Abstract in English:Abstract Introduction Premature Ventricular Contraction (PVC) is among the most common types of ventricular cardiac arrhythmia. However, it only poses danger if the person suffers from a heart disease, such as heart failure. Hence, this is an important factor to consider in heart disease people. This paper presents an ECG real-time analysis system for PVC detection. Methods This system is based on threshold adaptive methods and Redundant Discrete Wavelet Transform (RDWT), with a real-time approach. This analysis is based on wavelet coefficients energy for PVC detection. It is presented also a study to find the most indicated wavelet mother for ECG analysis application among the following wavelet families: Daubechies, Coiflets and Symlets. The system detection performance was validated on the MIT-BIH Arrhythmia Database. Results The best results were verified with db2 wavelet mother: the Sensitivity Se = 99.18%, Positive Predictive Value P+ = 99.15% and Specificity Sp = 99.94%, on 80.872 annotated beats, and 61.2 s processing speed for a half-hour record. Conclusion The proposed system exhibits reliable PVC detection, with real-time approach, and a simple algorithmic structure that can be implemented in many platforms.
Abstract in English:Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.
Abstract in English:Abstract Introduction The aim of this study was to predict 3D ground reaction force signals based on accelerometer data during gait, using a feed-forward neural network (MLP). Methods Seventeen healthy subjects were instructed to walk at a self-selected speed with a 3D accelerometer attached to the distal and anterior part of the shank. A force plate was embedded into the middle of the walkway. MLP neural networks with one hidden layer and three output layers were selected to simulate the anteroposterior (AP), vertical (Vert) and mediolateral (ML) ground reaction forces (GRF). The input layer was composed of fourteen inputs obtained from accelerometer signals, selected based on previous studies. Principal component analysis (PCA) was used to compare the simulated and collected curves. The Pearson correlation coefficient and the mean absolute deviation (MAD) between signals were calculated. Results PCA identified small, but significant differences between collected and simulated signals in the loading response phases of AP and ML GRF, while Vert did not show differences. The correlation between the simulated and collected signals was high (AP: 0.97; Vert: 0.98; ML: 0.80). MAD was 1.8%BW for AP, 4.5%BW for Vert and 1.4%BW for ML. Conclusion This study confirmed that multilayer perceptron neural network can predict the highly non-linear relationship of shank acceleration parameters and ground reaction forces, as well as other studies have done using plantar pressure devices. The greater advantages of this device are the low cost and the possibility of use outside the laboratory environment.
Abstract in English:Abstract Introduction This research investigates the applicability of a relatively new estimator of mutual information, KSG estimator, to find the reconstruction delay of phase space in dynamical systems. There are evidences that the KSG estimator is more accurate than the naive method commonly used. Methods In this paper we estimated mutual information between the voice signals and their delayed versions, with KSG method. The voice signals were obtained from a disordered voice database. Then, we found the reconstruction delay where mutual information reached its first minimum. We applied the encountered value of reconstruction delay in linear discriminant analysis, in order to discriminate between healthy and pathological voices or to discriminate between pathologies. Discrimination between voice pathologies using nonlinear measurements is still not much explored. Moreover, in this paper we used a single nonlinear measurement: reconstruction delay. Results The results show that the reconstruction delay obtained with KSG method has increased classification rates in most cases, in terms of accuracy, sensitivity and specificity, when compared to the naive estimator usually adopted. Conclusion The KSG estimator is a promising technique to improve the diagnosis of voice related pathologies.
Abstract in English:Abstract Introduction Cardiovascular diseases represent a major cause of death world-wide and one of their greatest complications is the development of cardiac arrhythmias, in which ventricular fibrillation (VF) stands out as the most severe one. The only therapy that reverses VF is defibrillation. However defibrillatory shock is capable of killing heart cells and it is known that the orientation of the cell major axis with respect to the electrical field (E) direction is a determining factor for cellular excitation and injury, which is leading to the development of new defibrillation protocols. The aim of this work is to fill the gap in information about cell lethality for intermediate cell orientation angles. Methods Ventricular myocytes were extracted from adult male Wistar rats and the cells were plated in a chamber for perfusion and stimulation with bipolar voltage pulses to determine the stimulation threshold (ET). Then, monopolar stimulus was applied and amplitude was increased until cell lethal injury. This protocol was performed on four experimental groups: cells oriented at 0°, 30°, 60° and 90°, with respect to E direction. Results 87 cells were analyzed and an increase in amplitude of E associated with 50% lethality (E50) was verified as the direction of E application and cell major axis orientation departed. Conclusion Taken the same probability of lethality, our data suggest a nonlinear increase of E amplitude from 0° to 90° similar to that of ET. These in-between data had not yet been shown and are important for service-based future defibrillation protocols.
Abstract in English:Abstract Introduction Statistical data reveal that approximately 140 million radiological exams are performed annually in Brazil. These exams are designed to detect and to analyze fractures, caused by different types of trauma; as well as, to diagnose pathologies such as pulmonary diseases. For better visualization of those lesions or abnormalities, methods of image segmentation can be implemented. Such methods lead to the separation of the region of interest, which allows extracting the characteristics and anomalies of the desired tissue. However, the methods developed by researchers in this area still have restrictions. Consequently, we present an automatic pulmonary segmentation approach that overcomes these constraints. Methods This method is composed of a combination of Discrete Wavelet Packet Frame (DWPF), morphological operations and Gradient Vector Flow (GVF). The methodology is divided into four steps: Pre-processing - the original image is enhanced by discrete wavelet; Processing - where occurs a combination of the Otsu threshold with a series of morphological operations in order to identify the pulmonary object; Post-processing - an innovative form of using GVF improves the binary information of pulmonary tissue, and; Evaluation – the segmented images were evaluated for accuracy of detection the pulmonary region and border. Results The evaluation was carried out by segmenting 247 digital X-ray challenging images of the thorax human. The results show high for values of Overlap (97,63% ± 3.34%), and Average Contour Distance (0.69mm ± 0.95mm). Conclusion The results allow verifying that the proposed technique is robust and more accurate than other methods of lung segmentation, besides being a fully automatic method of lung segmentation.
Abstract in English:Abstract Introduction People with cervical or high thoracic spinal cord injury usually have respiratory muscle weakness. When transcutaneous functional electrical stimulation (TFES) synchronized with the patient’s natural breathing is applied to respiratory muscles, their strength and resistance are increased. In this work, we propose a novel method to perform an automatic synchronization, composed of a signal acquisition system and an algorithm that recognizes both respiratory cycle phases during quiet breathing. Methods The respiratory signal acquisition unit consists of a load cell attached to an elastic belt. The algorithm is based on statistical evaluation and linear approximation for detecting the beginning of both inhalation and exhalation phases. Ten volunteers remained steady, breathing quietly for one minute for signal acquisition. Results The system’s automatic detection of inspiratory events reached 87.5% of true positives, 6.7% of false negatives and 5.8% of false positives. Both hit and error ratios obtained in the detection of expiratory events reached 94.3% true positives, 4.9% false positives and 0.8% false negatives. Conclusion The developed algorithm can identify the respiratory phases properly and it can be used in future synchronized TFES applications whether the patient remains in a quasi-static position during treatment.
Abstract in English:Abstract Introduction The use of the same imaging and quantification techniques in small animals and clinical studies presents the opportunity for direct translational research in drug discovery and development, in neuropharmacological basis of neurological and psychiatric diseases, and in optimization of drug therapy. Thus, positron emission tomography (PET) studies in rodents can bridge the gap between pre-clinical and clinical research. The aim should be to find a method with capability to measure, without compromising accuracy, glucose distribution in the structures of the brain, which can also be used in pathological situations and with applicability for other substances than glucose analogue. Methods This is a systematic review of several assessment techniques available, including visual and quantitative methods that enable the investigation of the transport mechanisms and enzymes involved in glucose metabolism in the brain. In addition to the ex vivo methods, PET with glucose analogues allows in vivo analyses using qualitative, semiquantitative and quantitative methods. Results These techniques provide different results, and the applicability of a specific method is related to the purpose of the study and the multiple factors that may interfere in the process. Conclusion This review provides a solid background of tools and quantification methods for medical physicists and other professionals interested in cerebral glycolytic metabolism quantification in experimental animals. It also addresses the main factors related to animals, equipment and techniques that are used, as well as how these factors should be understood to better interpret the results obtained from experiments.
Abstract in English:Abstract Introduction Solution blow spinning (SBS) and airbrushing are two techniques that can be used as alternatives to electrospinning in the production of fibrous scaffolds for tissue engineering (TE). SBS seems particularly interesting due to its versatility, however, it has not been much explored and no automated SBS systems were found in the literature. Therefore, the present work aimed to develop such equipment and compare the results to those found for airbrushing, considering the same set of parameters. Methods A new SBS set up, composed of a specially designed nozzle with automated movement, a syringe pump and a compressor, was used to produce fibrous poly (ε-caprolactone) (PCL) mats. The airbrushed fibers were produced under the same conditions, and samples of both types of mats were imaged using scanning electron microscopy (SEM) to compare them in terms of microstructure and fiber diameter. Results The SBS system was robust and performed well, in terms of movement and fiber deposition. In comparison to airbrushing’s, SBS mats presented different microstructural characteristics (considering the parameters used). Conclusion The biggest advantage over airbrushing may be its versatility and simple automation, which may improve sample reproducibility, especially considering scaled up processes. To further improve this apparatus, a better understanding of how process variables interfere in the microstructure is needed, as well as more sophisticated interface and operation.
Abstract in English:Abstract Introduction The use of 3D imaging in the medical field has proven to be a benefit to doctors when diagnosing patients. As for different medical applications, 3D visualization systems have advantages in terms of a better spatial understanding of anatomical structures, better performance of tasks that require high level of dexterity, increased learning performance, and improved communications with patients or between doctors. Methods In this technical report, we show how to employ a multi-view autostereoscopic system to provide 3D images without any special glasses or equipment, describing a new way to obtain 3D visualization using sets of 2D images instead of real volumetric data such as magnetic resonance imaging (MRI) or computed tomography (CT). We also propose an application of the images in neuroanatomy. Results We obtained three-dimensional images of anatomical parts for visualization without glasses with resolution of 336 × 210 pixels’. Conclusion The proposed method was able to generate three-dimensional high-resolution images and has great potential to be used in various areas such as anatomy and physiological studies.