Optimization of activity-driven event detection for long-term ambulatory urodynamics.

Introduction. Symptoms of lower urinary tract dysfunction (LUTS) in older men are most often associated with benign prostatic hyperplasia (BPH). A low level of testosterone leads to various changes in the organs and systems of the body, including causing structural changes in the tissues of the genitourinary system, which negatively affects their function. The aim of this work was to study the effect of testosterone replacement therapy on LUTS with androgen deficiency in patients after surgical treatment of BPH. Materials and methods. The analysis of observation and treatment of LUTS in 101 patients operated on for BPH was performed. The patients are divided into three groups. Group A (n=48) included patients with normal testosterone levels receiving conservative therapy for moderate LUTS. Groups B (n=38) and C (n=21) included patients with SNIP and low testosterone levels. In order to correct the level of testosterone in patients in group C, after surgical treatment, androgen replacement therapy was prescribed. For primary diagnosis and evaluation of the effectiveness of the treatment vinitial and during periods of 1, 3, 6 and 12 months, general clinical examination methods, the International Pancreatic Symptom Scale (IPSS) questionnaire, urination diaries (DM), uroflowmetry, and ultrasound examination of the pancreas and bladder were used. Results. According to the IPSS questionnaire, patients in group B had more pronounced manifestations of LUTS compared with patients in groups A and C due to accumulation symptoms. At the same time, by prescribing androgen replacement therapy, it was possible to achieve an increase in the level of total testosterone in patients of group C to «physiological values», corresponding to patients in group A. According to the results of the analysis of DM data, it was found that in patients in all observation groups, the number of urination per day decreased (p<0.001) with an increase in the functional capacity of the bladder. The highest values of the maximum micturition volume, at the end of the observation period, were recorded in patients in group A, the minimum micturition volume and the most pronounced decrease in episodes of nocturia and nocturia index – in patients in group C. Temporal values of the accumulative bladder function also indicate a more pronounced decrease LUTS in group C. Conclusions. The use of androgen replacement therapy increases the effectiveness of LUTS treatment in patients with androgen deficiency after surgical treatment of BPH.

Traumatic spinal cord injury (SCI) produces lower urinary tract (LUT) dysfunction that has been studied in surgical transection models. Our aim was to assess LUT functional deficit in a clinically relevant model of incomplete SCI to investigate how partial preservation of supraspinal connections might affect LUT dysfunction. Standardized weight-drop contusion (10 g x 2.5 cm) or complete transection, was produced at T8 in female Sprague-Dawley rats. Behavioral tests were used to assess hind limb sensorimotor function at Day 1 after surgery and weekly thereafter. The urometric experiments were conducted on groups (n = 7) of uninjured rats and on injured rats during Weeks 1 and 2 after SCI (before and after spontaneous voiding was established) as well as Week 2 after a complete transection (n = 3). Under anesthesia, the bladder was continuously perfused with saline. Changes in bladder pressure and external urethral sphincter (EUS) electrical activity were monitored. The bladder was then dissected and weighed and both the bladder and spinal cord were fixed for pathoanatomical analyses. Our results indicate that several aspects of LUT dysfunction after contusive SCI were similar to transection, e.g., reduction of voiding efficiency (approximately 5% of normal value), decrease in inter-contraction interval (47%), increase in bladder capacity (8-fold), and weight (4.6-fold). One aspect appeared different from transection--partial recovery from acute bladder/sphincter dyssynergia. Because the coordination of bladder and EUS function is mediated by brainstem pathways, partial recovery of synergy after SCI was likely due to sparing of some relevant bulbospinal projections as was confirmed by retrograde transneuronal viral tracing.

The ureteric diameter on IVU was compared with the MCU findings of reflux or lower urinary tract dysfunction in a group of patients followed up after spinal cord injury. A wide ureter was associated with reflux in 20%, but 8 normal sized ureters on IVU also refluxed--5 of them to a significant degree. In the non-refluxing group, wide ureters were noted more often in patients whose bladder pressures were high and wildly fluctuating than in those with normal bladder pressures. Similarly, the incidence of reflux was higher in patients with high bladder pressures and wild fluctuations of bladder pressure than in those with normal pressures. While a wide ureter on IVU should arouse suspicion that reflux or lower urinary tract dysfunction might be present, these conditions are not necessarily excluded by the finding of a ureter of normal size.

Capturing images in thousands of contiguous spectral bands has been made simpler with the emergence of technology in the field of hyperspectral remote sensing. Despite of these huge data available for analysis, Hyperspectral images (HSI) face many challenges due to high dimensionality, noise, spectral mixing and computational complexity. Several preprocessing methods can be used to overcome the above mentioned issues. In this paper, an enhancement technique using 2D-Empirical Wavelet Transform (EWT) is used as a preprocessing step for the HSI reconstruction prior to sparsity based classification (Subspace Pursuit and Orthogonal Matching Pursuit). The effectiveness of the proposed method is proved by comparing the classification results obtained with and without applying preprocessing. Experimental analysis shows a significant improvement in the classification accuracies i.e., for 40 $$\%$$ of training samples, OMP shows an improvement in overall classification accuracy from 66.12 $$\%$$ to 93.20 $$\%$$ and SP shows an improvement from 66.36 $$\%$$ to 92.74 $$\%$$ .

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1 ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.

A brain-computer interface (BCI) is a system that enables control of devices or communication with other persons, only through cerebral activity, without using muscles. The main application for BCIs is assistive technology for disabled persons. Examples for devices that can be controlled by BCIs are artificial limbs, spelling devices, or environment control systems. BCI research has seen renewed interest in recent years, and it has been convincingly shown that communication via a BCI is in principle feasible. However, present day systems still have shortcomings that prevent their widespread application. In part, these shortcomings are caused by limitations in the functionality of the pattern recognition algorithms used for discriminating brain signals in BCIs. Moreover, BCIs are often tested exclusively with able-bodied persons instead of conducting tests with the target user group, namely disabled persons. The goal of this thesis is to extend the functionality of pattern recognition algorithms for BCI systems and to move towards systems that are helpful for disabled users. We discuss extensions of linear discriminant analysis (LDA), which is a simple but efficient method for pattern recognition. In particular, a framework from Bayesian machine learning, the so-called evidence framework, is applied to LDA. An algorithm is obtained that learns classifiers quickly, robustly, and fully automatically. An extension of this algorithm allows to automatically reduce the number of sensors needed for acquisition of brain signals. More specifically, the algorithm allows to perform electrode selection. The algorithm for electrode selection is based on a concept known as automatic relevance determination (ARD) in Bayesian machine learning. The last part of the algorithmic development in this thesis concerns methods for computing accurate estimates of class probabilities in LDA-like classifiers. These probabilities are used to build a BCI that dynamically adapts the amount of acquired data, so that a preset, approximate bound on the probability of misclassifications is not exceeded. To test the algorithms described in this thesis, a BCI specifically tailored for disabled persons is introduced. The system uses electroencephalogram (EEG) signals and is based on the P300 evoked potential. Datasets recorded from five disabled and four able-bodied subjects are used to show that the Bayesian version of LDA outperforms plain LDA in terms of classification accuracy. Also, the impact of different static electrode configurations on classification accuracy is tested. In addition, experiments with the same datasets demonstrate that the algorithm for electrode selection is computationally efficient, yields physiologically plausible results, and improves classification accuracy over static electrode configurations. The classification accuracy is further improved by dynamically adapting the amount of acquired data. Besides the datasets recorded from disabled and able-bodied subjects, benchmark datasets from BCI competitions are used to show that the algorithms discussed in this thesis are competitive with state-of-the-art electroencephalogram (EEG) classification algorithms. While the experiments in this thesis are uniquely performed with P300 datasets, the presented algorithms might also be useful for other types of BCI systems based on the EEG. This is the case because functionalities such as robust and automatic computation of classifiers, electrode selection, and estimation of class probabilities are useful in many BCI systems. Seen from a more general point of view, many applications that rely on the classification of cerebral activity could possibly benefit from the methods developed in this thesis. Among the potential applications are interrogative polygraphy ("lie detection") and clinical applications, for example coma outcome prognosis and depth of anesthesia monitoring.

Introduction: This paper presents a novel approach for classifying different types of steel rods using a combination of pre-trained convolutional neural networks (CNNs) and fuzzy logic systems. We utilize VGG16, ResNet50, and InceptionV3 models to extract features from grayscale images of steel rods, which are then classified using Support Vector Machines (SVM). Furthermore, we incorporate a fuzzy logic system to improve the final classification accuracy by combining the individual accuracies of each CNN model. Experimental results demonstrate the effectiveness of our approach in achieving high classification accuracy compared to traditional methods. Objectives: This research presents a novel multi-model approach for steel rod classification utilizing three distinct fuzzy logic systems: Fuzzy Recurrent Neural Network (RNN), Fuzzy Convolutional Neural Network (CNN), and Fuzzy Neural Network. The proposed system addresses the critical challenge of accurate defect detection and classification in steel manufacturing processes. By combining these complementary approaches, we achieve enhanced feature extraction capabilities and improved classification accuracy. Experimental results demonstrate that our multi-model system achieves a classification accuracy of 97.8%, surpassing single-model approaches by an average of 8.2%. The system successfully identifies surface defects, structural anomalies, and dimensional variations with high precision, making it suitable for real-time industrial applications. Methods: This paper introduces a novel approach for the feature extraction and classification of steel rods by integrating multiple models and utilizing fuzzy logic systems. The methodology leverages pre-trained convolutional neural networks (CNNs) for extracting comprehensive feature representations from steel rod images. These extracted features are then combined and integrated using fuzzy logic to enhance classification accuracy and reliability. By addressing the limitations of traditional manual inspection methods and single-model approaches, this integrated multi-model system offers a robust solution for real-time industrial implementation. The proposed system aims to improve the efficiency and effectiveness of quality control processes in steel rod manufacturing, ensuring consistent and accurate classification even under varying conditions and complexities. This innovative approach paves the way for more advanced and adaptable automated inspection systems in the industry. Results: The paper aims to develop an integrated multi-model system that combines three distinct fuzzy logic approaches to enhance feature extraction capabilities through complementary methodologies. By improving the classification accuracy and reliability, the system addresses the limitations of existing methods. The goal is to create a robust and adaptable system that is suitable for real-time industrial implementation for steel rod analysis and classification. This comprehensive approach leverages the strengths of multiple models and fuzzy logic techniques, ensuring accurate and efficient classification processes, even under complex and varying conditions. The proposed multi model system is designed to meet the stringent quality control requirements of industrial applications, ultimately improving productivity and product quality through reliable automated inspection and classification for steel rod with accuracy of 97.80%, precision of 97.30% and recall 97.50%. Conclusions: This paper presents a novel approach for the classification of steel rods using a combination of pre-trained CNN models and fuzzy logic systems. The experimental results demonstrate the effectiveness of our method in achieving high classification accuracy. This research demonstrates the effectiveness of a multi-model fuzzy logic approach for steel rod classification. The system achieves significant improvements over single-model approaches while maintaining real-time processing capabilities. Our approach provides a foundation for further research and development in automated industrial image classification.

The presence of motion artifacts in the photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in real time and continuous monitoring applications. In the current paper we present a comparison between two motion artifacts detection methodologies proposed by Couceiro et al. [1] and Correia T. [2]. The first method is based on the analysis of the variations in the time and period domain characteristics of the PPG signal. The second method analysis the differences between the changes in the Heart Rate measured from the PPG and the ECG signals. Both methodologies are validated in healthy and cardiovascular diseased volunteers, for 11 different motion artifact patterns. The results achieved by the presented methodologies show a better performance of the first algorithm (SE: 83% and SP: 87%) and the great importance of both time and period domain features in the discrimination of motion artifacts from clean PPG pulses.

In land-use classification of hyperspectral remote sensing (RS) images, traditional classification methods often experience large amount of datasets and low efficiency. To solve these problems, a fast machine-learning method, the extreme learning machine (ELM) algorithm, was introduced. However, basic use of the ELM usually encounters problems of unstable classification results and low classification accuracy. Hence, in this paper, optimization methods for ELM-based RS image classification were mainly discussed and applied to solve the bottleneck problems. From the three perspectives of ensemble learning, making full use of image texture features, and deep learning, three classification optimization methods have been designed and implemented. The results show that: 1) To some extent, all the three methods can achieve a balance between classification accuracy and efficiency, i.e., they can maintain the advantage of ELM algorithm in classification efficiency and speed while have better classification accuracy; 2) The image texture feature optimization method (LBP-KELM) solves the problem of unsatisfactory classification results experienced by the ensemble learning optimization method (Ensemble-ELM) and further improves classification accuracy. However, the classification results are sensitive to the type of dataset; and 3) Fortunately, the optimization method combined with deep learning (CNN-ELM) can meet the application needs of multiple datasets. Furthermore, it can also further improve classification accuracy.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the ‘salt and pepper noise’. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Trunk exoskeletons are an emerging technology that could reduce spinal loading, guide trunk motion, and augment lifting ability. However, while they have achieved promising results in brief laboratory studies, they have not yet been tested in longer-term real-world studies - partially due to reliance on stationary sensors such as cameras. To enable future real-world evaluations of trunk exoskeletons, this paper describes two preliminary studies on using inertial measurement units (IMUs) to collect kinematic data from an exoskeleton wearer. In the first study, a participant performed three activities (walking, sit-to-stand, box lifting) while trunk flexion angle was measured with both IMUs and reference cameras. The mean absolute difference in flexion angle between the two methods was 1.4° during walking, 3.6° during sit-to-stand and 5.2° during box lifting, showing that IMUs can measure trunk flexion with a reasonable accuracy. In the second study, six participants performed five activities (standing, sitting straight, slouching, 'good' lifting, 'bad' lifting), and a naïve Bayes classifier was used to automatically classify the activity from IMU data. The classification accuracy was 92.2%, indicating the feasibility of automated activity classification using IMUs. The IMUs will next be used to obtain longer-term recordings of different activities performed both with and without a trunk exoskeleton to determine how the exoskeleton affects a person's posture and behavior.

Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.

Application of neural machine interface in individuals with chronic hemiparetic stroke is regarded as a great challenge, especially for classification of the hand opening and grasping during a functional upper extremity movement such as reach-to-grasp. The overall accuracy of classifying hand movements, while actively lifting the paretic arm, is subject to a significant reduction compared to the accuracy when the arm is fully supported. Such a reduction is believed to be due to the expression of flexion synergy, which couples shoulder abduction (SABD) with elbow/wrist and finger flexion, and is common in up to 60% of the stroke population. Little research has been done to develop methods to reduce the impact of flexion synergy on the classification of hand opening and grasping. In this study, we proposed a novel approach to classify hand opening and grasping in the context of the flexion synergy using a wavelet coherence-based filter. We first identified the frequency ranges where the coherence between the SABD muscle and wrist/finger flexion muscles is significant in each participant, and then removed the synergy-induced electromyogram (EMG) component with a subject-specific and muscle-specific coherence-based filter. The new approach was tested in 21 stroke individuals with moderate to severe motor impairments. Employing the filter, 14 participants gained improvement in classification accuracy with a range of 0.1 to 14%, while four showed 0.3 to 1.2% reduction. The remaining three participants were excluded from comparison due to the lack of significant coherence, thus no filters were applied. The improvement in classification accuracy is significant (p = 0.017) when the SABD loading equals 50% of the maximal torque. Our findings suggest that the coherence-based filters can reduce the impact of flexion synergy by removing the synergy-induced EMG component and have the potential to improve the overall classification accuracy of hand movements in individuals with poststroke flexion synergy.

Observation of broadband acoustic scatter from fish at multiple angles has the potential to offer advantages for remote classification in cases where the species exhibit differences in size and or body shape, and animal orientation is unknown or difficult to estimate. However, the total angular coverage of the observations, available bandwidth, and the inter- and intra-species variability in size can significantly influence classification accuracy. These parameters are investigated using laboratory data collected from ten different fish species using a linear array of receivers and a single transmitter with an effective angular coverage of up to 25 degrees with a 3 degree sampling increment. Results indicate that when the species exhibit significant differences in length, even two observations covering only 10 degrees are sufficient to obtain an 80 percent improvement in classification accuracy over a single observation angle. This holds true for a wide range of animal orientations. As inter-species size similarity increases, and intra-species size variability increases, more observations covering a larger range of angles are required to maintain a similar improvement in classification accuracy.

Often, previous knowledge about the classes on the ground may assist in the improvement of a land cover classification from remote sensing data. There are many ways in which the previous information in the form of a priori probabilities can be included in the training stage of a supervised classification process. The inclusion of a priori probabilities may be useful to classify the images dominated by mixed pixels. In this paper, an approach to incorporate a priori probabilities in back propagation neural network (BPNN) algorithm, by way of replicating the training data (consisting of pure pixels) of a class according to its proportional area coverage has been implemented. By doing this, the abundant classes are assigned more weights than the other classes in the image. The results show a significant improvement in classification accuracy by 20% over the case when the a priori probabilities are not included. However, the major limitation of this approach is that it depends on the use of pure pixels, which may often be hard to find in images dominated by mixed pixels. We therefore suggest an alternative approach that incorporates mixed pixels in the training stage of BPNN algorithm. The results from this approach also reflect a significant improvement in classification accuracy by 14%. Further, both the approaches produce significantly higher accuracy than the most widely used maximum likelihood classifier (MLC). However, the second approach, which does not depend on identification of pure pixels in the image and their replication, appears more attractive to produce meaningful and accurate land cover classification from remote sensing data.