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1.
Rev. bras. med. esporte ; 27(3): 249-252, July-Sept. 2021. tab, graf
Article in English | LILACS | ID: biblio-1288588

ABSTRACT

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm. Results The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy. Conclusions The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.


RESUMO Introdução O treinamento de reabilitação de alta intensidade produzirá fadiga ao exercício. Objetivo Um algoritmo neural de backpropagation network (BP) é proposto para prever a fadiga esportiva com base em imagens de sinais de eletromiografia (EMG). Métodos O algoritmo de análise de componente principal é usado para reduzir a dimensão das características do sinal EMG. O ângulo da articulação do joelho é estimado usando o algoritmo de aprendizado de máquina de limite regularizado acima e o algoritmo de rede neural BP. Resultados o valor RMSE do algoritmo de aprendizado de máquina acima do limite regularizado é menor que o do algoritmo de rede neural BP. Ao mesmo tempo, o valor de ρ do algoritmo de aprendizado de máquina acima do limite regularizado está próximo de 1, indicando sua maior precisão. Conclusões O tempo de treinamento do modelo de algoritmo de aprendizado de máquina acima do limite regularizado foi bastante reduzido, o que melhora a eficiência. Nível de evidência II; Estudos terapêuticos: investigação dos resultados do tratamento.


RESUMEN Introducción El entrenamiento de rehabilitación de alta intensidad producirá fatiga por ejercicio. Objetivo Se propone un algoritmo neuronal de red de retropropagación (BP) para predecir la fatiga deportiva basándose en imágenes de señales de electromiografía (EMG). Métodos El algoritmo de análisis de componentes principales se utiliza para reducir la dimensión de las características de la señal EMG. El ángulo de la articulación de la rodilla se estima mediante el algoritmo de la máquina de aprendizaje por encima del límite regularizado y el algoritmo de red neuronal BP. Resultados el valor de RMSE del algoritmo de la máquina de aprendizaje por encima del límite regularizado es menor que el del algoritmo de red neuronal de BP. Al mismo tiempo, el valor ρ del algoritmo de la máquina de aprendizaje por encima del límite regularizado está más cerca de 1, lo que indica su mayor precisión. Conclusiones El tiempo de entrenamiento del modelo del algoritmo de la máquina de aprendizaje por encima del límite regularizado se ha reducido en gran medida, lo que mejora la eficiencia. Nivel de evidencia II; Estudios terapéuticos: investigación de los resultados del tratamiento.


Subject(s)
Humans , Principal Component Analysis , Fatigue , High-Intensity Interval Training , Algorithms , Signal Processing, Computer-Assisted , Electromyography , Knee Joint/physiology
2.
Article in Chinese | WPRIM | ID: wpr-880439

ABSTRACT

Oxygen saturation and respiratory signals are important physiological signals of human body, respiratory monitoring plays an important role in clinical and daily life. A system was established to extract respiratory signals from photoplethysmography in this study. Including the collection of pulse wave signal, the extraction of respiratory signal, and the calculation of respiratory rate and pulse rate transmitted from the slave computer to the host computer in real time.


Subject(s)
Heart Rate , Humans , Monitoring, Physiologic , Photoplethysmography , Respiratory Rate , Signal Processing, Computer-Assisted
3.
Article in Chinese | WPRIM | ID: wpr-880412

ABSTRACT

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.


Subject(s)
Algorithms , Computer Simulation , Electrocardiography , Signal Processing, Computer-Assisted , Wavelet Analysis
4.
Article in Chinese | WPRIM | ID: wpr-922071

ABSTRACT

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classification algorithm and to solve the problem of subjects data information management. Matlab R2019b and MySQL Sever 8.0 are used to design the software platform. The software platform is divided into three modules including data management module, data receiving module and data processing module. The software platform can be used to query and set the subjects information. It has realized the functions of data receiving, signal processing and the display, analysis and storage of ECG data. The software platform is easy to operate and meets the basic needs of scientific research. It is of great significance to the research of AI-ECG algorithm.


Subject(s)
Algorithms , Artificial Intelligence , Electrocardiography , Humans , Signal Processing, Computer-Assisted , Software
5.
Journal of Biomedical Engineering ; (6): 1193-1202, 2021.
Article in Chinese | WPRIM | ID: wpr-921861

ABSTRACT

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.


Subject(s)
Electroencephalography , Epilepsy/diagnosis , Humans , Machine Learning , Seizures/diagnosis , Signal Processing, Computer-Assisted
6.
Journal of Biomedical Engineering ; (6): 1181-1192, 2021.
Article in Chinese | WPRIM | ID: wpr-921860

ABSTRACT

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.


Subject(s)
Algorithms , Arrhythmias, Cardiac , Electrocardiography , Heart Rate , Humans , Signal Processing, Computer-Assisted , Wavelet Analysis
7.
Journal of Biomedical Engineering ; (6): 1035-1042, 2021.
Article in Chinese | WPRIM | ID: wpr-921843

ABSTRACT

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.


Subject(s)
Algorithms , Electroencephalography , Epilepsy/diagnosis , Humans , Seizures/diagnosis , Signal Processing, Computer-Assisted , Support Vector Machine , Wavelet Analysis
8.
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
9.
Article in Chinese | WPRIM | ID: wpr-921822

ABSTRACT

The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the


Subject(s)
Algorithms , Atrial Fibrillation , Electrocardiography , Heart Rate , Humans , Signal Processing, Computer-Assisted , Support Vector Machine
10.
Article in Chinese | WPRIM | ID: wpr-888237

ABSTRACT

The dynamic electrocardiogram (ECG) collected by wearable devices is often corrupted by motion interference due to human activities. The frequency of the interference and the frequency of the ECG signal overlap with each other, which distorts and deforms the ECG signal, and then affects the accuracy of heart rate detection. In this paper, a heart rate detection method that using coarse graining technique was proposed. First, the ECG signal was preprocessed to remove the baseline drift and the high-frequency interference. Second, the motion-related high amplitude interference exceeding the preset threshold was suppressed by signal compression method. Third, the signal was coarse-grained by adaptive peak dilation and waveform reconstruction. Heart rate was calculated based on the frequency spectrum obtained from fast Fourier transformation. The performance of the method was compared with a wavelet transform based QRS feature extraction algorithm using ECG collected from 30 volunteers at rest and in different motion states. The results showed that the correlation coefficient between the calculated heart rate and the standard heart rate was 0.999, which was higher than the result of the wavelet transform method (


Subject(s)
Electrocardiography , Heart Rate , Humans , Signal Processing, Computer-Assisted , Wavelet Analysis , Wearable Electronic Devices
11.
Article in Chinese | WPRIM | ID: wpr-888220

ABSTRACT

Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.


Subject(s)
Algorithms , Electromyography , Gestures , Humans , Neural Networks, Computer , Signal Processing, Computer-Assisted
12.
Article in Chinese | WPRIM | ID: wpr-888203

ABSTRACT

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.


Subject(s)
Algorithms , Artifacts , Computer Simulation , Electroencephalography , Signal Processing, Computer-Assisted , Wavelet Analysis
13.
Article in Chinese | WPRIM | ID: wpr-879258

ABSTRACT

As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results: sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects: the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test,


Subject(s)
Algorithms , Electrocardiography , Electrocardiography, Ambulatory , Humans , Pilot Projects , Signal Processing, Computer-Assisted , Wearable Electronic Devices
14.
Article in Chinese | WPRIM | ID: wpr-879247

ABSTRACT

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.


Subject(s)
Algorithms , Electroencephalography , Epilepsy/diagnosis , Humans , Signal Processing, Computer-Assisted , Support Vector Machine
15.
Rev. cuba. estomatol ; 57(2): e2366, abr.-jun. 2020.
Article in Portuguese | LILACS, CUMED | ID: biblio-1126508

ABSTRACT

RESUMO Introdução: O escâner intraoral é um aparelho que surge como alternativa aos métodos convencionais de moldagem utilizando a técnica de impressão digital. O meio acadêmico vem realizando diversos estudos para avaliar a real efetividade da tecnologia e sua aplicabilidade clínica. Objetivo: Analisar resultados obtidos em estudos sobre escâneres intraorais na área de implantodontia quanto ao tipo de escâneres e acurácia, tempo de trabalho e preferência do operador e do paciente. Métodos: Foram realizadas buscas de artigos nas bases de dados "Pubmed" e "SciELO" utilizando os seguintes descritores: «intraoral AND scanner AND implant¼, «digital AND scanner AND implant¼ e «digital AND impression AND implant¼ em inglês, português e espanhol respectivamente. Os critérios de inclusão foram: artigos de avaliação clínica (in vivo) ou laboratorial (in vitro) que avaliassem o uso da técnica de escaneamento intra-oral para impressão digital de implantes com acesso integral, escritos em português, inglês ou espanhol e publicados a partir de 2013. Análise e integração da informação: Foram encontrados 158 artigos. Após a análise e seleção, 35 artigos foram incluídos, sendo 28 laboratoriais e 7 clínicos. Apesar de limitações na padronização dos estudos, percebemos o potencial e a viabilidade da técnica digital, com resultados clínicos e de acurácia favoráveis e vantagens como redução do tempo e etapas de trabalho, comunicação dinâmica com os laboratórios, preferência de pacientes e estudantes de odontologia e facilidade de incorporação por profissionais já experientes. Conclusões: Estudos laboratoriais indicam que, além de vantagens quanto ao uso de materiais de moldagem, comunicação com os laboratórios e facilidade de manipulação, a técnica digital pode alcançar resultados superiores aos da técnica convencional, assim, a técnica se mostra promissora para a área de implantodontia sendo necessário, contudo, estudos futuros, especialmente in vivo, para avaliar a consistência dos resultados clínicos(AU)


RESUMEN Introducción: El escáner intrabucal es un aparato que surge como una alternativa frente a los métodos convencionales de moldeo, y el medio académico viene realizando diversos estudios para evaluar la real efectividad de esta tecnología y su aplicabilidad clínica. Objetivo: Analizar resultados obtenidos en estudios sobre escáneres intrabucales en el área de implantología en cuanto a los tipos de escáneres y la exactitud, tiempo de trabajo y preferencia del operador y del paciente. Métodos: Se realizaron búsquedas en las bases de datos "PubMed" y "SciELO" utilizando los siguientes descriptores: "intraoral AND scanner AND implant", "digital AND scanner AND implant" and "digital AND impression AND implant" en inglés, portugués y español, respectivamente. Los criterios de inclusión fueron: artículos clínicos o de laboratorio para evaluar el uso de la técnica de escaneamiento digital de los implantes, con acceso completo al artículo, escrito en portugués, inglés o español y publicados desde 2013. Análisis e integración de la información: Se encontraron 158 artículos. Después del análisis y selección, 35 artículos fueron incluidos, siendo 28 de laboratorio y 7 clínicos. A pesar de las limitaciones en la estandarización de los estudios, percibimos el potencial y la viabilidad de la técnica digital, con resultados clínicos y de precisión favorables y ventajas como reducción del tiempo y etapas de trabajo, comunicación dinámica con los laboratorios, preferencia de pacientes y estudiantes de odontología y facilidad de incorporación de profesionales experimentados. Conclusiones: Los estudios de laboratorio indican que, además de ventajas en cuanto al uso de materiales de moldeo, comunicación con los laboratorios y facilidad de manipulación, la técnica digital puede alcanzar resultados superiores a los de la técnica convencional, por lo que el uso de escáneres intrabucales se muestra prometedor para el área de implantología siendo necesario, sin embargo, estudios futuros, especialmente in vivo, para evaluar la consistencia de los resultados clínicos(AU)


ABSTRACT Introduction: Intraoral scanners are devices that emerged as an alternative to conventional impression methods. A variety of studies have been conducted to evaluate the actual effectiveness of this technology and its clinical applicability. Objective: Analyze the results obtained by studies about intraoral scanners in the area of implantology in terms of types, accuracy, working time, and operator and patient preference. Methods: A search was conducted in the databases PubMed and SciELO using the following descriptors: "intraoral AND scanner AND implant", "digital AND scanner AND implant" and "digital AND impression AND implant" in English, Portuguese and Spanish. The inclusion criteria were the following: clinical or laboratory papers evaluating the use of digital implant scanning technique, full access to the paper, written in Portuguese, English or Spanish and published as of the year 2013. Data analysis and integration: Of the 158 papers obtained and analyzed, 35 were selected: 28 laboratory and 7 clinical. Despite the limitations in the standardization of the studies, we perceive the potential and viability of the digital technique, with favorable clinical and accuracy results, as well as advantages such as a reduction in work time and stages, dynamic communication with laboratories, preference by patients and dental students and ease of technical incorporation by experienced dentists. Conclusions: Laboratory studies indicate that, in addition to the advantages concerning the use of impression materials, communication with laboratories and ease of manipulation, the digital technique may achieve better results than conventional impression techniques. The use of intraoral scanners is therefore a promising technique for the area of ​​implantology. However, further studies shouldbe conducted, especially in vivo, to evaluate the consistency of the clinical results obtained(AU)


Subject(s)
Humans , Signal Processing, Computer-Assisted , Dental Implants/trends , Review Literature as Topic , Databases, Bibliographic , Dimensional Measurement Accuracy
16.
Arq. bras. oftalmol ; 83(1): 28-32, Jan.-Feb. 2020. tab, graf
Article in English | LILACS | ID: biblio-1088952

ABSTRACT

ABSTRACT Purpose: The purpose of the present work is to measure the interocular upper lid contour symmetry using a new method of lid contour quantification. Methods: The Bézier curve tool of the Image J software was used to extract the right and left upper eyelid contours of 75 normal subjects. Inter-observer variability of 29 right lid contours obtained by two independent observers was estimated using the coefficient of overlap of two curves and an analysis of the differences of the contour peak location. A two-way analysis of variance was used to test the mean value of the coefficient of overlap of the right and left contours in males and females and lid segments. The same analysis was performed to compare the location of the contour peak of the right and left contours. Results: The coefficient of contour overlap obtained by independent observers ranged from 93.5% to 98.8%, with a mean of 96.1% ± 1.6 SD. There was a mean difference of 0.02 mm in the location of the contour peak. Right and left contour symmetry did not differ between females and males and was within the range of the method variability for contour overlap and location of the contour peak. Conclusions: The upper eyelid contour is highly symmetrical. Bézier lines allow a quick and fast quantification of the lid contour, with a mean inter-observer variability of 3.9%.


RESUMO Objetivo: O objetivo do presente estudo é mensurar a simetria interocular do contorno da pálpebra superior por meio de um novo método de quantificação de contorno palpebral com curvas de Bézier. Métodos: A ferramenta de curva de Bézier do software ImageJ foi utilizada para extrair os contornos palpebrais direito e esquerdo de 75 sujeitos normais. A variabilidade interobservador de 29 contornos palpebrais do olho direito obtidos por dois observadores diferentes foi estimada pelo coeficiente de superposição de duas curvas e pela análise das diferenças das posições do pico do contorno. Análise de variância de dois fatores foi empregada para testar a média do coeficiente de superposição entre os contornos direito e esquerdo quanto ao sexo e segmento palpebral. A mesma análise foi utilizada para comparar a localização do pico do contorno dos olhos direito e esquerdo. Resultados: O coeficiente de superposição obtidos por observadores independentes variou ente 93,5% e 98,8% com média de 96,1% ± 1,6 DP. A diferença das médias da localização do pico do contorno palpebral foi de 0,02 mm. A simetria entre os contornos dos olhos direito e esquerdo não diferiu entre o sexo feminino e masculino e esteve na faixa de variabilidade do método para o coeficiente de superposição e localização do pico do contorno. Conclusões: O contorno da pálpebra superior é altamente simétrico. As linhas Bézier permitem uma rápida e prática quantificação do contorno palpebral com uma média de variabilidade interobservador de 3,9%.


Subject(s)
Humans , Male , Female , Adult , Eyelids/anatomy & histology , Facial Asymmetry/diagnosis , Reference Values , Computer Simulation , Signal Processing, Computer-Assisted , Software
17.
Article in Chinese | WPRIM | ID: wpr-828170

ABSTRACT

Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.


Subject(s)
Algorithms , Microelectrodes , Principal Component Analysis , Reproducibility of Results , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio , Wavelet Analysis
18.
Article in Chinese | WPRIM | ID: wpr-879204

ABSTRACT

Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.


Subject(s)
Algorithms , Heart Sounds , Humans , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio , Wavelet Analysis
19.
Article in Chinese | WPRIM | ID: wpr-781847

ABSTRACT

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


Subject(s)
Algorithms , Brain-Computer Interfaces , Discriminant Analysis , Electroencephalography , Imagination , Signal Processing, Computer-Assisted
20.
Article in Chinese | WPRIM | ID: wpr-781846

ABSTRACT

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


Subject(s)
Bayes Theorem , Depression , Electroencephalography , Humans , Schizophrenia , Signal Processing, Computer-Assisted , Support Vector Machine
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