6 resultados para NONINVASIVE MONITORIZATION
em Universidade Federal do Rio Grande do Norte(UFRN)
Resumo:
The human voice is an important communication tool and any disorder of the voice can have profound implications for social and professional life of an individual. Techniques of digital signal processing have been used by acoustic analysis of vocal disorders caused by pathologies in the larynx, due to its simplicity and noninvasive nature. This work deals with the acoustic analysis of voice signals affected by pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The purpose of this work is to develop a classification system of voices to help pre-diagnosis of pathologies in the larynx, as well as monitoring pharmacological treatments and after surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients (MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT) are applied to extract relevant characteristics of the voice signal. For the classification task is used the Support Vector Machine (SVM), which aims to build optimal hyperplanes that maximize the margin of separation between the classes involved. The hyperplane generated is determined by the support vectors, which are subsets of points in these classes. According to the database used in this work, the results showed a good performance, with a hit rate of 98.46% for classification of normal and pathological voices in general, and 98.75% in the classification of diseases together: edema and nodules
Resumo:
The strength of respiratory muscle are frequently assessed by maximal inspiratory and expiratory pressure, however, the maneuvers to assess PImax and PEmax are difficult for many patients. The sniff nasal inspiratory pressure (SNIP) is a simple and noninvasive technique use to assess inspiratory muscles strength. Reference values have been previous established for SNIP in adults but no previous studies have provided reference values for SNIP in adult Brazilian population. The main objective of this study were propose reference values of SNIP for Brazilian population through establishment of relationship between anthropometric measurements, physical activity profile and SNIP and at the same time compare the values obtained with reference values previously published. We studied 117 subjects (59 male and 58 female) distributed in different age grouped 20-80 years old. The results showed on significant positive relationship between SNIP and height and negative correlation with age (p<0.05). In the multiple linear regression analysis only age continued to have an independent predictive role for the two dependent variables that correlated with SNIP. The values of SNIP found in Brazilian population were higher when compared with predict values of previous studies. The results of this study provide reference equations of SNIP for health Brazilian population from 20 to 80 years old
Resumo:
To analyze the effects of electrical stimulation at two frequencies on the EMG parameters (EMG) and dynamometer, in muscles with different typing. MATERIALS AND METHODS: This is a controlled clinical trial, randomized and double blind. Sixty healthy volunteers (23.6 ± 4.2anos; 54.2 ± 7.7kg, 1.62 ± 0.009 cm) of both sexes were divided randomly into three groups: control group (CG), experimental group 1 (SG1) with application of the current Russian 30 HZ and experimental group 2 (EG2) at 70 Hz The volunteers performed an initial assessment (AV1) on the isokinetic dynamometer with three repetitions maximum voluntary isometric (MVC) for knee extension concomitant uptake of EMG for the VM muscle, VL and RF. Later, after application of NMES, they underwent an experimental protocol of isometric fatigue using 70% of MVIC, ending with the completion of a final assessment (AV2) in the same manner as the AV1. RESULTS: By analyzing the profile of the 60 subjects in three broad, VM showed a higher value of RMS behavior when the VL and RF (p = 0.03 and p = 0.02). With respect to Fmed the RF muscle (p = 0.001) showed a higher value for the VM. The VM muscle showed significant increases of Fmed (p = 0.05) after electrical stimulation at 70 Hz when compared the AV1 AV2 and RF showed significant decreases (p = 0.009) after stimulation at 30 Hz during the fatigue showed an increase RMS in the VM and VL, with a reduction in RF. For the variable Fmed was observed in three broad decline during fatigue. CONCLUSION: Our findings provide evidence that the muscles VM, VL and RF fiber typing are different besides indicating that the frequency of NMES tend to relate to the muscle stimulated. Finally suggests the surface EMG as a noninvasive method for characterizing muscle
Resumo:
Recently, genetically encoded optical indicators have emerged as noninvasive tools of high spatial and temporal resolution utilized to monitor the activity of individual neurons and specific neuronal populations. The increasing number of new optogenetic indicators, together with the absence of comparisons under identical conditions, has generated difficulty in choosing the most appropriate protein, depending on the experimental design. Therefore, the purpose of our study was to compare three recently developed reporter proteins: the calcium indicators GCaMP3 and R-GECO1, and the voltage indicator VSFP butterfly1.2. These probes were expressed in hippocampal neurons in culture, which were subjected to patchclamp recordings and optical imaging. The three groups (each one expressing a protein) exhibited similar values of membrane potential (in mV, GCaMP3: -56 ±8.0, R-GECO1: -57 ±2.5; VSFP: -60 ±3.9, p = 0.86); however, the group of neurons expressing VSFP showed a lower average of input resistance than the other groups (in Mohms, GCaMP3: 161 ±18.3; GECO1-R: 128 ±15.3; VSFP: 94 ±14.0, p = 0.02). Each neuron was submitted to current injections at different frequencies (10 Hz, 5 Hz, 3 Hz, 1.5 Hz, and 0.7 Hz) and their fluorescence responses were recorded in time. In our study, only 26.7% (4/15) of the neurons expressing VSFP showed detectable fluorescence signal in response to action potentials (APs). The average signal-to-noise ratio (SNR) obtained in response to five spikes (at 10 Hz) was small (1.3 ± 0.21), however the rapid kinetics of the VSFP allowed discrimination of APs as individual peaks, with detection of 53% of the evoked APs. Frequencies below 5 Hz and subthreshold signals were undetectable due to high noise. On the other hand, calcium indicators showed the greatest change in fluorescence following the same protocol (five APs at 10 Hz). Among the GCaMP3 expressing neurons, 80% (8/10) exhibited signal, with an average SNR value of 21 ±6.69 (soma), while for the R-GECO1 neurons, 50% (2/4) of the neurons had signal, with a mean SNR value of 52 ±19.7 (soma). For protocols at 10 Hz, 54% of the evoked APs were detected with GCaMP3 and 85% with R-GECO1. APs were detectable in all the analyzed frequencies and fluorescence signals were detected from subthreshold depolarizations as well. Because GCaMP3 is the most likely to yield fluorescence signal and with high SNR, some experiments were performed only with this probe. We demonstrate that GCaMP3 is effective in detecting synaptic inputs (involving Ca2+ influx), with high spatial and temporal resolution. Differences were also observed between the SNR values resulting from evoked APs, compared to spontaneous APs. In recordings of groups of cells, GCaMP3 showed clear discrimination between activated and silent cells, and reveals itself as a potential tool in studies of neuronal synchronization. Thus, our results indicate that the presently available calcium indicators allow detailed studies on neuronal communication, ranging from individual dendritic spines to the investigation of events of synchrony in neuronal networks genetically defined. In contrast, studies employing VSFPs represent a promising technology for monitoring neural activity and, although still to be improved, they may become more appropriate than calcium indicators, since neurons work on a time scale faster than events of calcium may foresee
Resumo:
The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.
Resumo:
The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.