometry is proposed in this paper. Based on this imaging framework, an reconstruction method combining plug and play-fast flexible denoising network is developed to recover high resolution interference fringe patterns and the corresponding 3D information. The simulation results show that the proposed method can observe the dynamic processes of the anti-phase vibration and the in-phase vibration of the crystal oscillator, therefore the dynamic 3D model is established. The proposed method outperforms other reconstruction methods in terms of average peak signal to noise ratio and structural similarity in recovery the ultrafast dynamic 3D surface.

Sympathetic Transduction to Blood Pressure in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is a major health problem affecting more than 35 million Americans. Notably, more patients with CKD die of cardiovascular complications than progress to end-stage renal disease. An overactive sympathetic nervous system is a well-known cardiovascular risk factor. Previous research has suggested elevated resting sympathetic nerve activity and a heightened α-adrenergic receptor sensitivity in patients with CKD, both of which may predispose this population to greater sympathetic transduction. Therefore, we tested the hypothesis that patients with stage III CKD have an augmented sympathetic transduction to blood pressure (BP) compared to controls without CKD. In 7 patients with stage III CKD and 8 controls muscle sympathetic nerve activity (MSNA; peroneal microneurography) and beat-to-beat BP (finometer) were continuously recorded during 10 minutes of quiet supine rest. Modelflow was used to estimate stroke volume and calculate total vascular conductance (TVC). Sympathetic transduction was quantified as the peak change in mean arterial pressure (MAP) and the nadir reduction of TVC over 10 cardiac cycles following spontaneous MSNA bursts using signal averaging. Patients with CKD and controls had similar resting MAP (CKD: 102 ± 14 mmHg; CON: 95 ± 8 mmHg; P = 0.27), MSNA burst frequency (CKD: 33 ± 10 bursts/min; CON: 31 ± 14 bursts/min; P = 0.78) and MSNA burst incidence (CKD: 53 ± 15 bursts/100 heartbeats; CON: 47 ± 24 bursts/100 heartbeats; P = 0.60). Peak increases in MAP following all bursts of MSNA were not different between patients with CKD and controls (CKD: 2.1 ± 0.7 mmHg; CON: 2.2 ± 0.9 mmHg; P = 0.83). Likewise, nadir reductions in TVC following all bursts of MSNA were not different in patients with CKD (CKD: -1.2 ± 0.6 ml/min/mmHg; CON: -1.4 ± 0.5 ml/min/mmHg; P = 0.46). Similar results were observed when considering burst patterning. MSNA bursts occurring in multiples (adjacent to other bursts) produced comparable peak MAP (CKD: 2.6 ± 0.6 mmHg; CON: 2.8 ± 1.2 mmHg; P = 0.67) and nadir TVC (CKD: -1.5 ± 0.7 ml/min/mmHg; CON: -1.9 ± 0.7 ml/min/mmHg; P = 0.25) responses between groups. There were also no differences in peak MAP and nadir TVC responses following single bursts of MSNA (bordered by cardiac cycles lacking bursts; both P &gt; 0.30). These preliminary data suggest that, in contrast to our hypothesis, sympathetic transduction to BP is not augmented in patients with stage III CKD. This project was supported by the National Heart, Lung, and Blood Institute R01 HL-127071 (PJF). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Ganglionic blockade modulates sympathetic neural discharge and neurocirculatory regulation in humans

This study investigated the impact of integrated muscle sympathetic nerve activity (MSNA) discharge patterns on human neurocirculatory regulation. In 13 healthy females (42 ± 15 years), the transduction of integrated MSNA (peroneal microneurography) into mean arterial pressure (MAP; arterial catheter) was studied during an intravenous infusion of a nicotinic ganglionic antagonist (trimethaphan camsylate, 1-7 mg/min). Neurovascular transduction (signal averaging) was studied based on the overall response, burst sequence (singlet, doublet, and triplet+), and burst amplitude (binned into tertials: small, medium, and large bursts). Data (mean ± SD) are reported for a 10-minute baseline (BSL), the one-minute period at the midpoint of the trimethaphan infusion with integrated MSNA bursts (TM-Mid), and the last minute of trimethaphan infusion with integrated MSNA bursts (TM-Last). During BSL, the MAP transduction responses were greatest for triplets+ (singlets: 0.9 ± 0.2 mmHg, doublets: 2.0 ± 0.6 mmHg, triplet+: 2.6 ± 0.9 mmHg; P &lt; 0.01) and large bursts (small: 1.0 ± 0.3 mmHg, medium: 1.7 ± 0.5 mmHg, large: 2.2 ± 0.7 mmHg; P &lt; 0.01). Trimethaphan infusion reduced integrated MSNA burst frequency resulting in a greater proportion of singlets (BSL: 45 ± 18 %, TM-Mid: 75 ± 23 %, TM-Last: 94 ± 20 %; P &lt; 0.01), a lower proportion of doublets, and a lower proportion of triplets+ (BSL: 23 ± 19 %, TM-Mid: 9 ± 18 %, TM-Last: 0 ± 0 %; P &lt; 0.01). Trimethaphan infusion reduced integrated MSNA burst amplitude resulting in a greater proportion of small bursts (BSL: 33 ± 0.2 %, TM-Mid: 60 ± 26 %, TM-Last: 78 ± 37 %; P &lt; 0.01), a lower proportion of medium bursts, and a lower proportion of large bursts (BSL: 34 ± 1 %, TM-Mid: 17 ± 26 %, TM-Last: 1 ± 5 %; P &lt; 0.01). Trimethaphan-mediated changes in MSNA burst frequency and amplitude were associated with reduced overall MAP transduction responses (BSL: 1.6 ± 0.5 mmHg, TM-Mid: 0.5 ± 0.2 mmHg, TM-Last: 0.3 ± 0.3 mmHg; P &lt; 0.01). Linear regression analyses demonstrated that the trimethaphan-mediated reduction in the proportion of large bursts ( β = 0.02 ± 0.008 mmHg/%, R2 = 0.13, P = 0.03) more strongly affected sympathetic neurovascular transduction responses than reductions in the proportion of triplet+ burst sequences (P = 0.91). These findings suggest that time varying MSNA discharge patterns, particularly variations in burst amplitude, support human neurocirculatory regulation. This work was supported by the National Institutes of Health and the Natural Sciences and Engineering Research Council of Canada. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Temporal Dynamics of Neural Response to Drug Cues in abstinent Methamphetamine Users.

Cue-induced craving is central to addictive disorders. Most cue-reactivity functional magnetic resonance imaging studies are analyzed statically and report averaged signals, disregarding the dynamic nature of craving and task fatigue. Accordingly, this study investigates temporal dynamics of the neural response to drug cues as a functional magnetic resonance imaging study among methamphetamine users. A total of 32 early abstinent methamphetamine users underwent functional magnetic resonance imaging while viewing visual methamphetamine cues. A craving > neutral contrast was obtained in regions of interest. To explore the changes over time, the pre-processed signal was divided into three intervals. Contrast estimates were calculated within each interval, and were compared using the analysis of variance followed by the post hoc t-tests. The results were compared with those from a static analysis across all blocks. A priori expected activations in the prefrontal cortex, insula, and striatum not detected by static analysis were discovered by the dynamic analysis. Post hoc tests revealed distinct temporal activation patterns in several regions. Most patterns showed rapid activation (including both ventral/dorsal striata and most regions in the prefrontal, insular, and cingulate cortices), whereas some had delayed activation (the right anterior insula, left middle frontal gyrus, and left dorsal anterior cingulate cortex). This study provided preliminary insights into the temporal dynamicity of cue-reactivity, and the potential of a conventional blocked-design task to consider it as a simple dynamic analysis. We highlight regional activations that were only uncovered by dynamic analysis and discuss the interesting and theoretically expected early versus late regional activation patterns. Rapidly activated regions are mostly those involved in the earlier stages of cue reactivity, while regions with later activation participate in cognitive functions relevant later, such as reappraisal, interoception, and executive control.

Functional Optical Coherence Tomography of Rat Cortical Neurovascular Activation during Monopulse Electrical Stimulation with the Microelectrode Array

This paper presents a study to evoke rat cortical functional activities, including hemodynamic and neural tissue signal changes, by monopulse electrical stimulation with a microelectrode array using functional optical coherence tomography (fOCT). Based on the principal component analysis and fuzzy clustering method (PCA-FCM), the hemodynamic response of different size blood vessels in rat cortex are analyzed, showing that the hemodynamic response of the superficial large blood vessels is more concentrated. In the regions of neural tissue where blood vessels are removed, positive significant pixels (the intensity of the pixel for five consecutive frames is greater than the average value plus triple standard deviation) and negative significant pixels (the intensity of the pixel for five consecutive frames is less than the average value minus triple standard deviation) exist, and the averaged intensity signal responds rapidly with an onset time of ~20.8 ms. Furthermore, the hemodynamic response was delayed by ~3.5 s from the neural tissue response. fOCT can provide a label-free, large-scale and depth-resolved map of cortical neurovascular activation, which is a promising technology to monitor cortical small-scale neurovascular activities.

A multiplex microchamber diffusion assay for the antibody-based detection of microRNAs on randomly ordered microbeads

MicroRNAs (miRNAs) are small non-coding RNA regulators linked to various human diseases incl. heart disease, a leading cause of death in Western countries. Their alterations signify the need for early detection methods. We devised a diffusion microbead assay, combining it with antibody-based miRNA detection. Diffusion involves co-diffusion of miRNAs and antibodies in a microchamber. Randomly ordered size and dye encoded microbeads loaded with specific capture probes target heart disease-associated miRNAs. MiRNA detection is time- and dose-dependent using an anti-DNA:RNA hybrid antibody. The miRNAs are successively exposed to randomly ordered microbeads, which leads to microbeads that become saturated with the target molecules first in front rows. Unbound miRNAs diffuse further and bind to microbeads with free binding sites. Our assay provides real-time multiplex detection of multiple miRNA within 2 h in an isothermal amplification-free environment, with low detection limits (miR-21-5p: 0.21 nM; miR-30a-3p: 0.03 nM; miR-93-5p: 0.43 nM). This study presents a miRNA detection principle that differs from other microbead assays where all microbeads are simultaneously mixed with the sample solution, so that all target molecules bind to microbeads equally, ultimately resulting in an averaged signal intensity.

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{82}$$\\end{document}Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model (p<0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<0.001$$\\end{document}), where the best AUC of 73.9% (CI 72.5–75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3–74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.

Obstacle shape determination by mobile robot sensor system using GP2Y0A (Sharp) type IR distance sensors

PURPOSE. When organizing a mobile robot (MR) movement in a non-deterministic environment, the SLAM problem arises, which includes the detection of an obstacle presence by the MR sensor system, the distance to the obstacle and its shape. To solve this problem, an infrared (IR) analog distance sensor is often used, the information flow from which is relatively small and can be processed in real time using low-performance microcontrollers. However, such a sensor can only detect an obstacle and determine а distance to a certain point on its surface. The goal is to develop a method for determining both а distance to an obstacle and its shape. When setting up experiments on the use of an analog sensor of the GP2Y0A (SHARP) type, a problem was revealed associated with the occurrence of not only fluctuation noise in а data communication channel, but also artifacts – anomalous signal values appear with random periodicity. It is necessary to determine the source of such interference, propose a method for estimating its parameters and a way to minimize its influence. METHODS. To determine the shape of an obstacle, a differential method is proposed based on the use of several readings of a scanning IR distance sensor. As an indicator of the “noisiness” of the channel, it is proposed to use the number of sensor signal values that exceed the average signal value by 1σ, 2σ, 3σ, 4σ and 5σ. The use of standard methods for filtering abnormal values of a sensor signal leads to significant delays in a response of the MR control system. This is unacceptable, because at the executive level of a control system it is required to provide a "hard" real-time mode. RESULTS. The article presents the results of experiments showing the conditions for applying the differential method for determining a shape of an obstacle, the source of anomalous signal values is identified and a method for minimizing them is proposed. A method for increasing the practical use range of a nonlinear IR sensor conversion function is also proposed. CONCLUSION. The number and magnitude of abnormal values depend on a communication channel length. When using analog sensors, it is necessary to convert an output signal into digital form using analog-to-digital converters (ADCs) in an integrated design, structurally bringing the ADC as close as possible to the signal source.

Investigating the Effects of Level-Specific CE-Chirp on Auditory Brainstem Response Waves in Normal Hearing Infants.

Auditory brainstem response (ABR) to the level-specific (LS) CE-Chirp has been reported to provide optimum neural synchrony along cochlear partitions, theoretically improving ABR waveform resolution. Despite this promising finding, limited studies have been conducted to contrast the results between LS CE-Chirp and Click stimuli. The current study aimed to compare the results of ABR between the two stimuli (Click and LS CE-Chirp). Sixty-seven normal-hearing infants, both with and without risk factors, aged less than 7 months old, participated in this study. The ABR test was conducted at 70 dBnHL using 33.3 stimulus repetition rates with both Click and LS CE-Chirp stimuli. The signal averaging was stopped at a maximum fixed signal average of 2,500 sweeps. Data were statistically compared between the two stimuli using the Wilcoxon signed-rank test. The waves I and V ABRs elicited by LS CE-Chirp exhibited significantly larger amplitudes than the Click stimulus. However, the amplitude of wave III and absolute latencies were similar in both stimuli at a supra-threshold level. LS CE-Chirp has the advantage of larger amplitudes than the ABR from Click at the supra-threshold level (70 dBnHL) in normal-hearing infants.

Helicopter Planet Gear Rim Crack Diagnosis and Trending Using Cepstrum Editing Enhanced with Deconvolution.

Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting-tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking.

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6dB at an input SNR of - 6dB and by 3.3dB at an input SNR of 10dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56dB.

The development and testing of a smart sensorized guide wire for catheterization in a "blood" vessel phantom to support aortic valve implementation.

Heart valve disease is commonly treated by minimally invasive procedures with guide wires and catheterization. The main purpose of this study is to find out whether an extension of the guide wire with a sensor can support the surgeon within the blood vessel to reduce X-ray necessity. A smart guide wire is developed by an extension with a flex-bending sensor to evaluate the sensor signal with and without "blood" flow at a constant compression force. Various surgically relevant investigations are performed. For assessment, the mean temporal average of the moving averaged filtered ADC signal and a subsequent FFT are carried out. Results show that there is a smaller sensor signal when the applied force or bending at the sensor is higher. In all investigations, there was a different sensor signal. The flex-bending sensor can detect the effect of pulsatile flow. The smallest temporal averaged signal difference between reference and clamp in the front wire's tip is 1.09%. For example, the mean temporal average of the filtered ADC signal for different clinically relevant scenarios is between 2550 and 2900. The results show that the sensorized guide wire developed for catheterization can support aortic valve implementation. The sensor sensitivity is sufficient to detect even very small variations within the blood vessel and therefore is promising to support catheterization heart valve surgeries in future.

Multipoint ECG Signal Extraction on Palms and Figures for Human Identification System

The ECG signal contain vital information for cardiac disease which is one of main cause of catastrophic mortality. In conventional technique, the signals are extracted through different points located on the limbs and surface of the chest. The biological impedance of human body eliminates any practical means of making fake copies of the relevant physiological traits. Moreover, this method needs the complex data acquisition system and apparatus as well as inconvenience way of acquisition of signal at the chest. Hence, there is a demand to find out the promising alternative technique to extract the ECG signals in more convenient way with high accuracy. In this paper, the real-time ECG monitor has been developed to extract the signals from the multipoint on the hands and fingers. Placement of the electrodes are done based on the acupressure point on palms and fingers. A total of 120 volunteers were investigated to develop the ECG database as the development dataset. This work uses the template matching algorithm and distance classification method to analyze and to find the best similarities between the developed dataset of ECG biometric signals to improve the identification rate. In order to lessen the noise that was recorded with the ECG signals, signal averaging was used to create ECG databases and templates. The recognition rate rose to 98% accuracy on the development dataset when the prescreening procedure was introduced to create a combined system model. ECG biometric model was created by combining the two models and using the development dataset's results. The algorithm was applied on the entire developed ECG dataset and 96% of accuracy rate was achieved in identification.