The use of intraluminal PRESERFLO stenting in avoiding early postoperative hypotony.

PURPOSE: Postoperative hypotony following PRESERFLO MicroShunt (PMS) implantation is a frequent cause of complications such as choroidal detachment and hypotony maculopathy. This study aims at evaluating the impact of intraluminal stenting of the PMS during the early postoperative period. METHODS: We retrospectively analyzed the data of 97 patients who underwent PMS implantation with intraoperative placement of a Nylon 10-0 suture as intraluminal stent (PStent) and compared the outcomes to those of an existing database of the traditional MicroShunt implantation technique (PTrad, n = 120). The primary outcome measure was the intraocular pressure (IOP) at one week postoperatively. As a secondary outcome measure, adverse hypotony, defined as an IOP ≤ 5 mmHg with significant choroidal effusion and/or anterior chamber shallowing or the presence of macular folds was also assessed. Additionally, the time to stent removal and the IOP one week after stent removal were reported. RESULTS: Preoperative median IOP was 25.0 (20.5-30.3) mmHg in PStent and 25.0 (19.3-32.0) mmHg in PTrad (p = 0.62). One week after surgery, the median IOP dropped to 10.0 (8.0-13.0) mmHg in PStent and 7.0 (5.0-9.0) in PTrad (p < 0.01). At one month, the IOP was 12.0 (10.0-14.0) mmHg in PStent and 10.0 (8.0-11.0) mmHg in PTrad (p < 0.01). After 3 months, both groups showed similar median IOP levels of 11.0 (8.0-13.5) mmHg and 10.0 (9.75-13.0) mmHg in PStent and PTrad, respectively (p = 0.66). The presence of adverse hypotony was significantly lower in PStent compared to PTrad (6.2% vs 15.8%, p < 0.05). In PStent the stent was removed after 30.0 (21.0-42.5) days. One week after stent removal the mean IOP drop was 6.1 ± 0.5 mmHg (p < 0.01). CONCLUSION: In the early follow-up period, intraluminal stenting of the PMS appears to be safe and effective in controlling the IOP while reducing early postoperative hypotony. Surgical success is not compromised by stent placement. Based on our data, it is recommended to remove the suture two to six weeks after surgery for most patients with uncomplicated postoperative clinical findings.

Phylogenetic indices and temporal and spatial scales shape the neighborhood effect on seedling survival in a mid-mountain moist evergreen broad-leaved forest, Gaoligong Mountains, Southwestern China.

Density dependence and habitat filtering have been proposed to aid in understanding community assembly and species coexistence. Phylogenetic relatedness between neighbors was used as a proxy for assessing the degree of ecological similarity among species. There are different conclusions regarding the neighborhood effect in previous studies with different phylogenetic indices or at different spatiotemporal scales. However, the effects of density dependence, neighbor phylogenetic relatedness, and habitat filtering on seedling survival with different phylogenetic indices or at different temporal and spatial scales are poorly understood. We monitored 916 seedlings representing 56 woody plant species within a 4-ha forest dynamics plot for 4 years (from 2020 to 2023) in a subtropical mid-mountain moist evergreen broad-leaved forest in the Gaoligong Mountains, Southwestern China. Using generalized linear mixed models, we tested whether and how four phylogenetic indices: total phylogenetic distance (TOTPd), average phylogenetic distance (AVEPd), relative average phylogenetic distance (APd'), and relative nearest taxon phylogenetic distance (NTPd'), three temporals (1, 2, and 3 years), and spatial scales (1, 2, and 4 ha) affect the effect of density dependence, phylogenetic density dependence, and habitat filtering on seedling survival. We found evidence of the effect of phylogenetic density dependence in the 4-ha forest dynamics plot. The effects of density dependence, phylogenetic density dependence, and habitat filtering on seedling survival were influenced by phylogenetic indices and temporal and spatial scales. The effects of phylogenetic density dependence and habitat filtering on seedling survival were more conspicuous only at 1-year intervals, compared with those at 2- and 3-year intervals. We did not detect any effects of neighborhood or habitat factors on seedling survival at small scales (1 and 2 ha), although these effects were more evident at the largest spatial scale (4 ha). These findings highlight that the effects of local neighborhoods and habitats on seedling survival are affected by phylogenetic indices as well as temporal and spatial scales. Our study suggested that phylogenetic index APd', shortest time scale (1 year), and largest spatial scales (4 ha) were suitable for neighborhood studies in a mid-mountain moist evergreen broad-leaved forest in Gaoligong Mountains. Phylogenetic indices and spatiotemporal scales have important impacts on the results of the neighborhood studies.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging.

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD's sensitivity to threshold selection, along with the RPCA's limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system.

Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for prompt gamma range verification compares the detected prompt gamma profile with a predicted one. Recently, a novel analytical prompt gamma prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. In this work, the said algorithm is validated against experimental data and benchmarked with another well-established prompt gamma prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation. The mean shifts between the experimental data and the simulated PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be 1.5~2.1 mm and -0.6~2.2 mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach.

A novel clustered-based detection method for shilling attack in private environments.

The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering.

Meso-scale environmental heterogeneity drives plant trait distributions in fragmented dry grasslands.

Environmental heterogeneity shapes the patterns of resources and limiting factors and therefore can be an important driver of plant community composition through the selection of the most adaptive functional traits. In this study, we explored plant trait-environment relationships in environmentally heterogeneous microsite complexes at the meso-scale (few meters), and used ancient Bulgarian and Hungarian burial mounds covered by dry grasslands as a model habitat. We assessed within-site trait variability typical of certain microsites with different combinations of environmental parameters (mound slopes with different aspects, mound tops, and surrounding plain grasslands) using a dataset of 480 vegetation plots. For this we calculated community-weighted means (CWMs) and abundance models. We found that despite their small size, the vegetation on mounds was characterized by different sets of functional traits (higher canopy, higher level of clonality, and heavier seeds) compared to the plain grasslands. North-facing slopes with mild environmental conditions were characterized by perennial species with light seeds, short flowering period, and a high proportion of dwarf shrubs sharply contrasted from the plain grasslands and from the south-facing slopes and mound tops with harsh environmental conditions. Patterns predicted by CWMs and abundance models differed in the case of certain traits (perenniality, canopy height, and leaf dry matter content), suggesting that environmental factors do not necessarily affect trait optima directly, but influence them indirectly through correlating traits. Due to the large relative differences in environmental parameters, contrasts in trait composition among microsites were mostly consistent and independent from the macroclimate. Mounds with high environmental heterogeneity can considerably increase variability in plant functional traits and ecological strategies at the site and landscape levels. The large trait variation on topographically heterogeneous landscape features can increase community resilience against climate change or stochastic disturbances, which underlines their conservation importance.

Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System.

Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss of groundnut plant growth, which will directly diminish the yield and quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System (GLDI-WDCGAN-AOA) is proposed in this paper. The pre-processed output is fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), the input leaf images are classified into Healthy leaf, early leaf spot, late leaf spot, nutrition deficiency, and rust. Finally, the weight parameters of WDCGAN are optimized by Aquila Optimization Algorithm (AOA) to achieve high accuracy. The proposed GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, and 18.65% higher accuracy and 24.78%, 23.24%, and 28.98% lower error rate analysed with existing methods, such as Real-time automated identification and categorization of groundnut leaf disease utilizing hybrid machine learning methods (GLDI-DNN), Online identification of peanut leaf diseases utilizing the data balancing method along deep transfer learning (GLDI-LWCNN), and deep learning-driven method depending on progressive scaling method for the precise categorization of groundnut leaf infections (GLDI-CNN), respectively.

Quantifying superimposed protein flow dynamics in live cells using spatial filtering and spatiotemporal image correlation spectroscopy.

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.

Prediction and positioning of UWSN mobile nodes based on tidal motion model.

As the node positioning of underwater wireless sensor networks is easily affected by tidal motion, ocean current motion and multipath effect, the node positioning accuracy is low. In order to better improve the positioning accuracy of moving nodes of underwater wireless sensor networks, a method of locating mobile nodes of underwater wireless sensor based on tidal motion model is proposed. Firstly, the Time Difference of Arrival (TDOA) localization optimized by niche genetic algorithm is used to initialize each node. The integration of niche technology can effectively find multiple excellent solutions in the solution space, thus providing more abundant solution choices. This algorithm has excellent performance in multi-modal optimization problems, and can avoid the algorithm falling into local optimal solutions, so as to obtain more comprehensive optimization results. The simulation results show that the proposed algorithm has better positioning accuracy than the traditional Chan algorithm and Taylor algorithm. Then, each node is updated in real time by the optimized tidal movement model formula predicted by Kalman filter algorithm. The prediction algorithm is used to compare the real-time predicted update position of the node with the actual position. The positioning distance error of the prediction algorithm is also enough to meet the practical application requirements.

Haperforatones A-M, thirteen undescribed limonoids from Harrisonia perforata with anti-inflammatory activity.

UPLC-Q-TOF-MS combined with mass defect filtering strategies were applied for the phytochemical investigation of Harrisonia perforata, leading to the isolation of thirteen undescribed limonoids named haperforatones A-M (1-13) and seventeen known compounds (14-30). Particularly, haperforatones D-E (4-5) have an unprecedented A, B, C, D-seco-6, 7-nor-C-24-limonoid skeleton, structurally stripped of the five-membered lactone ring B and formed a double bond at the C-5 and C-10 positions. Their 2D structures and relative configurations were identified using spectroscopic data. The absolute configurations of 1, 4, and 6 were established via X-ray diffraction crystallography. All 30 compounds were evaluated for anti-inflammatory potential in LPS-induced Raw 264.7 cell lines. Among those tested compounds, the most potent activity against LPS-induced NO generation was demonstrated by haperforatone F (6), with the IC50 value of inhibition NO production of 7.2 µM. Additionally, 6 could significantly inhibit IL-1ß and IL-6 release and markedly downregulate the protein expression level of iNOS in the LPS-stimulated RAW264.7 cells at 10 µM. The possible mechanism of NO inhibition of 6 was also investigated using molecular docking, which revealed the interaction of compound 6 with the iNOS protein.

Application of KL distance-based intelligent recommendation method to fund recommendation for users with investment behavior in Asia Region.

Over the past decade, the spectacular growth of many economies in Asia has amazed the economics profession and Asian people's quality of life has been significantly improved. Therefore, people's savings are increasing. The booming financial market attracts more people to invest in funds for its relatively low risk and high returns. This research first analyzed the widely used recommendation algorithms. Then it adopted the K-medoids clustering algorithm based on KL divergence to make recommendations. The recommendations were also based on the user-item rating matrix. First, the improved Kullback-Leibler distance was used to calculate the distance and similarity between the funds. Next, the data set of n funds were split by the K-medoids clustering technique into k clusters to obtain funds that were similar to the target fund. Finally, the prediction accuracy of the KL-KM recommendation algorithm was compared with those of traditional recommendation algorithms. When the number of recommendations changed from 1 to 5, the average minimum KL distance varied from 0.16, 0.22, 0.29, 0.31, to 0.39. The average KL similarity is 0.85, 0.83, 0.79, 0.74, and 0.72, respectively, and the average absolute error is 0.83, 0.82, 0.82, 0.83, and 0.82, respectively. The root mean square error (RMSE) of the proposed algorithm is 0.93, 0.94, 0.93, 0.94 and 0.94, respectively. Therefore, the proposed recommendation model has a better recommendation performance than existing models to meet users' demands. This algorithm generates recommendations of similar funds based on customers' purchase history. Moreover, principal component analysis is used to simplify the large data set of indicator values into a smaller set while still maintaining significant patterns and trends, thereby improving the accuracy and reducing the complexity of the model.

Unsupervised denoising of the non-invasive fetal electrocardiogram with sparse domain Kalman filtering and vectorcardiographic loop alignment.

Objective.Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method.Approach.This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG.Main results.The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33∘. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities.Significance.This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method's objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

Species turnover and functional nestedness constitute the geographic patterns of stream diatoms in the Three Parallel Rivers region, China.

Unraveling biodiversity patterns and their driving processes is paramount in ecology and biogeography. However, there remains a limited understanding regarding the underlying mechanisms of community assembly, particularly in alpine streams where significant elevation gradients and habitat heterogeneity exist. We investigated the patterns and drivers of beta diversity, explicitly focusing on taxonomic and functional diversity, in the three parallel rivers region in China. We employed a beta diversity partitioning approach to examine the turnover and nestedness components of beta diversity and further deconstructed the diatom community into attached and unattached groups. Our results revealed distinct diversity patterns and drivers for taxonomic and functional beta diversity. Specifically, taxonomic beta diversity was mainly driven by the turnover component affected by spatial processes, whereas functional beta diversity was dominated by the nestedness component affected by environmental processes. Furthermore, our analysis of the division of the whole communities demonstrated that the varying responses of benthic diatoms with different attached abilities to environmental filtering, dispersal limitation, and directional flow were the essential reasons for shaping the biodiversity patterns of species turnover and functional nestedness in the alpine stream. Our findings suggested that partitioning beta diversity and dividing the entire community can more deeply infer underlying community assembly processes, thereby providing valuable insights into understanding biodiversity patterns, drivers, and conservation strategies.

Efficacy and Safety of the Subtenon Injection of 0.01% Mitomycin C-augmented Trabeculectomy.

Background Trabeculectomy, with the application of mitomycin C (MMC), has been the gold standard glaucoma-filtering surgery. The conventional method of applying MMC using soaked sponges does not ensure a controlled amount of delivery of MMC, and incidences of blebitis are reported to be associated with leftover sponges. This study aims to assess the safety and efficacy of a low dose (0.1 mg/ml) of MMC administered through subtenon injection during trabeculectomy combined with cataract extraction, addressing existing research gaps. Methods It is a prospective interventional study on patients who underwent trabeculectomy with a subtenon injection of 0.1 mg/ml of MMC combined with cataract extraction and were followed up over six months. Efficacy was determined in terms of intraocular pressure (IOP) reduction; bleb architecture was graded using the Indiana Bleb Appearance Grading System (IBAGS) and success rate, and safety was commented upon in terms of complications. Results Thirty patients were enrolled, with the majority having primary open-angle glaucoma (63.33%), while 36.67% had primary angle-closure glaucoma. Baseline IOP was 31.40(±10.38) mmHg. It significantly reduced to 14.60(±3.75) mmHg on the first postoperative day, decreasing to 9.55(±1.57) mmHg by the sixth postoperative month (p=0.001). The percentage reduction in IOP was substantial, 69.57%, by the sixth postoperative visit. Bleb morphology assessment using IBAGS revealed significant improvements in bleb height and extent and a reduction in vascularity over the six-month follow-up (p=0.001), and no eyes had bleb encapsulation. Out of the total patients, 93.33% achieved controlled IOP without anti-glaucoma medications, while 6.67% required one medication for IOP control. Complications were minimal, with transient corneal edema in six patients and manageable postoperative hypotony in one case. Conclusion A subtenon injection of MMC during trabeculectomy effectively reduces IOP and promotes favorable bleb architecture, offering an effective and safer alternative to the conventional approach.

Single-beam digital holographic reconstruction: a phase-support enhanced complex wavefront on phase-only function for twin-image elimination.

Significance: In in-line digital holographic microscopy (DHM), twin-image artifacts pose a significant challenge, and reduction or complete elimination is essential for object reconstruction. Aim: To facilitate object reconstruction using a single hologram, significantly reduce inaccuracies, and avoid iterative processing, a digital holographic reconstruction algorithm called phase-support constraint on phase-only function (PCOF) is presented. Approach: In-line DHM simulations and tabletop experiments employing the sliding-window approach are used to compute the arithmetic mean and variance of the phase values in the reconstructed image. A support constraint mask, through variance thresholding, effectively enabled twin-image artifacts. Results: Quantitative evaluations using metrics such as mean squared error, peak signal-to-noise ratio, and mean structural similarity index show PCOF's superior capability in eliminating twin-image artifacts and achieving high-fidelity reconstructions compared with conventional methods such as angular spectrum and iterative phase retrieval methods. Conclusions: PCOF stands as a promising approach to in-line digital holographic reconstruction, offering a robust solution to mitigate twin-image artifacts and enhance the fidelity of reconstructed objects.

Manoeuvre Target Tracking in Wireless Sensor Networks Using Convolutional Bi-Directional Long Short-Term Memory Neural Networks and Extended Kalman Filtering.

Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios.

A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis.

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

Enhancing ECG readability in LVAD patients: A comparative analysis of Denoising techniques with an emphasis on discrete wavelet transform.

BACKGROUND: Electrocardiograms (ECGs) are vital for diagnosing cardiac conditions but obtaining clean signals in Left Ventricular Assist Device (LVAD) patients is hindered by electromagnetic interference (EMI). Traditional filters have limited efficacy. There is a current need for an easy and effective method. METHODS: Raw ECG data obtained from 5 patients with LVADs. LVAD types included HeartMate II, III at multiple impeller speeds, and a case with HeartMate III and a ProtekDuo. ECG spectral profiles were examined ensuring the presence of diverse types of EMI in the study. ECGs were then processed with four denoising techniques: Moving Average Filter, Finite Impulse Response Filter, Fast Fourier Transform, and Discrete Wavelet Transform. RESULTS: Discrete Wavelet Transform proved as the most promising method. It offered a one solution fits all, enabling automatic processing with minimal user input while preserving crucial high-frequency components and reducing LVAD EMI artifacts. CONCLUSION: Our study demonstrates the practicality and efficiency of Discrete Wavelet Transform in obtaining high-fidelity ECGs in LVAD patients. This method could enhance clinical diagnosis and monitoring.

High Density 3D Carbon Tube Nanoarray Electrode Boosting the Capacitance of Filter Capacitor.

Electric double-layer capacitors (EDLCs) with fast frequency response are regarded as small-scale alternatives to the commercial bulky aluminum electrolytic capacitors. Creating carbon-based nanoarray electrodes with precise alignment and smooth ion channels is crucial for enhancing EDLCs' performance. However, controlling the density of macropore-dominated nanoarray electrodes poses challenges in boosting the capacitance of line-filtering EDLCs. Herein, a simple technique to finely adjust the vertical-pore diameter and inter-spacing in three-dimensional nanoporous anodic aluminum oxide (3D-AAO) template is achieved, and 3D compactly arranged carbon tube (3D-CACT) nanoarrays are created as electrodes for symmetrical EDLCs using nanoporous 3D-AAO template-assisted chemical vapor deposition of carbon. The 3D-CACT electrodes demonstrate a high surface area of 253.0 m2 g-1, a D/G band intensity ratio of 0.94, and a C/O atomic ratio of 8. As a result, the high-density 3D-CT nanoarray-based sandwich-type EDLCs demonstrate a record high specific areal capacitance of 3.23 mF cm-2 at 120 Hz and exceptional fast frequency response due to the vertically aligned and highly ordered nanoarray of closely packed CT units. The 3D-CT nanoarray electrode-based EDLCs could serve as line filters in integrated circuits, aiding power system miniaturization.

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.