Statistical Methods and Artificial Neural Networks Techniques in Electromyography

2012 ◽  
Vol 1 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Ahmad Taher Azar ◽  
Valentina E. Balas
Keyword(s):  
Neural Network ◽  
Statistical Methods ◽  
Muscle Activity ◽  
Masseter Muscle ◽  
Discrete Wavelet ◽  
Neural Network Classifier ◽  
Denture Base ◽  
Feature Vectors ◽  
Significant Difference ◽  
Before And After

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.

An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

2021 ◽  
pp. 232020682110056
Author(s):  
Kaan Orhan ◽  
Gokhan Yazici ◽  
Mehmet Eray Kolsuz ◽  
Nihan Kafa ◽  
Ibrahim Sevki Bayrakdar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Retrospective Study ◽  
Masseter Muscle ◽  
Area Under The Curve ◽  
Muscle Thickness ◽  
Human Observer ◽  
Manual Segmentation ◽  
Significant Difference ◽  
Muscle Segmentation

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P < .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P > .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

HALF-WAVE SEGMENT FEATURE EXTRACTION OF EEG SIGNALS OF PATIENTS WITH DEPRESSION AND PERFORMANCE EVALUATION OF NEURAL NETWORK CLASSIFIERS

2017 ◽  
Vol 17 (01) ◽  
pp. 1750006 ◽  
Author(s):  
SUBHA D. PUTHANKATTIL ◽  
PAUL K. JOSEPH
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Time Domain ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Significant Difference

A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.

scholarly journals Pengaruh kopi Aceh ulee kareng terhadap kekerasan basis gigitiruan resin akrilik Effect of ulee kareng Aceh coffee the hardness of acrylic resin denture base

2011 ◽  
Vol 10 (3) ◽  
pp. 135
Author(s):  
Rini Defika Putri ◽  
Viona Diansari ◽  
Iin Sundari
Keyword(s):  
Surface Hardness ◽  
Acrylic Resin ◽  
Wilcoxon Test ◽  
Distilled Water ◽  
Denture Base ◽  
Robusta Coffee ◽  
Significant Difference ◽  
Before And After ◽  
Microhardness Tester

The hardness of denture base is influenced with the habit of most commonly consumed. Ulee Kareng Aceh coffeewas atype of robusta coffee which is acidic. The purpose of this study was to determine the change of surfacehardness of acrylic denture base after being immersed in ulee kareng coffee for 3 days. Twelve specimens (5 mmdiameter and 2 mm thickness)were randomly distributed in two groups: control (immersion in distilled water) andexperimental (immersion in Ulee Kareng Aceh coffee). Hardness was measured before and after immersion usingKnoop Microhardness tester (Shimadzu). Data were statistically analyzed by Mann Whitney and Wilcoxon test (α =0.05). The results of the study showed a significant difference between the groups P < 0.05. The surface hardness ofacrylic denture base decreased after immersion for both group P < 0.05.

scholarly journals SURFACE ROUGHNESS IMPROVEMENT OF POLYAMIDE RESIN DENTURE AFTER SOAKING IN 50% CINNAMON (Cinnamon burmanii) SOLUTION

2021 ◽  
Vol 1 (Volume 1 No 2) ◽  
pp. 165-174
Author(s):  
Rheni Safira Isnaeni ◽  
Zwista Yulia Dewi ◽  
Muhammad Hamzah Rahmatullah
Keyword(s):  
Surface Roughness ◽  
Base Material ◽  
T Test ◽  
Control Group ◽  
Denture Base ◽  
Cinnamon Extract ◽  
Extract Solution ◽  
Significant Difference ◽  
Before And After ◽  
Antibacterial And Antifungal

Polyamide resin is widely used in dentistry as a denture base material. Cinnamon burmanii has been proven to have antibacterial and antifungal substances. Therefore, it is the potential to be used as a nature denture cleanser. This study aimed to examine the effect of soaking 50% cinnamon extract solution on the surface roughness of polyamide resin. This study used 16 polyamide resin samples soaked in 50% cinnamon extract solution and 16 samples soaked in distilled water as the control group. It examined the surface roughness before and after immersion for four days and seven days. Data were analyzed using paired T-test and independent T-test. The results showed a significant difference in the surface roughness of polyamide resin before and after immersion in cinnamon solution for four days and seven days. The surface roughness change is due to the polyphenol's reaction on the polyamide resin surface, which has destroyed the polymer chain of polyamide resin.

scholarly journals Construction of WeChat Mobile Teaching Platform in the Reform of Physical Education Teaching Strategy Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yufeng Ba ◽  
Lin Qi
Keyword(s):  
Neural Network ◽  
Physical Education ◽  
Deep Neural Network ◽  
Teaching Strategy ◽  
Control Group ◽  
Teaching Platform ◽  
Significant Difference ◽  
Education Strategy ◽  
Before And After ◽  
Experimental Group

Physical education is an important part of school education. Doing a good job of physical education can not only increase students’ interest in sports but also improve their physical fitness. However, traditional physical education methods lack new ideas and fail to reach the goals of physical education. Therefore, it is extremely urgent to conduct physical education teaching strategies. Based on this, this paper proposes the construction of WeChat mobile teaching platform in the reform of physical education teaching strategy based on deep neural network. This paper adopts literature method and experimental analysis method to conduct in-depth research on the application of deep neural network in physical education and its characteristics, shortcomings, and improvements and build a WeChat mobile based on deep neural network in physical education strategy reform teaching platform. The comparison between the control group and the experimental group is used to compare multiple physical test indicators before and after the test to reflect the teaching effectiveness after the change in the physical education strategy of this paper. This paper mainly analyzes the results of the physical education teaching scale and the results of the students’ physical fitness test, including the students’ learning motivation, learning attitude, and learning process in the physical education process, as well as the male and female students’ results of the experimental group and the control group before and after the test compared. The P values of the boys in the experimental class and the control class are all greater than 0.05, which is limited to the relatively short time of the experiment. The data of the boys in the two classes on these three items show no significant difference. The t-test was performed on the posttest results of the three items of the girls, the P values were all less than 0.05, and there were significant differences, especially in the comparison of the results of the postthrowing solid ball and the corner running. The P values of the two test items were all less than 0.01; there is a very significant difference.

scholarly journals The Effect of Inclusive Quality Management on the Educational Accreditation Results of the Educational Centers of Mazandaran University of Medical Sciences in 2015-2018

2021 ◽  
Author(s):  
Roya Malekzadeh ◽  
Fattane Amuei ◽  
Elaheh Mahmoudi ◽  
Ghasem Abedi ◽  
Hamidreza Mohammadi
Keyword(s):  
Medical Education ◽  
Quality Management ◽  
Statistical Methods ◽  
Nonparametric Tests ◽  
Facilities Management ◽  
Medical Sciences ◽  
Interventional Study ◽  
Mazandaran Province ◽  
Significant Difference ◽  
Before And After

Background: The Deming model is an effective method for comprehensive quality management. Objectives: This study aimed to investigate the effect of inclusive quality management on the educational accreditation results of the educational hospitals in the Mazandaran Province, Iran. Methods: This interventional study was conducted in 5 hospitals. In this study, the Deming cycle validation model was employed. This model, which is based on the checklist for the educational centers of the Ministry of Health and Medical Education, has 91 benchmarks for 81 standards. Descriptive statistical methods (Wilcoxon and Friedman nonparametric tests) were used to analyze the data. Results: A significant difference was observed among the accreditation scores of the hospitals before and after the intervention (P<0.05). The accreditation score obtained by the educational centers improved by 41.1, 37, 15.7, 53.2, and 49.2 units. Besides, the intervention outcomes in all areas of accreditation, except facilities management, space, facilities, equipment, and resources, were significantly different. Conclusion: The use of the Deming cycle has proved effective in performing the educational accreditation of the centers, which can be achieved with continuous and proper implementation. This study can help improve the standards of education of the educational centers.

SEGMENTATION OF LIVER TUMOR USING FAST GREEDY SNAKE ALGORITHM

2019 ◽  
Vol 31 (02) ◽  
pp. 1950013 ◽  
Author(s):  
T. Rajalakshmi ◽  
U. Snekhalatha ◽  
Jisha Baby
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Liver Tumor ◽  
Liver Tumors ◽  
Ct Images ◽  
Neural Network Classifier ◽  
Statistical Features ◽  
Automated Method ◽  
Significant Difference ◽  
Liver Image

Back Ground: Liver tumors are a type of growth found in the liver which can be categorized as malignant or benign. It is also called as hepatic tumors. Early stage detection of tumor could be treated at a faster phase; if it is left undiagnosed it may lead to several complications. Traditional method adopted for diagnosis can be time consuming, error-prone and also requires an experts study. Hence a non invasive diagnostic method is required which overcomes the flaws of conventional method. Liver segmentation from CT images in post processing techniques not only is an essential prerequisite, but, by playing an important role in confirming liver function, pathological, and anatomical studies, is also a key technique for diagnosis of liver disease. Hence in the proposed study Fast greedy snakes algorithm in abdominal CT images were used for segmenting tumor portion. Aim & Objectives: The aim and objectives of study is: (i) to segment tumor region in the liver image using Fast Greedy Snakes Algorithm (FGSA); (ii) to extract the GLCM features from the segmented region; (iii) to classify the normal and abnormal liver image using neural network classifier. Methodology: The study involved a total of 30 normal and 30 abnormal Images from database. In the proposed study automated segmentation was performed using Fast Greedy Snakes (FGS) Algorithm and the features were extracted using GLCM method. Classification of normal and abnormal images was carried out using Back propagation Neural Network classifier. Result: The proposed FGS algorithm provides accurate segmentation in liver images. Statistical features like mean, kurtosis, correlation and Entropy showed a higher value for the normal image than liver tumor image. On the other hand, features like Skewness, Homogeneity, contrast, Energy and standard deviation showed a comparatively higher value for a liver tumor image than the normal. Statistical features such as Mean, Contrast, Homogeneity and standard deviation are statistically significant at [Formula: see text]. Features like correlation, entropy and energy exhibits significance at [Formula: see text]. The feature extracted values provided significant difference between the normal and abnormal liver images. The neural network classifier yields the sensitivity of 95.8%, sensitivity of 81.4% and achieved the overall accuracy of 92%. Conclusion: A most accurate, reliable and fast automated method was implemented to segment the liver tumor image using Fast Greedy snakes algorithm. Hence the proposed algorithm resulted in effective segmentation and the classifier could classify the normal and abnormal images with greater accuracy.

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