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1.
Rev. bras. med. esporte ; 27(5): 523-526, July-Sept. 2021. tab, graf
Article in English | LILACS | ID: biblio-1288612

ABSTRACT

ABSTRACT Background: Athletics plays a very important role in competitive sports. The strength of track and field directly represents the level of a country's sports competition. Objective: This work aimed to study the track and field sports forewarning model based on radial basis function (RBF) neural networks. One hundred outstanding athletes were taken as the research objects. The questionnaire survey method was adopted to count athletes' injury risk factors, and coaches were consulted to evaluate the questionnaire's overall quality, structure, and content. Methods: A track and field early warning model based on RBF neural network is established, and the results are analyzed. Results: The results showed that the number of people who thought the questionnaire was relatively complete (92%) was considerably higher than that of very complete (2%) and relatively complete (6%) (P<0.05). The number of people who thought that the questionnaire structure was relatively perfect (45%) was notably higher than that of the very perfect (18%) (P<0.05). The semi-reliability test result suggested that the questionnaire reliability was 0.85. Tests on ten samples showed that the RBF neural network model error and the actual results were basically controlled between −0.04~0.04. Conclusions: After the sample library test, the track and field sports forewarning model under RBF neural network can obtain relatively favorable results. Level of evidence II; Therapeutic studies - investigation of treatment results.


RESUMO Antecedentes: O atletismo desempenha um papel muito importante nos esportes competitivos. A força do atletismo representa diretamente o nível de competição esportiva de um país. Objetivo: Este trabalho teve como objetivo estudar o modelo de advertência em esportes de atletismo baseado em redes neurais de função de base radial (RBF). 100 atletas de destaque foram tomados como objetos de pesquisa. O método de pesquisa por questionário foi adotado para contar os fatores de risco de lesões dos atletas e os treinadores foram consultados para avaliar a qualidade geral, estrutura e conteúdo do questionário. Métodos: Um modelo de alerta precoce de pista e campo baseado na rede neural RBF é estabelecido e os resultados são analisados. Resultados: Os resultados mostraram que o número de pessoas que consideraram o questionário relativamente completo (92%) foi consideravelmente maior do que o de muito completo (2%) e relativamente completo (6%) (P <0,05). O número de pessoas que pensaram que a estrutura do questionário era relativamente perfeita (45%) foi notavelmente maior do que a das muito perfeitas (18%) (P <0,05). O resultado do teste de semifiabilidade sugeriu que a confiabilidade do questionário foi de 0,85. Testes em 10 amostras mostraram que o erro entre o modelo de rede neural RBF e os resultados reais foi basicamente controlado entre −0,04 ~ 0,04. Conclusões: Após o teste da biblioteca de amostras, o modelo de advertência em esportes de atletismo sob a rede neural RBF pode obter resultados relativamente favoráveis. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento.


RESUMEN Antecedentes: el atletismo juega un papel muy importante en los deportes competitivos. La fuerza de la pista y el campo representa directamente el nivel de competición deportiva de un país. Objetivo: Este trabajo tuvo como objetivo estudiar el modelo de alerta de los deportes de pista y campo basado en redes neuronales de función de base radial (RBF). Se tomaron como objeto de investigación 100 atletas destacados. Se adoptó el método de encuesta de cuestionario para contar los factores de riesgo de lesiones de los atletas y se consultó a los entrenadores para evaluar la calidad general, la estructura y el contenido del cuestionario. Métodos: Se establece un modelo de alerta temprana de pista y campo basado en la red neuronal RBF y se analizan los resultados. Resultados: Los resultados mostraron que el número de personas que pensaban que el cuestionario era relativamente completo (92%) era considerablemente mayor que el de muy completo (2%) y relativamente completo (6%) (P <0,05). El número de personas que pensaba que la estructura del cuestionario era relativamente perfecta (45%) fue notablemente superior al de los muy perfectos (18%) (P <0,05). El resultado de la prueba de semifiabilidad sugirió que la confiabilidad del cuestionario era 0,85. Las pruebas en 10 muestras mostraron que el error entre el modelo de red neuronal RBF y los resultados reales se controló básicamente entre −0,04 ~ 0,04. Conclusiones: Después de la prueba de la biblioteca de muestras, el modelo de advertencia de deportes de pista y campo bajo la red neuronal RBF puede obtener resultados relativamente favorables. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.


Subject(s)
Humans , Athletic Injuries/prevention & control , Track and Field/injuries , Algorithms , Surveys and Questionnaires , Risk Factors , Neural Networks, Computer
2.
Rev. bras. med. esporte ; 27(4): 367-371, Aug. 2021. graf
Article in English | LILACS | ID: biblio-1288608

ABSTRACT

ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects' body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.


RESUMO Objetivo: Com o objetivo de estudar a relação entre atividade aeróbia e atividade nervosa autonômica cardíaca por algoritmo de rede neural artificial e fusão biológica de imagens, tendo em vista os problemas existentes no modelo de rede neural artificial (RNA), é introduzida a tecnologia de processamento biológico de imagens com base em ANN. Métodos: um algoritmo de inteligência biológica de imagem Ann é proposto, um classificador adequado para triagem eletrocardiográfica (ECG) é projetado e um sistema de triagem de sinal de ECG é estabelecido com sucesso. Além disso, o conjunto de dados de sinais de ECG normais recuperados dos sujeitos durante o período experimental é construído e um classificador é usado para extrair os dados característicos de um sinal de ECG normal durante o período experimental. Resultados: As alterações na frequência cardíaca em repouso e outros indicadores de saúde física são analisadas pela combinação de indicadores fisiológicos de repouso, a saber, frequência cardíaca, peso corporal, índice de massa corporal e índice de gordura corporal. Os resultados mostram que o classificador autodesenhado pode processar com eficiência as imagens de ECG, e as atividades regulares de longo prazo podem melhorar as condições físicas da maioria das pessoas. O peso corporal e a taxa de gordura corporal da maioria dos indivíduos diminuem com a extensão do tempo do experimento, e a freqüência cardíaca em repouso diminui relativamente. Conclusões: Certos indicadores podem ser usados para prever a saúde física dinâmica de uma pessoa, o que indica que a pesquisa experimental de predição de índice nesta pesquisa tem um bom efeito, que não apenas estende a aplicação da rede neural artificial, mas também estabelece uma base para a pesquisa e implementação de dispositivos vestíveis de teste inteligente de ECG. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento.


RESUMEN Objetivo: Para estudiar la relación entre la actividad aeróbica y la actividad del nervio autónomo cardíaco mediante el algoritmo de red neuronal artificial y la fusión de imágenes biológicas, ante los problemas existentes en el modelo de red neuronal artificial (ANN), se introduce la tecnología de procesamiento de imágenes biológicas basada en ANA. Métodos: Se propone un algoritmo de inteligencia de imagen biológica de Ann, se diseña un clasificador adecuado para el cribado electrocardiógrafo (ECG) y se establece con éxito un sistema de cribado de señales de ECG. Además, se construye el conjunto de datos de las señales de ECG recuperadas normales de los sujetos durante el período experimental, y se utiliza un clasificador para extraer los datos característicos de una señal de ECG normal durante el período experimental. Resultados: Los cambios en la frecuencia cardíaca en reposo y otros indicadores de salud física se analizan combinando indicadores fisiológicos en reposo, a saber, frecuencia cardíaca, peso corporal, índice de masa corporal y tasa de grasa corporal. Los resultados muestran que el clasificador de diseño propio puede procesar de manera eficiente las imágenes de ECG, y las actividades regulares a largo plazo pueden mejorar las condiciones físicas de la mayoría de las personas. El peso corporal y la tasa de grasa corporal de la mayoría de los sujetos disminuyen con la extensión del tiempo del experimento, y la frecuencia cardíaca en reposo disminuye relativamente. Conclusiones: Ciertos indicadores pueden usarse para predecir la salud física dinámica de una persona, lo que indica que la investigación experimental de predicción de índices en esta investigación tiene un buen efecto, lo que no solo extiende la aplicación de la red neuronal artificial sino que también sienta las bases para la investigación. e implementación de dispositivos portátiles de prueba inteligente de ECG. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.


Subject(s)
Humans , Running/physiology , Autonomic Nervous System/physiology , Image Interpretation, Computer-Assisted/methods , Neural Networks, Computer , Heart Rate/physiology , Algorithms , Image Processing, Computer-Assisted , Electrocardiography
3.
Rev. bras. med. esporte ; 27(4): 405-409, Aug. 2021. graf
Article in English | LILACS | ID: biblio-1288596

ABSTRACT

ABSTRACT Objective: The paper uses artificial neural network images to explore the effects of aerobic exercise on the gamma rhythm of theta period in the awake hippocampal CA1 area of APP/PS1/tau mice and the low-frequency gamma rhythm of the sleep state hippocampal CA1 area SWR period. Methods: Clean grade 6-month-old APP/PS1/tau mice were randomly divided into quiet group (AS) and exercise group (AE), C57BL/6J control group mice were randomly divided into quiet group (CS) and exercise group (CE). The AE group and the CE group performed 12-week treadmill exercise, 5d/week, 60min/d, the first 10min exercise load was 12m/min, the last 50min was 15m/min treadmill slope was 0°. Eight-arm maze detection of behavioral changes in mice; multi-channel in vivo recording technology to record the electrical signals of the awake state and sleep state in the hippocampal CA1 area, MATLAB extracts the awake state theta period and sleep state SWR period, multi-window spectrum estimation method Perform time-frequency analysis and power spectral density analysis. Results: 12 weeks of aerobic exercise can significantly improve the working memory and reference memory of the AS group, increase the gamma energy in theta period of the awake hippocampus CA1 area and the low-frequency gamma energy in the sleep state CA1 area SWR period. Conclusions: Aerobic exercise can improve the neural network state of the AD model and increase the gamma energy in theta period of the hippocampus CA1 area, and the low-frequency gamma energy in the SWR period is one of the neural network mechanisms for its overall behavioral improvement. Level of evidence II; Therapeutic studies - investigation of treatment results.


RESUMO Objetivo: o artigo usa imagens de redes neurais artificiais para explorar os efeitos do exercício aeróbio no ritmo gama do período teta na área CA1 do hipocampo desperto de camundongos APP/PS1/tau e o ritmo gama de baixa frequência da área CA1 do hipocampo do estado de sono Período SWR. Métodos: Camundongos APP/PS1/tau de grau limpo de 6 meses de idade foram divididos aleatoriamente em grupo quieto (AS) e grupo de exercício (AE), os camundongos do grupo controle C57BL/6J foram divididos aleatoriamente em grupo quieto (CS) e grupo de exercício (CE). O grupo AE e o grupo CE realizaram 12 semanas de exercício em esteira, 5d/semana, 60min/d, a primeira carga de exercício de 10min foi de 12m/min, a última de 50min foi de 15m/min e a inclinação da esteira foi de 0 °. Detecção de labirinto de oito braços de mudanças comportamentais em camundongos; tecnologia de gravação in vivo multicanal para registrar os sinais elétricos do estado de vigília e do estado de sono na área CA1 do hipocampo, MATLAB extrai o período de tempo teta do estado de vigília e o período de tempo SWR do estado de sono, método de estimativa de espectro de múltiplas janelas. e análise de densidade espectral de potência. Resultados: 12 semanas de exercícios aeróbicos podem melhorar significativamente a memória de trabalho e a memória de referência do grupo AS, aumentar a energia gama no período teta da área CA1 do hipocampo acordado e a energia gama de baixa frequência na área CA1 do estado de sono período SWR. Conclusões: O exercício aeróbico pode melhorar o estado da rede neural do modelo AD e aumentar a energia gama no período teta da área CA1 do hipocampo e a energia gama de baixa frequência no período SWR é um dos mecanismos da rede neural para seu comportamento geral. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento.


RESUMEN Objetivo: El artículo utiliza imágenes de redes neuronales artificiales para explorar los efectos del ejercicio aeróbico en el ritmo gamma del período theta en el área CA1 del hipocampo despierto de ratones APP/PS1/tau y el ritmo gamma de baja frecuencia del área CA1 del hipocampo en estado de sueño. Período de ROE. Métodos: Se dividieron aleatoriamente ratones APP/PS1/tau de 6 meses de edad de grado limpio en grupo tranquilo (AS) y grupo de ejercicio (AE), los ratones del grupo de control C57BL/6J se dividieron aleatoriamente en grupo tranquilo (CS) y grupo de ejercicio (CE). El grupo de EA y el grupo de EC realizaron 12 semanas de ejercicio en cinta rodante, 5 días a la semana, 60 min/d, la primera carga de ejercicio de 10 min fue de 12 m/min, los últimos 50 min fueron de 15 m/min y la pendiente de la cinta fue de 0 °. Detección en laberinto de ocho brazos de cambios de comportamiento en ratones; tecnología de grabación in vivo multicanal para registrar las señales eléctricas del estado despierto y del estado de sueño en el área CA1 del hipocampo, MATLAB extrae el período de tiempo theta del estado despierto y el período de tiempo de SWR del estado de suspensión, método de estimación de espectro de múltiples ventanas Realizar análisis de tiempo-frecuencia y análisis de densidad espectral de potencia. Resultados: 12 semanas de ejercicio aeróbico pueden mejorar significativamente la memoria de trabajo y la memoria de referencia del grupo AS, aumentar la energía gamma en el período theta del área CA1 del hipocampo despierto y la energía gamma de baja frecuencia en el período SWR del área CA1 del estado de sueño. Conclusiones: El ejercicio aeróbico puede mejorar el estado de la red neuronal del modelo AD y aumentar la energía gamma en el período theta del área del hipocampo CA1 y la energía gamma de baja frecuencia en el período SWR es uno de los mecanismos de la red neuronal para su comportamiento general. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.


Subject(s)
Animals , Mice , Exercise/physiology , Neural Networks, Computer , Gamma Rhythm/physiology , Hippocampus/diagnostic imaging , Models, Animal
4.
Rev. bras. med. esporte ; 27(4): 395-399, Aug. 2021. tab, graf
Article in English | LILACS | ID: biblio-1288595

ABSTRACT

ABSTRACT Introduction: With the advent of the network information age, the teaching of martial arts and traditional national sports has broken the limitations of the previous "teaching by precept and deeds" and combined with computer information technology to construct a teaching resource system for martial arts and traditional national sports, realizing the sharing of teaching resources. Objective: The article determines the concept of teaching resource integration by formulating a resource integration plan for the teaching resource system of martial arts and traditional national sports majors. At the same time provides a theoretical basis for the resource integration of the teaching resource system. Methods: The article takes the general university martial arts and national traditional sports discipline model as the research object, uses the method of literature data, investigation, logical analysis and other methods to research some general university martial arts and traditional national sports discipline curriculum models, and analyzes the existing ones. Results: The article formulated the resource integration plan of the professional teaching platform and provided theoretical guidance for the resource integration of the professional teaching resource platform. Secondly, it analyzes the advantages and disadvantages of resource integration of the teaching resource platform combining martial arts and sports. It proposes corresponding improvement suggestions to improve the quality and efficiency of the teaching resource platform. Conclusions: The teaching resource platform combining martial arts and physical education forms a relatively complete teaching resource system; reasonable teaching resource module design; realizes the network sharing of professional teaching resources in colleges and universities; the martial arts rank system highlights the key points and demonstrates the characteristics of the times. Level of evidence II; Therapeutic studies - investigation of treatment results.


RESUMO Introdução: Com o advento da era das redes de informação, o ensino das artes marciais e dos esportes nacionais tradicionais rompeu as limitações do antigo "ensino por preceito e fatos" e foi combinado com a tecnologia da computação para construir um sistema de recursos didáticos para as artes marciais. artes e esportes tradicionais nacionais, fazendo a troca de recursos didáticos. Objetivo: o artigo determina o conceito de integração de recursos didáticos formulando um plano de integração de recursos para o sistema de recursos didáticos das carreiras das artes marciais e dos esportes tradicionais nacionais. Ao mesmo tempo, fornece uma base teórica para a integração de recursos do sistema de recursos de ensino. Métodos: O artigo toma o modelo geral das artes marciais universitárias e o modelo da disciplina nacional de esportes tradicionais como objeto de pesquisa, usa o método de dados da literatura, pesquisa, análise lógica e outros métodos para investigar alguns modelos de currículo geral de artes marciais universitárias e disciplinas desportivas nacionais tradicionais. e analisar os existentes. Resultados: o artigo formulou o plano de integração de recursos da plataforma de ensino profissional e forneceu orientações teóricas para a integração de recursos da plataforma de recursos de ensino profissional. Em segundo lugar, ele discute as vantagens e desvantagens de integrar recursos da plataforma de recursos de ensino que combina artes marciais e esportes. Propõe as correspondentes sugestões de melhoria para melhorar a qualidade e eficiência da plataforma de recursos didáticos. Conclusões: A plataforma de recursos didáticos que combina artes marciais e educação física forma um sistema de recursos didáticos relativamente completo; projeto de módulo de recursos de ensino razoável; Inclui o intercâmbio de redes de recursos de ensino profissional em faculdades e universidades; o sistema de classificação das artes marciais destaca os pontos-chave e demonstra as características da época. Nível de evidência II; Estudos terapêuticos: investigação dos resultados do tratamento.


RESUMEN Introducción: Con el advenimiento de la era de la información en red, la enseñanza de artes marciales y deportes nacionales tradicionales ha roto las limitaciones de la anterior "enseñanza por precepto y hechos" y se ha combinado con la tecnología informática para construir un sistema de recursos didácticos para artes marciales y deportes nacionales tradicionales, realizando el intercambio de recursos didácticos. Objetivo: El artículo determina el concepto de integración de recursos didácticos mediante la formulación de un plan de integración de recursos para el sistema de recursos didácticos de las carreras de artes marciales y deportes nacionales tradicionales. Al mismo tiempo, proporciona una base teórica para la integración de recursos del sistema de recursos didácticos. Métodos: El artículo toma el modelo general de artes marciales universitarias y el modelo de disciplina deportiva tradicional nacional como objeto de investigación, utiliza el método de datos de literatura, investigación, análisis lógico y otros métodos para investigar algunos modelos de currículo de artes marciales universitarias generales y disciplinas deportivas nacionales tradicionales. y analiza los existentes. Resultados: El artículo formuló el plan de integración de recursos de la plataforma docente profesional y brindó orientación teórica para la integración de recursos de la plataforma de recursos docentes profesionales. En segundo lugar, analiza las ventajas y desventajas de la integración de recursos de la plataforma de recursos didácticos que combina artes marciales y deportes. Propone las correspondientes sugerencias de mejora para mejorar la calidad y la eficiencia de la plataforma de recursos didácticos. Conclusiones: La plataforma de recursos didácticos que combina artes marciales y educación física forma un sistema de recursos didácticos relativamente completo; diseño de módulo de recursos didácticos razonables; comprende el intercambio de redes de recursos docentes profesionales en colegios y universidades; el sistema de clasificación de las artes marciales destaca los puntos clave y demuestra las características de la época. Nivel de evidencia II; Estudios terapéuticos: investigación de los resultados del tratamiento.


Subject(s)
Humans , Physical Education and Training , Martial Arts/education , Universities , Neural Networks, Computer , Curriculum , Education/methods
5.
Prensa méd. argent ; 107(5): 282-286, 20210000.
Article in English | LILACS, BINACIS | ID: biblio-1359365

ABSTRACT

El aprendizaje profundo es un tipo de inteligencia artificial computarizada que tiene como objetivo entrenar a una computadora para que realice tareas que normalmente realizan los humanos basándose en redes neuronales artificiales. Los avances tecnológicos recientes han demostrado que las redes neuronales artificiales se pueden aplicar a campos como el reconocimiento de voz y audio, la traducción automática, los juegos de mesa, el diseño de fármacos y el análisis de imágenes médicas. El desarrollo de estas técnicas ha sido extremadamente rápido en los últimos años y las redes neuronales artificiales hoy en día superan a los humanos en muchas de estas tareas. Las redes neuronales artificiales se inspiraron en la función de sistemas biológicos como el cerebro y los nodos conectados dentro de estas redes que modelan las neuronas. El principio de tales redes es que están capacitadas con conjuntos de datos donde se conoce la verdad fundamental. Como ejemplo, la red debe estar capacitada para identificar imágenes donde se representa una bicicleta. Esto requiere una gran cantidad de imágenes donde las bicicletas se etiquetan manualmente (la llamada verdad fundamental) que luego son analizadas por la computadora. Si se utilizan suficientes imágenes con bicicleta o sin bicicleta, la red neuronal artificial puede entrenarse para identificar bicicletas en otros conjuntos de imágenes. En las imágenes médicas, los enfoques clásicos incluyen la extracción de características semánticas definidas por expertos humanos o características agonísticas definidas por ecuaciones. Las características semánticas pueden proporcionar una buena especificidad para el diagnóstico de enfermedades, pero pueden diferir entre diferentes médicos dependiendo de su nivel de experiencia, requieren mucho tiempo y son costosas. Las características agonísticas pueden tener una especificidad limitada, pero ofrecen la ventaja de una alta reproducibilidad. El aprendizaje profundo tiene un enfoque diferente. Se requiere un conjunto de datos de entrenamiento donde se conoce la verdad básica, en este caso el diagnóstico. El número de datos necesarios es elevado y, por lo general, se utilizan 100.000 imágenes o más. Una vez que se entrena la red neuronal artificial, se puede aplicar a un conjunto de datos de validación en el que también se conoce el diagnóstico, pero no se informa a la computadora. La salida de la red neuronal artificial es, en el caso más simple, una enfermedad o ninguna enfermedad que pueda compararse con la verdad fundamental. La concordancia con la verdad del terreno se cuantifica utilizando medidas como el área bajo la curva (AUC, puede tomar valores entre 0 y 1, siendo 1 la discriminación perfecta entre salud y enfermedad), especificidad (puede tomar valores entre 0% y 100% y la proporción de negativos reales que se identifican correctamente) y la sensibilidad (puede tomar valores entre 0% y 100% y cuantifica la proporción de positivos reales que se identifican correctamente). Si se requiere una alta sensibilidad o una alta especificidad depende de la enfermedad, la prevalencia de la enfermedad, así como el entorno clínico real donde se debe emplear esta red


Subject(s)
Humans , Artificial Intelligence , Neural Networks, Computer , Speech Recognition Software , Deep Learning
6.
Rev. bras. med. esporte ; 27(spe2): 83-86, Apr.-June 2021. tab, graf
Article in English | LILACS | ID: biblio-1280091

ABSTRACT

ABSTRACT Athletes' psychological control ability directly affects competitions. Therefore, it is necessary to supervise the athletes' game psychology. Athletes' game state supervision model is constructed through the facial information extraction algorithm. The homography matrix and the calculation method are introduced. Then, two methods are introduced to solve the rotation matrix from the homography matrix. After the rotation matrix is solved, the method of obtaining the facial rotation angle from the rotation matrix is introduced. The two methods are compared in the simulation data, and the advantages and disadvantages of each algorithm are analyzed to determine the method used in this paper. The experimental results show that the model prediction accuracy reaches 70%, which can effectively supervise the psychological state of athletes. This research study is of great significance to improve the performance of athletes in competitions and improve the application of back propagation (BP) neural network algorithm.


RESUMO A capacidade de controle psicológico de atletas afeta diretamente as competições. Portanto, é muito necessário supervisionar a psicologia de jogo desses indivíduos. O modelo de supervisão do estado de jogo dos atletas é construído através do algoritmo de extração de informações faciais. A matriz de homografia e o método de cálculo são introduzido. Em seguida, são introduzidos dois métodos para resolver a matriz de rotação a partir da matriz de homografia. Após a resolução da matriz de rotação, introduz-se o método de obtenção do ângulo de rotação facial a partir dessa matriz. Os dois métodos são comparados nos dados da simulação, e as vantagens e desvantagens de cada algoritmo são analisadas para determinar o método utilizado neste estudo. Os resultados experimentais mostram que a precisão da previsão do modelo atinge 70%, sendo possível efetivamente supervisionar o estado psicológico dos atletas. O presente estudo é de grande importância para melhorar o desempenho dos atletas em competições e melhorar a aplicação do algoritmo de rede neural backpropagation (BP).


RESUMEN La capacidad de control psicológico de atletas afecta directamente las competencias. Por lo tanto, es muy necesario supervisar la psicología de juego de esos individuos. El modelo de supervisión del estado de juego de los atletas es construido por medio del algoritmo de extracción de informaciones faciales. La matriz de homografía y el método de cálculo son introducidos. Enseguida, son introducidos dos métodos para resolver la matriz de rotación a partir de la matriz de homografía. Después de la resolución de la matriz de rotación, se introduce el método de obtención del ángulo de rotación facial a partir de esa matriz. Los dos métodos son comparados en los datos de la simulación, y las ventajas y desventajas de cada algoritmo son analizadas para determinar el método utilizado en este estudio. Los resultados experimentales muestran que la precisión de la previsión del modelo alcanza 70%, siendo posible efectivamente supervisar el estado psicológico de los atletas. El presente estudio es de gran importancia para mejorar el desempeño de los atletas en competencias y mejorar la aplicación del algoritmo de red neuronal backpropagation (BP).


Subject(s)
Humans , Neural Networks, Computer , Athletic Performance/psychology , Athletes/psychology , Algorithms
7.
Arch. cardiol. Méx ; 91(1): 58-65, ene.-mar. 2021. tab, graf
Article in English | LILACS | ID: biblio-1152861

ABSTRACT

Abstract Objective: The aim of this study was to develop, train, and test different neural network (NN) algorithm-based models to improve the Global Registry of Acute Coronary Events (GRACE) score performance to predict in-hospital mortality after an acute coronary syndrome. Methods: We analyzed a prospective database, including 40 admission variables of 1255 patients admitted with the acute coronary syndrome in a community hospital. Individual predictors included in GRACE score were used to train and test three NN algorithm-based models (guided models), namely: one- and two-hidden layer multilayer perceptron and a radial basis function network. Three extra NNs were built using the 40 admission variables of the entire database (unguided models). Expected mortality according to GRACE score was calculated using the logistic regression equation. Results: In terms of receiver operating characteristic area and negative predictive value (NPV), almost all NN algorithms outperformed logistic regression. Only radial basis function models obtained a better accuracy level based on NPV improvement, at the expense of positive predictive value (PPV) reduction. The independent normalized importance of variables for the best unguided NN was: creatinine 100%, Killip class 61%, ejection fraction 52%, age 44%, maximum creatine-kinase level 41%, glycemia 40%, left bundle branch block 35%, and weight 33%, among the top 8 predictors. Conclusions: Treatment of individual predictors of GRACE score with NN algorithms improved accuracy and discrimination power in all models with respect to the traditional logistic regression approach; nevertheless, PPV was only marginally enhanced. Unguided variable selection would be able to achieve better results in PPV terms.


Resumen Objetivo: El objetivo fue desarrollar, entrenar y probar diferentes modelos basados en algoritmos de redes neuronales (RN) para mejorar el rendimiento del score del Registro Global de Eventos Coronarios Agudos (GRACE) para predecir la mortalidad hospitalaria después de un síndrome coronario agudo. Métodos: Analizamos una base de datos prospectiva que incluía 40 variables de ingreso de 1255 pacientes con síndrome coronario agudo en un hospital comunitario. Las variables incluidas en la puntuación GRACE se usaron para entrenar y probar tres algoritmos basados en RN (modelos guiados), a saber: perceptrones multicapa de una y dos capas ocultas y una red de función de base radial. Se construyeron tres RN adicionales utilizando las 40 variables de admisión de toda la base de datos (modelos no guiados). La mortalidad esperada según el GRACE se calculó usando la ecuación de regresión logística. Resultados: En términos del área ROC y valor predictivo negativo (VPN), casi todos los algoritmos RN superaron la regresión logística. Solo los modelos de función de base radial obtuvieron un mejor nivel de precisión basado en la mejora del VPN, pero a expensas de la reducción del valor predictivo positivo (VPP). La importancia normalizada de las variables incluidas en la mejor RN no guiada fue: creatinina 100%, clase Killip 61%, fracción de eyección 52%, edad 44%, nivel máximo de creatina quinasa 41%, glucemia 40%, bloqueo de rama izquierda 35%, y peso 33%, entre los 8 predictores principales. Conclusiones: El tratamiento de las variables del score GRACE mediante algoritmos de RN mejoró la precisión y la discriminación en todos los modelos con respecto al enfoque tradicional de regresión logística; sin embargo, el VPP solo mejoró marginalmente. La selección no guiada de variables podría mejorar los resultados en términos de PPV.


Subject(s)
Humans , Male , Female , Middle Aged , Aged , Algorithms , Registries , Neural Networks, Computer , Hospital Mortality , Acute Coronary Syndrome/mortality , Prognosis , Databases, Factual
8.
Braz. arch. biol. technol ; 64: e21210130, 2021. tab, graf
Article in English | LILACS | ID: biblio-1278436

ABSTRACT

Abstract This research aims to compare the classical thin-layer models, stepwise fit regression method (SRG) and artificial neural networks (ANN) in the modelling of drying kinetics of shrimp shell and crab exoskeleton. Thus, drying curves were obtained using a convective dryer (3.0 m/s) at temperatures of 30.45 and 60oC. The results showed a decreasing tendency for the drying time as the temperature increased for both materials. Drying curves modelling of both materials showed fitted results with R 2 adj >0.998 and MRE<13.128% for some thin-layer models. On the other hand, by SRG a simple model could be obtained as a function of time and temperature, with the greatest accuracy being found in the modelling of experimental data of crab exoskeleton, with MRE<10.149%. Finally, the ANNs were employed successfully in the modelling of drying kinetics, showing high prediction quality with the trained recurrent ANN models.


Subject(s)
Crustacea , Animal Shells , Kinetics , Neural Networks, Computer , Models, Anatomic
9.
Neuroscience Bulletin ; (6): 1469-1480, 2021.
Article in English | WPRIM | ID: wpr-922634

ABSTRACT

Effective methods for visualizing neurovascular morphology are essential for understanding the normal spinal cord and the morphological alterations associated with diseases. However, ideal techniques for simultaneously imaging neurovascular structure in a broad region of a specimen are still lacking. In this study, we combined Golgi staining with angiography and synchrotron radiation micro-computed tomography (SRμCT) to visualize the 3D neurovascular network in the mouse spinal cord. Using our method, the 3D neurons, nerve fibers, and vasculature in a broad region could be visualized in the same image at cellular resolution without destructive sectioning. Besides, we found that the 3D morphology of neurons, nerve fiber tracts, and vasculature visualized by SRμCT were highly consistent with that visualized using the histological method. Moreover, the 3D neurovascular structure could be quantitatively evaluated by the combined methodology. The method shown here will be useful in fundamental neuroscience studies.


Subject(s)
Animals , Imaging, Three-Dimensional , Mice , Neural Networks, Computer , Spinal Cord/diagnostic imaging , Synchrotrons , X-Ray Microtomography
10.
Article in English | WPRIM | ID: wpr-921871

ABSTRACT

Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine,


Subject(s)
Computer Simulation , Computers , Neural Networks, Computer , Tomography, X-Ray Computed
11.
Journal of Biomedical Engineering ; (6): 1072-1080, 2021.
Article in Chinese | WPRIM | ID: wpr-921847

ABSTRACT

As one of the non-invasive imaging techniques, myocardial perfusion imaging provides a basis for the diagnosis of myocardial ischemia in coronary heart disease. Aiming at the bull-eye image in myocardial perfusion imaging, this paper proposed a branching structure, which included multi-layer transposed convolution up-sampling concatenate module and four-channel weighted channels attention module, and the output results of the branch structure were fused with the output results of trunk U-Net, to achieve accurate segmentation of the cardiac ischemia missing degree in myocardial perfusion bull-eye image. The experimental results show that the multi-layer transposed convolution up-sampling concatenate module realizes the fusion of different depth feature maps, and effectively reduces the interference of the severe sparse degree which is similar to the missing degree on the segmentation. Four-channel weighted attention module can further improve the ability to distinguish between the two similar degrees and the ability to learn edge details of the targets, and retain more abundant edge details features. The experimental data came from Tianjin Medical University General Hospital, Tianjin TEDA Hospital, Tianjin First Central Hospital and Third Central Hospital. The Jaccard scores in the self-built dataset was 5.00% higher than that of U-Net. The model presented in this paper is superior to other optimized models based on U-Net, and the subjective evaluation meets the accuracy requirements for clinical diagnosis.


Subject(s)
Humans , Image Processing, Computer-Assisted , Ischemia , Myocardial Ischemia/diagnostic imaging , Neural Networks, Computer , Perfusion
12.
Journal of Biomedical Engineering ; (6): 1054-1061, 2021.
Article in Chinese | WPRIM | ID: wpr-921845

ABSTRACT

Otitis media is one of the common ear diseases, and its accurate diagnosis can prevent the deterioration of conductive hearing loss and avoid the overuse of antibiotics. At present, the diagnosis of otitis media mainly relies on the doctor's visual inspection based on the images fed back by the otoscope equipment. Due to the quality of otoscope equipment pictures and the doctor's diagnosis experience, this subjective examination has a relatively high rate of misdiagnosis. In response to this problem, this paper proposes the use of faster region convolutional neural networks to analyze clinically collected digital otoscope pictures. First, through image data enhancement and preprocessing, the number of samples in the clinical otoscope dataset was expanded. Then, according to the characteristics of the otoscope picture, the convolutional neural network was selected for feature extraction, and the feature pyramid network was added for multi-scale feature extraction to enhance the detection ability. Finally, a faster region convolutional neural network with anchor size optimization and hyperparameter adjustment was used for identification, and the effectiveness of the method was tested through a randomly selected test set. The results showed that the overall recognition accuracy of otoscope pictures in the test samples reached 91.43%. The above studies show that the proposed method effectively improves the accuracy of otoscope picture classification, and is expected to assist clinical diagnosis.


Subject(s)
Computers , Diagnosis, Computer-Assisted , Humans , Neural Networks, Computer , Otitis Media/diagnosis
13.
Article in Chinese | WPRIM | ID: wpr-921837

ABSTRACT

Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.


Subject(s)
Algorithms , Artificial Intelligence , Brain , Neural Networks, Computer
14.
Article in Chinese | WPRIM | ID: wpr-921835

ABSTRACT

Automatic classification of heart sounds plays an important role in the early diagnosis of congenital heart disease. A kind of heart sound classification algorithms based on sub-band envelope feature and convolution neural network was proposed in this paper, which did not need to segment the heart sounds according to cardiac cycle accurately. Firstly, the heart sound signal was divided into some frames. Then, the frame level heart sound signal was filtered with Gammatone filter bank to obtain the sub-band signals. Next, the sub-band envelope was extracted by Hilbert transform. After that, the sub-band envelope was stacked into a feature map. Finally, type Ⅰ and type Ⅱ convolution neural network were selected as classifier. The result shown that the sub-band envelope feature was better in type Ⅰ than type Ⅱ. The algorithm is tested with 1 000 heart sound samples. The test results show that the overall performance of the algorithm proposed in this paper is significantly improved compared with other similar algorithms, which provides a new method for automatic classification of congenital heart disease, and speeds up the process of automatic classification of heart sounds applied to the actual screening.


Subject(s)
Algorithms , Heart , Heart Defects, Congenital/diagnosis , Heart Sounds , Humans , Neural Networks, Computer , Signal Processing, Computer-Assisted
15.
Article in Chinese | WPRIM | ID: wpr-921821

ABSTRACT

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 (


Subject(s)
Algorithms , Anesthesia, General , Electroencephalography , Humans , Neural Networks, Computer , Wavelet Analysis
16.
Journal of Integrative Medicine ; (12): 395-407, 2021.
Article in English | WPRIM | ID: wpr-888774

ABSTRACT

OBJECTIVE@#By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes.@*METHODS@#From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.@*RESULTS@#The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%.@*CONCLUSION@#Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.


Subject(s)
Bayes Theorem , Humans , Liver Neoplasms/diagnosis , Machine Learning , Neural Networks, Computer , Syndrome
17.
Article in Chinese | WPRIM | ID: wpr-888238

ABSTRACT

The inverse problem of diffuse optical tomography (DOT) is ill-posed. Traditional method cannot achieve high imaging accuracy and the calculation process is time-consuming, which restricts the clinical application of DOT. Therefore, a method based on stacked auto-encoder (SAE) was proposed and used for the DOT inverse problem. Firstly, a traditional SAE method is used to solved the inverse problem. Then, the output structure of SAE neural network is improved to a single output SAE, which reduce the burden on the neural network. Finally, the improved SAE method is used to compare with traditional SAE method and traditional levenberg-marquardt (LM) iterative method. The result shows that the average time to solve the inverse problem of the method proposed in this paper is only 1.67% of the LM method. The mean square error (MSE) value is 46.21% lower than the traditional iterative method, 61.53% lower than the traditional SAE method, and the image correlation coefficient(ICC) value is 4.03% higher than the traditional iterative method, 18.7% higher than the traditional SAE method and has good noise immunity under 3% noise conditions. The research results in this article prove that the improved SAE method has higher image quality and noise resistance than the traditional SAE method, and at the same time has a faster calculation speed than the traditional iterative method, which is conducive to the application of neural networks in DOT inverse problem calculation.


Subject(s)
Algorithms , Neural Networks, Computer , Tomography, Optical
18.
Article in Chinese | WPRIM | ID: wpr-888233

ABSTRACT

The background of abdominal computed tomography (CT) images is complex, and kidney tumors have different shapes, sizes and unclear edges. Consequently, the segmentation methods applying to the whole CT images are often unable to effectively segment the kidney tumors. To solve these problems, this paper proposes a multi-scale network based on cascaded 3D U-Net and DeepLabV3+ for kidney tumor segmentation, which uses atrous convolution feature pyramid to adaptively control receptive field. Through the fusion of high-level and low-level features, the segmented edges of large tumors and the segmentation accuracies of small tumors are effectively improved. A total of 210 CT data published by Kits2019 were used for five-fold cross validation, and 30 CT volume data collected from Suzhou Science and Technology Town Hospital were independently tested by trained segmentation models. The results of five-fold cross validation experiments showed that the Dice coefficient, sensitivity and precision were 0.796 2 ± 0.274 1, 0.824 5 ± 0.276 3, and 0.805 1 ± 0.284 0, respectively. On the external test set, the Dice coefficient, sensitivity and precision were 0.817 2 ± 0.110 0, 0.829 6 ± 0.150 7, and 0.831 8 ± 0.116 8, respectively. The results show a great improvement in the segmentation accuracy compared with other semantic segmentation methods.


Subject(s)
Humans , Kidney Neoplasms/diagnostic imaging , Neural Networks, Computer , Specimen Handling , Tomography, X-Ray Computed
19.
Article in Chinese | WPRIM | ID: wpr-888228

ABSTRACT

Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.


Subject(s)
Algorithms , Atrial Fibrillation/diagnosis , Electrocardiography , Humans , Neural Networks, Computer , Stroke
20.
Article in Chinese | WPRIM | ID: wpr-888227

ABSTRACT

Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constructed a one-dimensional convolutional neural network (CNN) model, which consists in four convolution layers, four pooling layers, two full connection layers and one classification layer. The automatic feature extraction and classification were realized by the structure of the proposed CNN model. The model was verified by the whole night single-channel sleep electrocardiogram (ECG) signals of 70 subjects from the Apnea-ECG dataset. Our results showed that the accuracy of per-segment SA detection was ranged from 80.1% to 88.0%, using the input signals of single-channel ECG signal, RR interval (RRI) sequence, R peak sequence and RRI sequence + R peak sequence respectively. These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence. When the input signals were RRI sequence + R peak sequence, the CNN model achieved the best performance. The accuracy, sensitivity and specificity of per-segment SA detection were 88.0%, 85.1% and 89.9%, respectively. And the accuracy of per-recording SA diagnosis was 100%. These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years. The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.


Subject(s)
Electrocardiography , Humans , Machine Learning , Neural Networks, Computer , Sensitivity and Specificity , Sleep Apnea Syndromes/diagnosis
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