Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 46.374
Filtrar
1.
Food Chem ; 302: 125329, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31404874

RESUMO

Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.


Assuntos
Análise de Alimentos/métodos , Aprendizado de Máquina , Azeite de Oliva/análise , Processamento de Sinais Assistido por Computador , Análise Espectral/métodos , Algoritmos , Lasers , Azeite de Oliva/química
2.
Food Chem ; 302: 125345, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31445377

RESUMO

This paper compares the results of standard chemical analytical processes and electrochemical impedance spectroscopy (EIS) in the characterization of different beverages, namely ground coffee, soluble coffee, coffee substitutes, barley, cow milk, vegetable drinks, tea, plant infusions and plant mixtures. For the two approaches, the similarities between the experimental data are assessed by means of the Euclidean and Canberra distances. The resulting information is processed by means of the multidimensional scaling (MDS) clustering and visualization algorithm. The results of the chemical analytical processes and EIS reveal identical clusters for the two adopted distances. Furthermore, the robustness of the experimental and computational scheme are assessed by means of the Procrustes technique. The results confirm the effectiveness of combining the EIS and MDS.


Assuntos
Bebidas/análise , Visualização de Dados , Espectroscopia Dielétrica/métodos , Algoritmos , Animais , Técnicas de Química Analítica/métodos , Análise por Conglomerados , Café/química , Espectroscopia Dielétrica/estatística & dados numéricos , Análise de Alimentos/métodos , Análise de Alimentos/estatística & dados numéricos , Leite/química , Processamento de Sinais Assistido por Computador , Chá/química
3.
Spectrochim Acta A Mol Biomol Spectrosc ; 224: 117339, 2020 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-31344573

RESUMO

An analytical investigation was carried out to study the treatment and amplification of the spectral signals produced by critical concentrations with high accuracy and precision using two advanced approaches. The factorized-spectrum approach was applied through two novel methods which were: absorptivity centering technique via both: factorized zero order absorption spectrum (ACT-FSD0ΔA) and factorized ratio spectrum (ACT-FSRΔP). The proposed methods were found to be linear in the ranges of (15-100 µg/mL) and (3-40 µg/mL) for ASP and MTO, respectively. Those methods were compared to the methods following the geometrical standard addition approach: ratio H-point standard addition method (RHPSAM) and geometrical induced amplitude modulation (GIAM). The approaches were applied for the determination of the minor component metoclopramide in its mixture with the major component aspirin in the challengeable ratio of (1,90) respectively in a white multicomponent system. The results obtained from the proposed approaches were statistically compared with each other. The methods were validated according to ICH guidelines where the results were found to be within the acceptable limits. The methods were found to be accurate and reliable for the determination of metoclopramide critical concentration besides aspirin concentration. The results of single factor ANOVA analysis indicated that there is no significant difference among the developed methods. These methods provided simple resolution of this binary combination from synthetic mixtures and pharmaceutical preparation and can be conveniently adopted for routine quality control analysis.


Assuntos
Preparações Farmacêuticas/análise , Processamento de Sinais Assistido por Computador , Espectrofotometria/métodos , Aspirina/análise , Aspirina/química , Modelos Lineares , Metoclopramida/análise , Metoclopramida/química , Modelos Químicos , Modelos Estatísticos , Preparações Farmacêuticas/química , Reprodutibilidade dos Testes
4.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(6): 911-915, 2019 Dec 25.
Artigo em Chinês | MEDLINE | ID: mdl-31875363

RESUMO

This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Algoritmos , Análise Discriminante , Imaginação , Processamento de Sinais Assistido por Computador
5.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(6): 916-923, 2019 Dec 25.
Artigo em Chinês | MEDLINE | ID: mdl-31875364

RESUMO

The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, ß). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the ß rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.


Assuntos
Depressão , Esquizofrenia , Teorema de Bayes , Eletroencefalografia , Humanos , Processamento de Sinais Assistido por Computador , Máquina de Vetores de Suporte
6.
Adv Exp Med Biol ; 1101: 167-206, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31729676

RESUMO

The theory and implementation of modern cochlear implant are presented in this chapter. Major signal processing strategies of cochlear implants are discussed in detail. Hardware implementation including wireless signal transmission circuit, integrated circuit design of implant circuit, and neural response measurement circuit are provided in the latter part of the chapter. Finally, new technologies that are likely to improve the performance of current cochlear implants are introduced.


Assuntos
Implantes Cocleares , Processamento de Sinais Assistido por Computador , Percepção da Fala , Implante Coclear , Implantes Cocleares/tendências , Humanos , Percepção da Fala/fisiologia
7.
Int J Behav Nutr Phys Act ; 16(1): 84, 2019 10 07.
Artigo em Inglês | MEDLINE | ID: mdl-31590666

RESUMO

BACKGROUND: Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes. METHODS: We used data of 285 participants (RECORD MultiSensor Study, 2013-2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken. RESULTS: Compared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city. CONCLUSIONS: Our approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity.


Assuntos
Acelerometria/métodos , Sistemas de Informação Geográfica , Saúde Pública , Transportes , Caminhada/fisiologia , Humanos , Processamento de Sinais Assistido por Computador , Transportes/métodos , Transportes/estatística & dados numéricos
8.
Zhongguo Yi Liao Qi Xie Za Zhi ; 43(5): 318-321, 2019 Sep 30.
Artigo em Chinês | MEDLINE | ID: mdl-31625325

RESUMO

In order to diagnose and evaluate the human spinal lesions through the paravertebral muscles, a paravertebral muscle monitoring system based on surface EMG signals was designed. The system used surface mount electrodes to obtain the surface myoelectric signal (sEMG) of paravertebral muscle. The signal was filtered and amplified by the conditioning circuit. The signal was collected by the microcontroller NRF52832 and was sent to the mobile APP. After the signal was preprocessed by the wavelet threshold denoising algorithm in APP, the time and frequency characteristics of the sEMG signal reflecting the functional state of the muscle were extracted. The calculated characteristic parameters was displayed in real time in the application interface. The experimental results show that the system meets the design requirements in analog signal acquisition, digital processing of signals and calculation of characteristic parameters. The system has certain application value.


Assuntos
Algoritmos , Computadores , Eletromiografia , Eletrodos , Eletromiografia/instrumentação , Humanos , Monitorização Fisiológica , Músculo Esquelético , Processamento de Sinais Assistido por Computador
9.
Zhongguo Yi Liao Qi Xie Za Zhi ; 43(5): 337-340, 2019 Sep 30.
Artigo em Chinês | MEDLINE | ID: mdl-31625330

RESUMO

The paper describes how to develop a digital heart sound signal detection device based on high gain MEMS MIC that can accurately collect and store human heart sounds. According to the method of collecting heart sound signal by traditional stethoscope, the system improves the traditional stethoscope, and a composite probe equipped with a MEMS microphone sensor is designed. The MEMS microphone sensor converts the sound pressure signal into a voltage signal, and then amplifies, converts with Sigma Delta, extracts and filters the collected signal. After the heart sound signal is uploaded to the PC, the Empirical Mode Decomposition (EMD) is carried out to reconstruct the signal, and then the Independent Component Analysis (ICA) method is used for blind source separation and finally the heart rate is calculated by autocorrelation analysis. At the end of the paper, a preliminary comparative analysis of the performance of the system was carried out, and the accuracy of the heart sound signal was verified.


Assuntos
Ruídos Cardíacos , Sistemas Microeletromecânicos , Estetoscópios , Coração , Humanos , Processamento de Sinais Assistido por Computador
10.
Zhongguo Yi Liao Qi Xie Za Zhi ; 43(5): 341-344, 2019 Sep 30.
Artigo em Chinês | MEDLINE | ID: mdl-31625331

RESUMO

OBJECTIVE: A method for dynamically collecting and processing ECG signals was designed to obtain classification information of abnormal ECG signals. METHODS: Firstly, the ECG eigenvectors were acquired by real-time acquisition of ECG signals combined with discrete wavelet transform, and then the ECG fuzzy information entropy was calculated. Finally, the Euclidean distance was used to obtain the semantic distance of ECG signals, and the classification information of abnormal signals was obtained. RESULTS: The device could effectively identify abnormal ECG signals on an embedded platform based on the Internet of Things, and improved the diagnosis accuracy of heart diseases. CONCLUSIONS: The fuzzy diagnosis device of ECG signal could accurately classify the abnormal signal and output an online signal classification matrix with a high confidence interval.


Assuntos
Eletrocardiografia , Cardiopatias , Algoritmos , Arritmias Cardíacas , Lógica Fuzzy , Cardiopatias/diagnóstico , Humanos , Internet , Processamento de Sinais Assistido por Computador , Análise de Ondaletas
11.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(5): 834-840, 2019 Oct 25.
Artigo em Chinês | MEDLINE | ID: mdl-31631633

RESUMO

In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.


Assuntos
Balistocardiografia , Frequência Cardíaca , Processamento de Sinais Assistido por Computador , Algoritmos , Humanos
12.
PLoS Comput Biol ; 15(9): e1007091, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31525179

RESUMO

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Córtex Sensório-Motor/fisiologia , Fala/fisiologia , Eletrocorticografia , Humanos , Processamento de Sinais Assistido por Computador
13.
Accid Anal Prev ; 133: 105296, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31563015

RESUMO

Risky driving states such as aggressive driving and unstable driving are the cause of many traffic accidents. Many studies have used either driving data or physiological data such as electroencephalography (EEG) to estimate and monitor driving states. However, few studies made comparison among those driving-feature-based, EEG-feature-based and hybrid-feature-based (combination of driving features and EEG features) models. Further, limited types of EEG features have been extracted and investigated in the existing studies. To fill these research gaps aforementioned, this study adopts two EEG analysis techniques (i.e., independent component analysis and brain source localization), two signal processing methods (i.e., power spectrum analysis and wavelets analysis) to extract twelve kinds of EEG features for the short-term driving state prediction. The prediction performance of driving features, EEG features and hybrid features of them was evaluated and compared. The results indicated that EEG-based model has better performance than driving-data-based model (i.e., 83.84% versus 71.59%) and the integrated model of driving features and the full brain regions features extracted by wavelet analysis outperforms other types of features with the highest accuracy of 86.27%.


Assuntos
Direção Agressiva/psicologia , Encéfalo/fisiologia , Eletroencefalografia/métodos , Processamento de Sinais Assistido por Computador/instrumentação , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Análise Espectral , Análise de Ondaletas
14.
IEEE J Biomed Health Inform ; 23(6): 2265-2275, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31478879

RESUMO

Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study explored from physiological and behavioral perspectives simultaneously and fused pervasive electroencephalography (EEG) and vocal signals to make the detection of depression more objective, effective and convenient. After extraction of several effective features for these two types of signals, we trained six representational classifiers on each modality, then denoted diversity and correlation of decisions from different classifiers using co-decision tensor and combined these decisions into the ultimate classification result with multi-agent strategy. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed multi-modal depression detection strategy is superior to the single-modal classifiers or other typical late fusion strategies in accuracy, f1-score and sensitivity. This work indicates that late fusion of pervasive physiological and behavioral signals is promising for depression detection and the multi-agent strategy can take advantage of diversity and correlation of different classifiers effectively to gain a better final decision.


Assuntos
Depressão/diagnóstico , Eletroencefalografia/métodos , Processamento de Sinais Assistido por Computador , Espectrografia do Som/métodos , Fala/classificação , Algoritmos , Feminino , Humanos , Masculino
15.
IEEE J Biomed Health Inform ; 23(6): 2276-2285, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31478880

RESUMO

Mental illnesses are vast and cause a lot of individual and social discomfort, with significant healthcare costs associated in terms of diagnosis and treatment. They can be triggered by a number of factors including stress, fatigue or anxiety. The associated physiological, cardiac and autonomic changes can be assessed, centrally, through brain imaging or, peripherally, by other signal recording modalities. With recent advances in wearable devices, these parameters can now be assessed in natural living conditions as associated mood disorders such as obsessive/compulsive behavior or depression are difficult to be examined in controlled settings. In this paper, we present a low-powered and flexible device with electrocardiogram (ECG), galvanic skin response (GSR), temperature and bio-motion detection channels, with signal accuracies of 62 µV for ECG, 6.6 kΩ for GSR, 0.13 °C for temperature and 0.04 g for acceleration. Potential applications include mental health assessment of patients during daily activities at home and/or work through non-continuous and multimodal sensing as demonstrated in this paper during exercise, rest and mental activities performed by healthy individuals only, achieving an overall accuracy of 89% in the classification of the different tasks executed by volunteers.


Assuntos
Resposta Galvânica da Pele/fisiologia , Frequência Cardíaca/fisiologia , Transtornos Mentais , Monitorização Fisiológica/instrumentação , Dispositivos Eletrônicos Vestíveis , Adulto , Algoritmos , Eletrocardiografia/instrumentação , Desenho de Equipamento , Humanos , Masculino , Transtornos Mentais/diagnóstico , Transtornos Mentais/fisiopatologia , Transtornos Mentais/terapia , Monitorização Fisiológica/métodos , Movimento/fisiologia , Processamento de Sinais Assistido por Computador/instrumentação , Termometria/instrumentação
16.
Appl Opt ; 58(20): 5540-5546, 2019 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-31504024

RESUMO

In this paper, a vibration-displacement immunization model is proposed to measure the free spectral range of a resonant cavity by using a laser self-mixing velocimeter. The validity of this method is demonstrated by the experimental results, which can effectively get rid of low measurement accuracy related to the self-mixing vibration system due to the vibratory displacement. According to the periodic waveform separation characteristic of the self-mixing velocity signal, the free spectral range of a multilongitudinal mode diode laser is calculated to be 88.24 GHz. Moreover, the influences of different target velocities and signal sampling frequencies on the free spectral range have been analyzed in detail from the theoretical analysis. In the case of high signal sampling rate and low velocity, from which the undistorted velocity signal waveform at the integral order external cavity mode keeps stable, it is possible to obtain relatively accurate measured results.


Assuntos
Imunização , Lasers , Modelos Teóricos , Reologia/instrumentação , Vibração , Simulação por Computador , Processamento de Sinais Assistido por Computador
17.
Rev Sci Instrum ; 90(8): 085003, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31472627

RESUMO

Atomic magnetometers (AMs) offer many advantages over superconducting quantum interference devices due to, among other things, having comparable sensitivity while not requiring cryogenics. One of the major limitations of AMs is the challenge of configuring them as gradiometers. We report the development of a spin-exchange relaxation free vector atomic magnetic gradiometer with a sensitivity of 3 fT cm-1 Hz-1/2 and common mode rejection ratio >150 in the band from DC to 100 Hz. We introduce a background suppression figure of merit for characterizing the performance of gradiometers. It allows for optimally setting the measurement baseline and for quickly assessing the advantage, if any, of performing a measurement in a gradiometric mode. As an application, we consider the problem of fetal magnetocardiography (fMCG) detection in the presence of a large background maternal MCG signal.


Assuntos
Feto/fisiologia , Magnetocardiografia/instrumentação , Calibragem , Desenho de Equipamento , Feminino , Humanos , Mães , Processamento de Sinais Assistido por Computador
18.
J Electromyogr Kinesiol ; 48: 176-186, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31401341

RESUMO

Transcutaneous electromyography (tc-EMG) has been used to measure the electrical activity of respiratory muscles during inspiration in various studies. Processing the raw tc-EMG signal of these inspiratory muscles has shown to be difficult as baseline noise, cardiac interference, cross-talk and motion artefacts can influence the signal quality. In this review we will discuss the most important sources of signal noise in tc-EMG of respiratory muscles and the various techniques described to suppress or reduce this signal noise. Furthermore, we will elaborate on the options available to develop or improve an algorithm that can be used to guide the approach for analysis of tc-EMG signals of inspiratory muscles in future research.


Assuntos
Eletromiografia/métodos , Músculos Respiratórios/fisiologia , Eletromiografia/normas , Humanos , Processamento de Sinais Assistido por Computador
19.
J Clin Neurosci ; 69: 184-189, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31409548

RESUMO

OBJECTIVES: The combined use of perfusion neuroimaging and brain oscillatory activity may provide a better clinical picture of neurovascular coupling of the injured area in ischemic stroke. The aim is to assess stroke-related topographic electroencephalography (EEG) changes during the earliest phase of ischemic stroke and to compare them with hypoperfusion identified by computer tomography perfusion (CTP). PATIENTS AND METHODS: The study included 15 patients with ischemic stroke, who underwent both CTP and EEG recording within 4.5 h. Topographic representation of power for each band was calculated and compared with hypoperfusion areas estimated by CTP maps. RESULTS: Predominance of slow delta frequencies was found in all patients. The main finding is the agreement between slow rhythms hemispheric prevalence on EEG maps and cerebral hypoperfusion area identified using CTP. CONCLUSION: The results of this preliminary study show that the combined use of EEG and CTP, as highly available techniques, in acute ischemic stroke may be helpful in clinical practice and provide information about functional and metabolic aspects of brain involvement. The joint use of these methodologies may give a better clinical insight of the functionality of injured area in the hyperacute phase.


Assuntos
Isquemia Encefálica/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Neuroimagem/métodos , Acoplamento Neurovascular/fisiologia , Acidente Vascular Cerebral/diagnóstico por imagem , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Encéfalo/fisiopatologia , Isquemia Encefálica/fisiopatologia , Eletroencefalografia , Feminino , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Masculino , Pessoa de Meia-Idade , Imagem de Perfusão/métodos , Estudos Retrospectivos , Processamento de Sinais Assistido por Computador , Acidente Vascular Cerebral/fisiopatologia , Tomografia Computadorizada por Raios X/métodos
20.
Ultrasonics ; 99: 105966, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31394481

RESUMO

The acoustic microscope is a powerful tool for the observation of biological matters. Non-invasive in-situ observation can be performed without any staining process. Acoustic microscopy is contrasted by elastic parameters like sound speed and acoustic impedance. We have proposed an acoustic microscope that can acquire three-dimensional acoustic impedance profile. The technique was applied to cell-size observation. Glial cells were cultured on a 70 µm-thick polypropylene film substrate. A highly focused ultrasound beam was transmitted from the rear side of the substrate, and the reflection was received by the same transducer. An acoustic pulse, its spectrum spreading briefly 100 through 450 MHz, was transmitted. By analyzing the internal reflections in the cell, the distribution of characteristic acoustic impedance along the beam direction was determined. Three-dimensional acoustic impedance mapping was realized by scanning the transducer, exhibiting the intra-cellular structure including nucleus and cytoskeleton.


Assuntos
Imagem Tridimensional , Microscopia Acústica/métodos , Neuroglia , Análise de Célula Única/métodos , Animais , Células Cultivadas , Impedância Elétrica , Ratos , Processamento de Sinais Assistido por Computador , Transdutores
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA