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1.
PLoS Comput Biol ; 16(4): e1007662, 2020 04.
Artículo en Inglés | MEDLINE | ID: mdl-32352973

RESUMEN

Alpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing a different degree of alpha blocking upon opening of their eyes. Though it has been notoriously difficult to estimate parameters by fitting such models, we show how, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral differences between states: Changes to just a single parameter, pei, corresponding to the strength of a tonic excitatory input to the inhibitory cortical population, are sufficient to explain the reduction in alpha rhythm upon opening of the eyes. We detect this by comparing the shift in each model parameter between eyes-closed and eyes-open states. Whereas changes in most parameters are weak or negligible and do not scale with the degree of alpha attenuation across subjects, the change in pei increases monotonically with the degree of alpha blocking observed. These results indicate that opening of the eyes reduces alpha activity by increasing external input to the inhibitory cortical population.


Asunto(s)
Ritmo alfa , Electroencefalografía , Procesamiento de Señales Asistido por Computador , Atención , Mapeo Encefálico , Humanos , Modelos Neurológicos , Neuronas/fisiología , Distribución Normal
2.
PLoS One ; 15(5): e0231767, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32459820

RESUMEN

Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity.


Asunto(s)
Electroencefalografía/métodos , Desempeño Psicomotor/fisiología , Procesamiento de Señales Asistido por Computador , Encéfalo/fisiología , Mapeo Encefálico/métodos , Femenino , Humanos , Masculino , Corteza Motora/fisiología , Neuronas/fisiología , Adulto Joven
3.
PLoS One ; 15(5): e0232769, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32392232

RESUMEN

The end timing of T waves in fetal electrocardiogram (fECG) is important for the evaluation of ST and QT intervals which are vital markers to assess cardiac repolarization patterns. Monitoring malignant fetal arrhythmias in utero is fundamental to care in congenital heart anomalies preventing perinatal death. Currently, reliable detection of end of T waves is possible only by using fetal scalp ECG (fsECG) and fetal magnetocardiography (fMCG). fMCG is expensive and less accessible and fsECG is an invasive technique available only during intrapartum period. Another safer and affordable alternative is the non-invasive fECG (nfECG) which can provide similar assessment provided by fsECG and fMECG but with less accuracy (not beat by beat). Detection of T waves using nfECG is challenging because of their low amplitudes and high noise. In this study, a novel model-based method that estimates the end of T waves in nfECG signals is proposed. The repolarization phase has been modeled as the discharging phase of a capacitor. To test the model, fECG signals were collected from 58 pregnant women (age: (34 ± 6) years old) bearing normal and abnormal fetuses with gestational age (GA) 20-41 weeks. QT and QTc intervals have been calculated to test the level of agreement between the model-based and reference values (fsECG and Doppler Ultrasound (DUS) signals) in normal subjects. The results of the test showed high agreement between model-based and reference values (difference < 5%), which implies that the proposed model could be an alternative method to detect the end of T waves in nfECG signals.


Asunto(s)
Electrocardiografía , Feto/diagnóstico por imagen , Modelos Teóricos , Procesamiento de Señales Asistido por Computador , Adulto , Algoritmos , Femenino , Humanos , Embarazo
4.
Sensors (Basel) ; 20(9)2020 Apr 27.
Artículo en Inglés | MEDLINE | ID: mdl-32349382

RESUMEN

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR- UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate's health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.


Asunto(s)
Frecuencia Cardíaca , Monitoreo Fisiológico/métodos , Radar , Frecuencia Respiratoria , Procesamiento de Señales Asistido por Computador , Telemedicina , Infecciones por Coronavirus/diagnóstico , Humanos , Modelos Teóricos , Monitoreo Fisiológico/instrumentación , Pandemias , Neumonía Viral/diagnóstico , Ondas de Radio
5.
Zhongguo Yi Liao Qi Xie Za Zhi ; 44(1): 28-32, 2020 Jan 08.
Artículo en Chino | MEDLINE | ID: mdl-32343062

RESUMEN

This study describes the development of a wireless and wearable ECG monitoring system with ultra-low power consumption. The system is mainly composed of a connection part of an ECG electrode sticker, an electrocardiogram collecting part, a data storage part, a Bluetooth main control unit, a charging module, a voltage regulator and a lithium battery. The low-power ECG acquisition chip ADS1292R and the ultra-low-power Bluetooth microcontroller nRF51822 together constitute the ECG signal acquisition and wireless data communication part. The collected ECG signals can be sent to the mobile APP through the Bluetooth function provided by the MCU, and can completly display and analysis to achieve low power system. After testing, the system power consumption is only (3.7 V×2.87 mA)10.619 mW, and if it is optimized, it can further reduce power consumption, therefore, the system design can have good applicability.


Asunto(s)
Suministros de Energía Eléctrica , Electrocardiografía , Dispositivos Electrónicos Vestibles , Tecnología Inalámbrica , Diseño de Equipo , Monitoreo Fisiológico/instrumentación , Procesamiento de Señales Asistido por Computador
6.
PLoS Biol ; 18(4): e3000491, 2020 04.
Artículo en Inglés | MEDLINE | ID: mdl-32282798

RESUMEN

Nervous systems exploit regularities in the sensory environment to predict sensory input, adjust behavior, and thereby maximize fitness. Entrainment of neural oscillations allows retaining temporal regularities of sensory information, a prerequisite for prediction. Entrainment has been extensively described at the frequencies of periodic inputs most commonly present in visual and auditory landscapes (e.g., >0.5 Hz). An open question is whether neural entrainment also occurs for regularities at much longer timescales. Here, we exploited the fact that the temporal dynamics of thermal stimuli in natural environment can unfold very slowly. We show that ultralow-frequency neural oscillations preserved a long-lasting trace of sensory information through neural entrainment to periodic thermo-nociceptive input as low as 0.1 Hz. Importantly, revealing the functional significance of this phenomenon, both power and phase of the entrainment predicted individual pain sensitivity. In contrast, periodic auditory input at the same ultralow frequency did not entrain ultralow-frequency oscillations. These results demonstrate that a functionally significant neural entrainment can occur at temporal scales far longer than those commonly explored. The non-supramodal nature of our results suggests that ultralow-frequency entrainment might be tuned to the temporal scale of the statistical regularities characteristic of different sensory modalities.


Asunto(s)
Encéfalo/fisiología , Percepción del Dolor/fisiología , Dolor/fisiopatología , Estimulación Acústica , Adulto , Electroencefalografía , Femenino , Humanos , Rayos Láser , Masculino , Dolor/psicología , Dimensión del Dolor , Procesamiento de Señales Asistido por Computador
7.
Nucleic Acids Res ; 48(10): 5217-5234, 2020 06 04.
Artículo en Inglés | MEDLINE | ID: mdl-32338745

RESUMEN

As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.


Asunto(s)
Algoritmos , Metagenómica/métodos , Probabilidad , Procesamiento de Señales Asistido por Computador , Humanos , Metagenoma/genética
8.
Nat Commun ; 11(1): 1946, 2020 04 23.
Artículo en Inglés | MEDLINE | ID: mdl-32327635

RESUMEN

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.


Asunto(s)
Encéfalo/fisiopatología , Electroencefalografía/métodos , Epilepsias Parciales/fisiopatología , Neuroimagen Funcional/métodos , Algoritmos , Encéfalo/patología , Encéfalo/cirugía , Simulación por Computador , Fenómenos Electromagnéticos , Epilepsias Parciales/patología , Epilepsias Parciales/cirugía , Humanos , Reproducibilidad de los Resultados , Procesamiento de Señales Asistido por Computador
9.
J. eletrocardiol ; 59: 1-29, Mar-Abr., 2020. tab., graf.
Artículo en Inglés | Sec. Est. Saúde SP, SESSP-IDPCPROD, Sec. Est. Saúde SP | ID: biblio-1095567

RESUMEN

INTRODUCTION: The vectorcardiography (VCG) is a method of representing the heart's electrical activity in three dimensions that is not frequently used in clinical practice due to the higher complexity compared to electrocardiography (ECG). A way around this problem was the development of regression techniques to obtain the VCG from the 12 lead ECG and the evaluation of these techniques is done by comparing the parameters obtained by the gold standard method and by the VCG obtained by the alternative methods. In this paper it is proposed instead a comparison between the images of the VCG planes using the values returned by digital image processing metrics such as PSNR, SSIM and PW-SSIM. METHODS: The signals used were obtained from the Physikalisch-Technische Bundesanstalt Diagnostic ECG Database, which contains both the VCGs obtained by the gold standard method and the 12 lead ECG signals. They were divided into five groups that contained a control group and according to the region of the wall infarction. The ECG signals were then filtered using a Butterworth Finite Impulse Response bandpass filter, with cutoff frequencies of 3 Hz and 45 Hz and then the VCGs were by a computer application using the Kors inverse matrix method, the Kors quasi-orthogonal method and the Dower Inverse Matrix method. The reconstructed signals were then compared using the PSNR, SSIM and PW-SSIM methods. The returned values were presented in tables for each group containing the average value and standard deviance for each method in each VCG plane. RESULTS: Using image processing techniques, it was possible to perceive that the alternative methods to obtain the VCG have a high confiability that could be compared to the gold standard in signals from healthy subjects. However, signals from pathological subjects present variations that could be caused by a deficit of these alternative methods to represent the pathology in these cases. Considering the PW-SSIM, the frontal plane by the reconstructions was considered the most similar to the gold standard, having PW-SSIM values higher than 0.93 and for the horizontal plane two groups had PW-SSIM values lower than 0.90 and for the Sagittal plane all groups had values lower than this value. DISCUSSION: The values yielded by the PSNR and SSIM had low variance, worsening the perception of the effect of the reconstruction method used or the infarction effect over the reconstruction. The values lower than 0.90 could indicate that these planes have their generation most affected by the infarction. CONCLUSION: The three methods of obtaining the VCG Frank leads, the Kors Quasi-Orthogonal method, the Kors Linear Regression method and the Dower Inverse Matrix, presented differences in the metrics: PSNR, SSIM and PW-SSIM in normal subjects according to the planes frontal, horizontal and sagittal and in subjects with Myocardial Infarction according to its topography: anterior, inferolateral, inferior or multiarterials. Considering only the PW-SSIM, the QO method had the best performance in different signals, followed by the Dower method. (AU)


Asunto(s)
Vectorcardiografía/tendencias , Procesamiento de Señales Asistido por Computador
10.
Anesth Analg ; 130(5): 1244-1254, 2020 05.
Artículo en Inglés | MEDLINE | ID: mdl-32287131

RESUMEN

BACKGROUND: Cardiovascular waveforms contain information for clinical diagnosis. By learning and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimensional structure and display it as a novel 3-dimensional (3D) image. We hypothesize that the shape of this structure conveys clinically relevant inner dynamics information. METHODS: To validate this hypothesis, we investigate the electrocardiography (ECG) waveform for ischemic heart disease and arterial blood pressure (ABP) waveform in dynamic vasoactive episodes. We model each beat or pulse to be a point lying on a manifold-like a surface-and use the diffusion map (DMap) to establish the relationship among those pulses. The output of the DMap is converted to a 3D image for visualization. For ECG datasets, first we analyzed the non-ST-elevation ECG waveform distribution from unstable angina to healthy control in the 3D image, and we investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG waveform changes. For ABP datasets, we analyzed waveforms collected under endotracheal intubation and administration of vasodilator. To quantify the dynamic separation, we applied the support vector machine (SVM) analysis and reported the total accuracy and macro-F1 score. We further performed the trajectory analysis and derived the moving direction of successive beats (or pulses) as vectors in the high-dimensional space. RESULTS: For the non-ST-elevation ECG, a hierarchical tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented in the 3D image (accuracy = 97.6%, macro-F1 = 96.1%). The DMap helps quantify and visualize the evolving direction of intraoperative ST-elevation myocardial episode in a 1-hour period (accuracy = 97.58%, macro-F1 = 96.06%). The ABP waveform analysis of Nicardipine administration shows interindividual difference (accuracy = 95.01%, macro-F1 = 96.9%) and their common directions from intraindividual moving trajectories. The dynamic change of the ABP waveform during endotracheal intubation shows a loop-like trajectory structure, which can be further divided using the manifold learning knowledge obtained from Nicardipine. CONCLUSIONS: The DMap and the generated 3D image of ECG or ABP waveforms provides clinically relevant inner dynamics information. It provides clues of acute coronary syndrome diagnosis, shows clinical course in myocardial ischemic episode, and reveals underneath physiological mechanism under stress or vasodilators.


Asunto(s)
Bases de Datos Factuales , Electrocardiografía/métodos , Frecuencia Cardíaca/fisiología , Imagenología Tridimensional/métodos , Aprendizaje Automático no Supervisado , Análisis de Ondículas , Humanos , Procesamiento de Señales Asistido por Computador
11.
PLoS One ; 15(4): e0230853, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32271781

RESUMEN

Variation of information in the firing rate of neural population, as reflected in different frequency bands of electroencephalographic (EEG) time series, provides direct evidence for change in neural responses of the brain to hypnotic suggestibility. However, realization of an effective biomarker for spiking behaviour of neural population proves to be an elusive subject matter with its impact evident in highly contrasting results in the literature. In this article, we took an information-theoretic stance on analysis of the EEG time series of the brain activity during hypnotic suggestions, thereby capturing the variability in pattern of brain neural activity in terms of its information content. For this purpose, we utilized differential entropy (DE, i.e., the average information content in a continuous time series) of theta, alpha, and beta frequency bands of fourteen-channel EEG time series recordings that pertain to the brain neural responses of twelve carefully selected high and low hypnotically suggestible individuals. Our results show that the higher hypnotic suggestibility is associated with a significantly lower variability in information content of theta, alpha, and beta frequencies. Moreover, they indicate that such a lower variability is accompanied by a significantly higher functional connectivity (FC, a measure of spatiotemporal synchronization) in the parietal and the parieto-occipital regions in the case of theta and alpha frequency bands and a non-significantly lower FC in the central region's beta frequency band. Our results contribute to the field in two ways. First, they identify the applicability of DE as a unifying measure to reproduce the similar observations that are separately reported through adaptation of different hypnotic biomarkers in the literature. Second, they extend these previous findings that were based on neutral hypnosis (i.e., a hypnotic procedure that involves no specific suggestions other than those for becoming hypnotized) to the case of hypnotic suggestions, thereby identifying their presence as a potential signature of hypnotic experience.


Asunto(s)
Encéfalo/fisiología , Electroencefalografía , Hipnosis , Procesamiento de Señales Asistido por Computador , Adulto , Entropía , Femenino , Humanos , Masculino
12.
PLoS One ; 15(4): e0231698, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32324752

RESUMEN

Thermosensation is crucial for humans to probe the environment and detect threats arising from noxious heat or cold. Over the last years, EEG frequency-tagging using long-lasting periodic radiant heat stimulation has been proposed as a means to study the cortical processes underlying tonic heat perception. This approach is based on the notion that periodic modulation of a sustained stimulus can elicit synchronized periodic activity in the neuronal populations responding to the stimulus, known as a steady-state response (SSR). In this paper, we extend this approach using a contact thermode to generate both heat- and cold-evoked SSRs. Furthermore, we characterize the temporal dynamics of the elicited responses, relate these dynamics to perception, and assess the effects of displacing the stimulated skin surface to gain insight on the heat- and cold-sensitive afferents conveying these responses. Two experiments were conducted in healthy volunteers. In both experiments, noxious heat and innocuous cool stimuli were applied during 75 seconds to the forearm using a Peltier-based contact thermode, with intensities varying sinusoidally at 0.2 Hz. Displacement of the thermal stimulation on the skin surface was achieved by independently controlling the Peltier elements of the thermal probe. Continuous intensity ratings to sustained heat and cold stimulation were obtained in the first experiment with 14 subjects, and the EEG was recorded in the second experiment on 15 subjects. Both contact heat and cool stimulation elicited periodic EEG responses and percepts. Compared to heat stimulation, the responses to cool stimulation had a lower magnitude and shorter latency. All responses tended to habituate along time, and this response attenuation was most pronounced for cool compared to warm stimulation, and for stimulation delivered using a fixed surface compared to a variable surface.


Asunto(s)
Frío , Electroencefalografía , Calor , Percepción , Procesamiento de Señales Asistido por Computador , Sensación Térmica/fisiología , Adulto , Ritmo alfa/fisiología , Análisis de Varianza , Femenino , Habituación Psicofisiológica , Humanos , Masculino , Factores de Tiempo , Adulto Joven
13.
PLoS Biol ; 18(3): e3000625, 2020 03.
Artículo en Inglés | MEDLINE | ID: mdl-32119658

RESUMEN

Working memory (WM) is important to maintain information over short time periods to provide some stability in a constantly changing environment. However, brain activity is inherently dynamic, raising a challenge for maintaining stable mental states. To investigate the relationship between WM stability and neural dynamics, we used electroencephalography to measure the neural response to impulse stimuli during a WM delay. Multivariate pattern analysis revealed representations were both stable and dynamic: there was a clear difference in neural states between time-specific impulse responses, reflecting dynamic changes, yet the coding scheme for memorised orientations was stable. This suggests that a stable subcomponent in WM enables stable maintenance within a dynamic system. A stable coding scheme simplifies readout for WM-guided behaviour, whereas the low-dimensional dynamic component could provide additional temporal information. Despite having a stable subspace, WM is clearly not perfect-memory performance still degrades over time. Indeed, we find that even within the stable coding scheme, memories drift during maintenance. When averaged across trials, such drift contributes to the width of the error distribution.


Asunto(s)
Memoria a Corto Plazo/fisiología , Procesamiento de Señales Asistido por Computador , Adulto , Encéfalo/fisiología , Electroencefalografía , Potenciales Evocados/fisiología , Femenino , Humanos , Masculino , Análisis Multivariante , Experimentación Humana no Terapéutica , Estimulación Luminosa
14.
Nat Commun ; 11(1): 1551, 2020 03 25.
Artículo en Inglés | MEDLINE | ID: mdl-32214095

RESUMEN

Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.


Asunto(s)
Aprendizaje Profundo , Actividades Humanas/clasificación , Fenómenos Magnéticos , Monitoreo Fisiológico/métodos , Procesamiento de Señales Asistido por Computador , Humanos , Movimiento (Física) , Dispositivos Electrónicos Vestibles , Tecnología Inalámbrica
15.
PLoS Comput Biol ; 16(3): e1007650, 2020 03.
Artículo en Inglés | MEDLINE | ID: mdl-32163407

RESUMEN

Calcium imaging has been widely used for measuring spiking activities of neurons. When using calcium imaging, we need to extract summarized information from the raw movie beforehand. Recent studies have used matrix deconvolution for this preprocessing. However, such an approach can neither directly estimate the generative mechanism of spike trains nor use stimulus information that has a strong influence on neural activities. Here, we propose a new deconvolution method for calcium imaging using marked point processes. We consider that the observed movie is generated from a probabilistic model with marked point processes as hidden variables, and we calculate the posterior of these variables using a variational inference approach. Our method can simultaneously estimate various kinds of information, such as cell shape, spike occurrence time, and tuning curve. We apply our method to simulated and experimental data to verify its performance.


Asunto(s)
Potenciales de Acción/fisiología , Señalización del Calcio/fisiología , Procesamiento de Imagen Asistido por Computador/métodos , Algoritmos , Animales , Calcio/metabolismo , Simulación por Computador , Ratones , Modelos Neurológicos , Modelos Teóricos , Neuronas/fisiología , Procesamiento de Señales Asistido por Computador
16.
Am J Phys Med Rehabil ; 99(8): 694-700, 2020 08.
Artículo en Inglés | MEDLINE | ID: mdl-32084035

RESUMEN

OBJECTIVE: The aim of this study was to investigate electroencephalographic (EEG) connectivity short-term changes, quantified by node strength and betweenness centrality, induced by a single trial of exoskeleton-assisted gait in chronic stroke survivors. DESIGN: Study design was randomized crossover. Electroencephalographic data (64-channel system) were recorded before gait (baseline) and after unassisted overground walking and overground exoskeleton-assisted walking. Coherence was estimated for alpha1, alpha2, and beta frequency ranges. Graph analysis assessed network model properties: node strength and betweenness centrality. RESULTS: Nine participants were included in the final analysis. In the group (four participants) with a left-hemisphere stroke lesion (dominant hemisphere), over the vertex, node strength increased in alpha1, alpha2, and beta bands, and betweenness centrality decreased in alpha2 both after unassisted overground walking and exoskeleton-assisted walking. In the group (five participants) with a right-hemisphere lesion (nondominant hemisphere), node strength increased in alpha1 and alpha2 over the contralesional sensorimotor area and ipsilesional prefrontal area after overground exoskeleton-assisted walking, compared with baseline and unassisted overground walking. CONCLUSION: A single session of exoskeleton training provides short-term neuroplastic modulation in chronic stroke. In participants with a nondominant hemisphere lesion, exoskeleton training induces activations similar to those observed in able-bodied participants, suggesting a role of lesion lateralization in networks' reorganization.


Asunto(s)
Electroencefalografía , Dispositivo Exoesqueleto , Trastornos Neurológicos de la Marcha/rehabilitación , Hemiplejía/rehabilitación , Rehabilitación de Accidente Cerebrovascular/métodos , Adulto , Anciano , Estudios Cruzados , Femenino , Trastornos Neurológicos de la Marcha/fisiopatología , Hemiplejía/fisiopatología , Humanos , Masculino , Persona de Mediana Edad , Plasticidad Neuronal , Proyectos Piloto , Estudios Prospectivos , Procesamiento de Señales Asistido por Computador , Accidente Cerebrovascular/fisiopatología
17.
Artículo en Inglés | MEDLINE | ID: mdl-32033231

RESUMEN

Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed. In this study, an effective autism diagnostic system using electroencephalogram (EEG) signals, which are generated from electrical activity in the brain, was developed and characterized. The pre-processed signals were converted to two-dimensional images using the higher-order spectra (HOS) bispectrum. Nonlinear features were extracted thereafter, and then reduced using locality sensitivity discriminant analysis (LSDA). Significant features were selected from the condensed feature set using Student's t-test, and were then input to different classifiers. The probabilistic neural network (PNN) classifier achieved the highest accuracy of 98.70% with just five features. Ten-fold cross-validation was employed to evaluate the performance of the classifier. It was shown that the developed system can be useful as a decision support tool to assist healthcare professionals in diagnosing autism.


Asunto(s)
Trastorno del Espectro Autista/diagnóstico , Adolescente , Trastorno del Espectro Autista/fisiopatología , Niño , Preescolar , Análisis Discriminante , Electroencefalografía , Femenino , Humanos , Masculino , Redes Neurales de la Computación , Procesamiento de Señales Asistido por Computador
18.
J Physiol Anthropol ; 39(1): 4, 2020 Feb 21.
Artículo en Inglés | MEDLINE | ID: mdl-32085811

RESUMEN

BACKGROUND: Recently, attempts have been made to use the pulse rate variability (PRV) as a surrogate for heart rate variability (HRV). PRV, however, may be caused by the fluctuations of left ventricular pre-ejection period and pulse transit time besides HRV. We examined whether PRV differs not only from HRV but also depending on the measurement site. RESULTS: In five healthy subjects, pulse waves were measured simultaneously on both wrists and both forearms together with single-lead electrocardiogram (ECG) in the supine and sitting positions. Although average pulse interval showed no significant difference from average R-R interval in either positions, PRV showed greater power for the low-frequency (LF) and high-frequency (HF) components and lower LF/HF than HRV. The deviations of PRV from HRV in the supine and sitting positions were 13.2% and 7.9% for LF power, 24.5% and 18.3% for HF power, and - 15.0% and - 30.2% for LF/HF, respectively. While the average pulse interval showed 0.8% and 0.5% inter-site variations among the four sites in the supine and sitting positions, respectively, the inter-site variations in PRV were 4.0% and 3.6% for LF power, 3.8% and 4.7% for HF power, and 18.0% and 17.5% for LF/HF, respectively. CONCLUSIONS: These suggest that PRV shows not only systemic differences from HRV but also considerable inter-site variations.


Asunto(s)
Electrocardiografía/métodos , Frecuencia Cardíaca/fisiología , Análisis de la Onda del Pulso/métodos , Dispositivos Electrónicos Vestibles , Adulto , Femenino , Antebrazo/irrigación sanguínea , Humanos , Masculino , Postura/fisiología , Procesamiento de Señales Asistido por Computador , Muñeca/irrigación sanguínea , Adulto Joven
19.
Yonsei Med J ; 61(3): 243-250, 2020 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-32102125

RESUMEN

PURPOSE: We aimed to analyze the surveillance reports of adverse events (AEs) due to different types of pneumococcal vaccines, in addition to detecting and validating signals of pneumococcal vaccines by comparing AEs with labels. MATERIALS AND METHODS: We analyzed the percentages of AEs according to vaccine type [pneumococcal polysaccharide vaccines (PPSVs) and pneumococcal conjugate vaccines (PCVs)] in children and adults using data from the Korea Adverse Event Reporting System (KAERS) database from 2005 to 2016. A signal was defined as an AE that met all three indices of data mining: proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). We validated the detected signals by calculating sensitivity, specificity, as well as positive and negative predictive values of the signals against label information. RESULTS: Of the 39933 AE reports on vaccination, 5718 (7.0%) were related to pneumococcal vaccine. The most frequent AE after vaccination with PPSV was fever (23.9%) in children and injection-site reaction in adults. The most frequent AE after vaccination with PCV in children was pharyngitis (26.2%). In total, 13 AEs met all three indices for signal detection. Among these, hypotension, apathy, sepsis, and increased serum glutamic oxaloacetic transaminase level were not listed on vaccine labels. In validation analysis, PRR and ROR performed slightly better than IC for adults who were vaccinated with PPSVs. CONCLUSION: Overall, 13 new signals of PPSVs, including four signals not listed on the labels, were detected. Further research based on additional AE reports is required to confirm the validity of these signals for children.


Asunto(s)
Sistemas de Registro de Reacción Adversa a Medicamentos , Bases de Datos Factuales , Vacunas Neumococicas/inmunología , Procesamiento de Señales Asistido por Computador , Adolescente , Niño , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Valor Predictivo de las Pruebas , República de Corea , Sensibilidad y Especificidad , Vacunación , Vacunas Conjugadas/inmunología
20.
Nat Methods ; 17(3): 261-272, 2020 03.
Artículo en Inglés | MEDLINE | ID: mdl-32015543

RESUMEN

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.


Asunto(s)
Algoritmos , Biología Computacional/métodos , Lenguajes de Programación , Programas Informáticos , Biología Computacional/historia , Simulación por Computador , Historia del Siglo XX , Historia del Siglo XXI , Modelos Lineales , Modelos Biológicos , Dinámicas no Lineales , Procesamiento de Señales Asistido por Computador
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