Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning / 生物医学工程学杂志

The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.

Primary study on recognition of vascular stiffness based on wavelet scattering neural network / 生物医学工程学杂志

Cardiovascular disease is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.

Predicting Vertical Ground Reaction Force during Treadmill Running Using Principal Component Analysis and Wavelet Neural Network / 医用生物力学

Objective To establish the method of predicting the vertical ground reaction force (vGRF) during treadmill running based on principal component analysis and wavelet neural network (PCA-WNN). Methods Nine rearfoot strikers were selected and participated in running experiment on an instrumented treadmill at the speed of 12, 14 and 16 km/h. The kinematics data and vGRF were collected using infrared motion capture system and dynamometer treadmill. A three-layer neural network framework was constructed, in which the activation function of the hidden layers was the Morlet function. Velocities of mass center of the thigh, shank and foot as well as joint angles of the hip, knee and ankle were input into the WNN model. The prediction accuracy of the model was evaluated by the coefficient of multiple correlation (CMC) and error. The consistencies between predicted and measured peak GRF were analyzed by Bland-Altman method. Results The CMC between the predicted and measured GRF at different speeds were all greater than 0.99; the root mean square error (RMSE) between the predicted and measured vGRF was 0.18-0.28 BW; and the normalized root mean square error (NRMSE) was 6.20%-8.42%; the NRMSE between the predicted and measured impact forces and propulsive forces were all smaller than 15%. Bland-Altman results showed that the predicted peak errors of propulsive force at 12 km/h and that of impact force and propulsive force at 14 km/h were within the 95% agreement interval. Conclusions The PCA-WNN model constructed in this study can accurately predict the vGRF during treadmill running. The results provide a new method to obtain kinetic data and perform real-time monitoring on a treadmill, which is of great significance for studying running injuries and rehabilitation treatment.

Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory / 生物医学工程学杂志

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

An Adaptive Method for Detecting and Removing EEG Noise / 中国医疗器械杂志

To solve the problem of real-time detection and removal of EEG signal noise in anesthesia depth monitoring, we proposed an adaptive EEG signal noise detection and removal method. This method uses discrete wavelet transform to extract the low-frequency energy and high-frequency energy of a segment of EEG signals, and sets two sets of thresholds for the low-frequency band and high-frequency band of the EEG signal. These two sets of thresholds can be updated adaptively according to the energy situation of the most recent EEG signal. Finally, we judge the level of signal interference according to the range of low-frequency energy and high-frequency energy, and perform corresponding denoising processing. The results show that the method can more accurately detect and remove the noise interference in the EEG signal, and improve the stability of the calculated characteristic parameters.

Distribuição espacial da dengue e sua coerência geográfica no município do Rio de Janeiro nos anos 2000 a 2019 / Spatial distribution of dengue and its geographic coherence in the municipality of Rio de Janeiro in the years 2000 to 2019

A dengue é um grande problema de saúde pública no município do Rio de Janeiro desde a sua reintrodução no Brasil na década de 1980 e vários fatores têm sido apontados como adequados para a persistência da doença no município, como o clima quente e úmido, a vulnerabilidade social em grande parte do território com deficiência na oferta e no acesso da população aos bens e serviços essenciais, criando condições propícias ao desenvolvimento e manutenção do ciclo do vetor Aedes aegypti. Além disso, a ampla difusão do vetor por todo o município e a co- circulação de vários sorotipos do vírus (DEN-1, DEN-2, DEN-3 e DEN 4) dificulta o seu controle. De acordo com o levantamento de índice rápido para o Aedes aegypti (LIRAa) de agosto de 2019, o Rio de Janeiro foi um dos municípios do estado do Rio de Janeiro que apresentou regiões classificadas no estrato de risco, refletindo a fragilidade de controle do vetor no município. Neste contexto, torna-se relevante a utilização de ferramentas de análise espacial e temporal, para compreender a dinâmica da dengue no munícipio do Rio de Janeiro durante os 20 anos de estudo. Para isso, foi realizado um estudo ecológico com base em dados secundários de notificação de dengue no período de 2000 a 2019, onde, inicialmente foi descrito o movimento da trajetória geográfica da doença durante o período de estudo, por meses e por bairros, além de caracterizar áreas de alto e baixo risco para dengue. Foram calculadas taxas de incidência suavizadas pelo método Bayes empírico local e indicadores locais de autocorrelação espacial para os anos estudados. A localização dos centróides dos bairros e as taxas de incidência suavizadas foram utilizadas para traçar a trajetória mensal e anual do centro geográfico de ocorrência da dengue. Posteriormente foi realizado uma análise da coerência geográfica utilizando análise de wavelet a fim de compreender a propagação da doença no município ao longo do tempo em suas Regiões Administrativas. Analisamos as taxas de incidência mensais da dengue no período de 2000 a 2019 no município do Rio de Janeiro para entender o comportamento entre as séries temporais de diferentes áreas e identificar possíveis relações entre elas. Foi realizada análise do espectro de potência para as séries temporais de cada Região Administrativa, que permite verificar a periodicidade das séries, seu sinal predominante e a potência média. Também foi realizada análise de fase de cada Região Administrativa e extração de fase dos anos epidêmicos no período anual. Os resultados sugerem que a dengue ocorre de forma heterogênea na cidade do Rio de Janeiro, com aglomerados de alta incidência migrando para a Zona Oeste da cidade ao longo das duas décadas analisadas. Quanto à coerência da dengue os resultados sugeriram periodicidade predominantemente anual entre as regiões e com grandes picos de 3, 4 e 5 anos, com diferenças entre regiões e a maioria das séries se moveram juntas ao longo dos anos. Nossos achados sugerem que nos anos epidêmicos algumas Regiões Administrativas da Zona Norte e Zona Oeste estiveram aproximadamente 2 meses a 1 mês à frente das outras regiões. Essa defasagem pode ser um potencial para o controle nas áreas que tiveram atraso, considerando esse tempo para o planejamento da assistência, vigilância e ações de controle. A trajetória da dengue e a variação de sua distribuição espacial ao longo do tempo revelam um complexo sistema de ocorrência, potencialmente dependente da interação entre os níveis de imunidade adquiridos pela população e modificações em fatores socioambientais e demográficos. Análises que incorporam dimensões espaciais e temporais podem ser úteis para informar estratégias de controle mais intensivas, integradas e direcionadas a regiões específicas.

Dengue is a major public health problem in the municipality of Rio de Janeiro since its reintroduction in Brazil in the 1980s and several factors have been pointed out as suitable for the persistence of the disease in the municipality, such as the hot and humid climate, social vulnerability in much of the territory with deficient supply and access of the population to essential goods and services, creating conditions conducive to the development and maintenance of the cycle of the vector Aedes aegypti. Moreover, the wide dissemination of the vector throughout the city and the co-circulation of various serotypes of the virus (DEN-1, DEN-2, DEN-3, and DEN 4) make its control difficult. According to the rapid index survey for Aedes aegypti (LIRAa) of August 2019, Rio de Janeiro was one of the municipalities in the state of Rio de Janeiro that presented regions classified in the risk stratum, reflecting the fragility of vector control in the municipality. In this context, it becomes relevant the use of spatial and temporal analysis tools, to understand the dynamics of dengue in the city of Rio de Janeiro during the 20 years of study. For this, an ecological study was conducted based on secondary data of dengue notification in the period from 2000 to 2019, where, initially, the movement of the geographic trajectory of the disease during the study period was described, by months and neighborhoods, in addition to characterizing areas of high and low risk for dengue. Incidence rates smoothed by the local empirical Bayes method and local indicators of spatial autocorrelation were calculated for the years studied. The location of neighborhood centroids and smoothed incidence rates were used to plot the monthly and annual trajectory of the geographic center of dengue occurrence. Subsequently, a geographic coherence analysis was performed using wavelet analysis in order to understand the spread of the disease in the municipality over time in its Administrative Regions. We analyzed the monthly incidence rates of dengue in the period from 2000 to 2019 in the municipality of Rio de Janeiro to understand the behavior between time series from different areas and identify possible relationships between them. Power spectrum analysis was performed for the time series of each Administrative Region, which allows to verify the periodicity of the series, its predominant signal, and the average power. Phase analysis was also performed for each Administrative Region and phase extraction of the epidemic years in the annual period. The results suggest that dengue occurs in a heterogeneous manner in the city of Rio de Janeiro, with clusters of high incidence migrating to the West Zone of the city over the two decades analyzed. Regarding the consistency of dengue the results suggested predominantly annual periodicity across regions and with large peaks of 3, 4, and 5 years, with differences between regions and most series moved together over the years. Our findings suggest that in epidemic years, some Administrative Regions in the North Zone and West Zone were approximately 2 months to 1 month ahead of the other regions. This lag may be a potential for control in areas that were delayed, considering this time for planning assistance, surveillance, and control actions. The trajectory of dengue and the variation of its spatial distribution over time reveal a complex system of occurrence, potentially dependent on the interaction between the levels of immunity acquired by the population and changes in socioenvironmental and demographic factors. Analyses that incorporate spatial and temporal dimensions may be useful to inform more intensive, integrated, and targeted control strategies for specific regions.

Wavelet packet transform and multilayer perceptron to identify voices with a mild degree of vocal deviation / Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal

Introduction. Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the in-dividual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications. Aim. For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation ap-plying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN). Methods. A dataset of 74 audio files were used. Shannon energy and entropy mea-sures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN. Results. The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, re-spectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively. Conclusion. The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation

ntroducción. Los trastornos laríngeos se caracterizan por un cambio en el patrón vibratorio de los pliegues vocales. Este trastorno puede tener un origen orgánico, descrito como la modificación anatómica de los pliegues vocales, o de origen fun-cional, provocado por abuso o mal uso de la voz. Los métodos de diagnóstico más comunes se realizan mediante procedimientos invasivos que causan malestar al pa-ciente. Además, los desvíos vocales de grado leve no impiden que el individuo utilice la voz, lo que dificulta la identificación del problema y aumenta la posibilidad de complicaciones futuras.Objetivo. Por esas razones, el objetivo de esta investigación es desarrollar una he-rramienta alternativa, no invasiva para la identificación de voces con grado leve de desvío vocal aplicando Transformada Wavelet Packet (WPT) y la red neuronal artifi-cial del tipo Perceptrón Mutlicapa (PMC). Métodos. Fue utilizado un banco de datos con 78 voces. Fueron extraídas las me-didas de energía y entropía de Shannon usando las familias Daubechies 2 y Symlet 2 para después aplicar la red neuronal PMC. Resultados. La familia Symlet 2 fue más eficiente en su generalización, obteniendo un 99.75% y un 99.56% de precisión mediante el uso de medidas de energía y en-tropía de Shannon, respectivamente. La familia Daubechies 2, sin embargo, obtuvo menores índices de precisión: 91.17% y 70.01%, respectivamente. Conclusión. La combinación de WPT y PMC presentó alta precisión para la iden-tificación de voces con grado leve de desvío vocal

Suicide cases in developed and emerging countries: an analysis using wavelets / Casos de suicídio em países desenvolvidos e emergentes: uma análise usando ondaletas

OBJECTIVE: The aim of this study was to use a wavelet technique to determine whether the number of suicides is similar between developed and emerging countries. METHODS: Annual data were obtained from World Health Organization (WHO) reports from 1986 to 2015. Discrete nondecimated wavelet transform was used for the analysis, and the Daubechies wavelet function was applied with five-level decomposition. Regarding clustering, energy (variance) was used to analyze the clusters and visualize the clustering process. We constructed a dendrogram using the Mahalanobis distance. The number of groups was set using a specific function in the R program. RESULTS: The cluster analysis verified the formation of four groups as follows: Japan, the United States and Brazil were distinct and isolated groups, and other countries (Austria, Belgium, Chile, Israel, Mexico, Italy and the Netherlands) constituted a single group. CONCLUSION: The methods utilized in this paper enabled a detailed verification of countries with similar behaviors despite very distinct socioeconomic, geographic and climate characteristics.

OBJETIVO: Verificar se existe relação de similaridade entre o número de suicídio em países desenvolvidos e emergentes usando a técnica de ondaletas. MÉTODOS: Os dados anuais foram obtidos a partir do relatório da Organização Mundial da Saúde (OMS), no período de 1986 a 2015. Para análise, foi empregada a transformada discreta não decimada de ondaleta (NDWT), a função ondaleta aplicada foi a Daubechies com cinco níveis de decomposição. Com relação ao agrupamento, utilizou-se a energia (variância) para analisar os clusters e, para a visualização do processo de clusterização, trabalhamos com o dendograma, no qual se empregou a distância de Mahalanobis. A quantidade de grupos foi definida por meio da função NbCluster. RESULTADOS: A partir da análise de cluster, verificou-se a formação de quatros grupos. No qual, Japão e Estados Unidos e Brasil localizam-se em grupos distintos e isolados. E os demais países (Áustria, Bélgica, Chile, Israel, México, Itália e Holanda) em um único grupo. CONCLUSÃO: Utilizando esse método, foi possível verificar com mais detalhes quais países apresentaram comportamentos semelhantes, mesmo apresentando características bem distintas entre si, tanto socioeconômica, geográfica e climática.

Research on the detection algorithm of electrocardiogram characteristic wave based on energy segmentation and stationary wavelet transform / 生物医学工程学杂志

The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.

Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform / 生物医学工程学杂志

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.

An anesthesia depth computing method study based on wavelet transform and artificial neural network / 生物医学工程学杂志

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper proposes a method which combines wavelet transform and artificial neural network (ANN) to assess the depth of anesthesia. Discrete wavelet transform was used to decompose the EEG signal, and the approximation coefficients and detail coefficients were used to calculate the 9 characteristic parameters. Kruskal-Wallis statistical test was made to these characteristic parameters, and the test showed that the parameters were statistically significant for the differences of the four levels of anesthesia: awake, light anesthesia, moderate anesthesia and deep anesthesia (

Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition / 生物医学工程学杂志

The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.

Application of Improved Wavelet Threshold in Denoising of ECG Signals / 中国医疗器械杂志

The ECG signal is susceptible to interference from the external environment during the acquisition process, affecting the analysis and processing of the ECG signal. After the traditional soft-hard threshold function is processed, there is a defect that the signal quality is not high and the continuity at the threshold is poor. An improved threshold function wavelet denoising is proposed, which has better regulation and continuity, and effectively solves the shortcomings of traditional soft and hard threshold functions. The Matlab simulation is carried out through a large amount of data, and various processing methods are compared. The results show that the improved threshold function can improve the denoising effect and is superior to the traditional soft and hard threshold denoising.

Mild poikilocapnic hypoxia increases very low frequency haemoglobin oxygenation oscillations in prefrontal cortex

BACKGROUND: The aim of the study was to investigate the effect of mild cerebral hypoxia on haemoglobin oxygenation (HbO2), cerebrospinal fluid dynamics and cardiovascular physiology. To achieve this goal, four signals were recorded simultaneously: blood pressure, heart rate / electrocardiogram, HbO2 from right hemisphere and changes of subarachnoid space (SAS) width from left hemisphere. Signals were registered from 30 healthy, young participants (2 females and 28 males, body mass index = 24.5 ± 2.3 kg/m2, age 30.8 ± 13.4 years). RESULTS: We analysed the recorded signals using wavelet transform and phase coherence. We demonstrated for the first time that in healthy subjects exposed to mild poikilokapnic hypoxia there were increases in very low frequency HbO2 oscillations (< 0.052 Hz) in prefrontal cortex. Additionally, SAS fluctuation diminished in the whole frequency range which could be explained by brain oedema. CONCLUSIONS: Consequently the study provides insight into mechanisms governing brain response to a mild hypoxic challenge. Our study supports the notion that HbO2 and SAS width monitoring might be beneficial for patients with acute lung disease.

Proceso para la fusión de neuroimágenes de Tomografía por Emisión de Positrones y Resonancia Magnética / Process for the fusion of Positron Emission Tomography and Magnetic Resonance neuroimaging

En radiología se utilizan varias técnicas imagenológicas para el diagnóstico de enfermedades y la asistencia en intervenciones quirúrgicas con el objetivo de determinar la ubicación y dimensión exacta de un tumor cerebral. Técnicas como la Tomografía por Emisión de Positrones y la Resonancia Magnética permiten determinar la naturaleza maligna o benigna de un tumor cerebral y estudiar las estructuras del cerebro con neuroimágenes de alta resolución. Investigadores a nivel internacional han utilizado diferentes técnicas para la fusión de la Tomografía por Emisión de Positrones y Resonancia Magnética al permitir la observación de las características fisiológicas en correlación con las estructuras anatómicas. La presente investigación tiene como objetivo elaborar un proceso para la fusión de neuroimágenes de Tomografía por Emisión de Positrones y Resonancia Magnética. Para ello se definieron 5 actividades en el proceso y los algoritmos a utilizar en cada una, lo cual propició identificar los más eficientes para aumentar la calidad en el proceso de fusión. Como resultado se obtuvo un proceso de fusión de neuroimágenes basado en un esquema híbrido Wavelet y Curvelet que garantiza obtener imágenes fusionadas de alta calidad(AU)

In radiology, various imaging techniques are used for the diagnosis of diseases and assistance in surgical interventions with the aim of determining the exact location and dimension of a brain tumor. Techniques such as Positron Emission Tomography and Magnetic Resonance can determine the malignant or benign nature of a brain tumor and study brain structures with high-resolution neuroimaging. International researchers have used different techniques for the fusion of Positron Emission Tomography and Magnetic Resonance, allowing the observation of physiological characteristics in correlation with anatomical structures. The present research aims to develop a process for the fusion of neuroimaging of Positron Emission Tomography and Magnetic Resonance Imaging. Five activities were defined in the process and the algorithms to be used in each one, which led identifying the most efficient ones to increase the quality in the fusion process. As a result, a neuroimaging fusion process was obtained based on a hybrid Wavelet and Curvelet scheme that guarantees high quality merged images(AU)

Ortogonalización de derivaciones de electrocardiograma / Orthogonalization of electrocardiographic derivations

Introducción: El delineador de señales electrocardiográficas (ECG) multiderivación basado en la transformada wavelet posee alta resolución espacial y permite eliminar las diferencias interderivación que aparecen tradicionalmente en los métodos uniderivación. Para esto necesita de derivaciones de señales electrocardiográficas ortogonales entre sí para la obtención de un bucle espacial. Objetivo: Desarrollar métodos de ortogonalización de dos o tres derivaciones de señales electrocardiográficas que permitan la generalización del delineador multiderivación basado en la transformada wavelet en cualquier base de datos señales electrocardiográficas con más de una derivación. Métodos: Se implementaron tres métodos de ortogonalización de derivaciones de señales electrocardiográficas: ortogonalización de dos derivaciones a partir de la proyección de vectores, ortogonalización a partir de componentes principales y ortogonalización a partir del método clásico de Gram-Schmidt. Resultados: Se comparó el funcionamiento del delineador multiderivación de ECG cuando es usado cada método de ortogonalización, mediante el cálculo de la media aritmética y la desviación estándar teniendo en cuenta diferentes combinaciones de derivaciones de ambas bases de datos para cada una de las marcas analizadas. Los mejores resultados se obtuvieron con el método análisis de componentes principales y el peor comportamiento con el método de ortogonalización de dos derivaciones. Conclusiones: Los algoritmos de ortogonalización que obtuvieron los mejores resultados fueron los basados en tres derivaciones ortogonales, en la que fue ligeramente superior la descomposición en componentes principales y, por tanto, se considera el método más adecuado para la generalización del delineador multiderivación(AU)

Introduction: The wavelet transform-based multiderivation electrocardiographic (ECG) signal delineator has high spatial resolution and makes it possible to eliminate interderivation differences traditionally appearing in uniderivation methods. But this requires electrocardiographic signal derivations orthogonal to one another to obtain a spatial loop. Objective: Develop orthogonalization methods of two or three electrographic signal derivations allowing generalization of the wavelet transform-based multiderivation delineator in any electrographic signal database with more than one derivation. Methods: Three orthogonalization methods were implemented for electrocardiographic signal derivations: vector projection-based two-derivation orthogonalization, principal component-based orthogonalization, and orthogonalization based on the Gram-Schmidt classic method. Results: A comparison was performed between the operation of the ECG multiderivation delineator when used with each orthogonalization method. The comparison was based on estimation of the arithmetic mean and standard deviation bearing in mind different combinations of derivations from both databases for each of the marks analyzed. The best results were obtained with the principal component analysis method and the worst ones with the two-derivation orthogonalization method. Conclusions: The orthogonalization algorithms obtaining the best results were those based on three orthogonal derivations, in which decomposition into principal components was slightly higher. This is therefore considered to be the most appropriate method for generalization of the multiderivation delineator(AU)

Determination of maximum noise level in an ECG channel under SURE Wavelet filtering for HRV extraction

Abstract Heart Rate Variability (HRV) is the measure of variation between R-R interbeats, it has been demonstrated to be a good representation of physiological features, especially to the alterations in the Autonomic Nervous System (ANS). Considering the values that compose a HRV distribution are extracted from electrocardiography (ECG), many of the electrical disturbances that affect ECG-based diagnosis can also interfere with the results of the HRV analysis. This paper uses a 30-minute portion of a healthy patient (no arrhythmias detected or annotated) from the MIT-BIH ECG database to analyze the effectiveness of the SURE Wavelet denoising method for extracting the HRV from a progressively noisier ECG channel. Results show that the minimum SNR for reliable HRV extraction under these conditions is approximately 5dB and outlines the exponential behavior of HRV extraction for escalating noise levels in the ECG signal.

Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network

A brain tumoris a mass of abnormal cells in the brain. Brain tumors can be benignor malignant. Conventional diagnosis of a brain tumor by the radiologist, is done by examining a set of images produced by magnetic resonance imaging (MRI).Many computer-aided detection (CAD) systems have been developed in order to help the radiologist reach his goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents anovel CAD technique for the classification of brain tumors in MRI images The proposed system extracts features from the brain MRI images by utilizingthe strong energy compactness property exhibited by the Discrete Wavelet transform (DWT). The Wavelet features are then applied to a CNNto classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 98.5%.

Análisis y selección de técnicas para la fusión de imágenes PET/CT basado en software / Analysis and selection of techniques for the fusion of PET/CT imaging based on software

Técnicas como la Tomografía por Emisión de Positrones y la Tomografía Computarizada permiten determinar la naturaleza maligna o benigna de un tumor y estudiar las estructuras anatómicas del cuerpo con imágenes de alta resolución, respectivamente. Investigadores a nivel internacional han utilizado diferentes técnicas para la fusión de la Tomografía por Emisión de Positrones y la Tomografía Computarizada porque permite observar las funciones metabólicas en correlación con las estructuras anatómicas. La presente investigación se propone realizar un análisis y selección de algoritmos que propicien la fusión de neuroimágenes, basado en la precisión de los mismos. De esta forma contribuir al desarrollo de software para la fusión sin necesidad de adquirir los costosos equipos de adquisición de imágenes de alto rendimiento, los cuales son costosos. Para el estudio se aplicaron los métodos Análisis documental, Histórico lógico e Inductivo deductivo. Se analizaron e identificaron las mejores variantes de algoritmos y técnicas para la fusión según la literatura reportada. A partir del análisis de estas técnicas se identifica como mejor variante el esquema de fusión basado en Wavelet para la fusión de las imágenes. Para el corregistro se propone la interpolación Bicúbica. Como transformada discreta de Wavelet se evidencia el uso de la de Haar. Además, la investigación propició desarrollar el esquema de fusión basado en las técnicas anteriores. A partir del análisis realizado se constataron las aplicaciones y utilidad de las técnicas de fusión como sustitución a los altos costos de adquisición de escáneres multifunción PET/CT para Cuba(AU)

Techniques such as Positron Emission Tomography and Computed Tomography allow to determine the malignant or benign nature of a tumor and to study the anatomical structures of the body with high resolution images, respectively. International researchers have used different techniques for the fusion of Positron Emission Tomography and Computed Tomography because it allows observing metabolic functions in correlation with anatomical structures. The present investigation proposes to carry out an analysis and selection of algorithms that favor the fusion of neuroimaging, based on their precision. In this way, contribute to the development of fusion software without the need to purchase expensive high-performance imaging equipment, which is expensive. For the study the documentary analysis, logical historical and deductive inductive methods were applied. The best algorithm variants and techniques for fusion were analyzed and identified according to the reported literature. From the analysis of these techniques, the Wavelet-based fusion scheme for image fusion is identified as the best variant. Bicubic interpolation is proposed for co-registration. As a discrete Wavelet transform, the use of Haar's is evidenced. In addition, the research led to the development of the fusion scheme based on the previous techniques. From the analysis carried out, the applications and usefulness of fusion techniques were verified as a substitute for the high costs of acquiring PET / CT multifunction scanners for Cuba(AU)

Differences of dynamic connectivity of motor function network between meningioma and low-grade glioma based on wavelet transform / 中国医学影像技术

Objective: To observe the differences of dynamic connectivity in motor function networks between meningioma and low-grade glioma. Methods: Totally 14 patients with meningioma (meningioma group), 14 patients with low grade gliomas (gliomas group) and 14 healthy controls (control group) were enrolled. The wavelet transform coherence (WTC) was used to analyze the dynamic connectivity in motor function networks of 3 groups. Results: The motor function networks nodes were determined by hands movement task stimulus and the generalized linear model analysis, which located at the left and right side of the primary motor cortex (LPMC and RPMC) and the supplementary motor area (SMA). The values of WTC in LPMC-SMA, RPMC-SMA and LPMC-RPMC in meningioma group and gliomas group were lower than those in control group, and in gliomas group were lower than those in meningioma group (all P<0.05). Conclusion: Low frequency fluctuation alteration of connectivity in motor function networks in patients with meningioma and low grade gliomas can be sensitively detected using WTC.