Pub Date : 1900-01-01DOI: 10.18127/j15604136-202101-04
N. T. Abdullaev, U. N. Musevi, K. Pashaeva
Formulation of the problem. This work is devoted to the use of artificial neural networks for diagnosing the functional state of the gastrointestinal tract caused by the influence of parasites in the body. For the experiment, 24 symptoms were selected, the number of which can be increased, and 9 most common diseases. The coincidence of neural network diagnostics with classical medical diagnostics for a specific disease is shown. The purpose of the work is to compare the neural networks in terms of their performance after describing the methods of preprocessing, isolating symptoms and classifying parasitic diseases of the gastrointestinal tract. Computer implementation of the experiment was carried out in the NeuroPro 0.25 software environment and optimization methods were chosen for training the network: "gradient descent" modified by Par Tan, "conjugate gradients", BFGS. Results. The results of forecasting using a multilayer perceptron using the above optimization methods are presented. To compare optimization methods, we used the values of the minimum and maximum network errors. Comparison of optimization methods using network errors makes it possible to draw the correct conclusion that for the task at hand, the best results were obtained when using the "conjugate gradients" optimization method. Practical significance. The proposed approach facilitates the work of the experimenter-doctor in choosing the optimization method when working with neural networks for the problem of diagnosing parasitic diseases of the gastrointestinal tract from the point of view of assessing the network error.
{"title":"Application of various optimization methods in calculating the error of a neural network for diagnosing parasitological diseases of the gastrointestinal tract","authors":"N. T. Abdullaev, U. N. Musevi, K. Pashaeva","doi":"10.18127/j15604136-202101-04","DOIUrl":"https://doi.org/10.18127/j15604136-202101-04","url":null,"abstract":"Formulation of the problem. This work is devoted to the use of artificial neural networks for diagnosing the functional state of the gastrointestinal tract caused by the influence of parasites in the body. For the experiment, 24 symptoms were selected, the number of which can be increased, and 9 most common diseases. The coincidence of neural network diagnostics with classical medical diagnostics for a specific disease is shown. The purpose of the work is to compare the neural networks in terms of their performance after describing the methods of preprocessing, isolating symptoms and classifying parasitic diseases of the gastrointestinal tract. Computer implementation of the experiment was carried out in the NeuroPro 0.25 software environment and optimization methods were chosen for training the network: \"gradient descent\" modified by Par Tan, \"conjugate gradients\", BFGS. Results. The results of forecasting using a multilayer perceptron using the above optimization methods are presented. To compare optimization methods, we used the values of the minimum and maximum network errors. Comparison of optimization methods using network errors makes it possible to draw the correct conclusion that for the task at hand, the best results were obtained when using the \"conjugate gradients\" optimization method. Practical significance. The proposed approach facilitates the work of the experimenter-doctor in choosing the optimization method when working with neural networks for the problem of diagnosing parasitic diseases of the gastrointestinal tract from the point of view of assessing the network error.","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202201-06
V. Chikhman, S. Solnushkin, V. Molodtsov, V. Smirnov, O. Lyubashina, I. Sivachenko
{"title":"Control of the vital parameters of the experimental animal","authors":"V. Chikhman, S. Solnushkin, V. Molodtsov, V. Smirnov, O. Lyubashina, I. Sivachenko","doi":"10.18127/j15604136-202201-06","DOIUrl":"https://doi.org/10.18127/j15604136-202201-06","url":null,"abstract":"","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121027995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202104-09
A.A. Zhilnikov, T.A. Zhilnikov, V. Zhulev
{"title":"Development and approbate of a measuring device for diagnosing the patient's condition during a magnetotherapy session","authors":"A.A. Zhilnikov, T.A. Zhilnikov, V. Zhulev","doi":"10.18127/j15604136-202104-09","DOIUrl":"https://doi.org/10.18127/j15604136-202104-09","url":null,"abstract":"","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122879111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202202-09
E. A. Yumatov, N. Karatygin, E. Dudnik, O. Glazachev, A.I. Filipchenko, L. T. Sushkova, R. V. Isakov, V.A. Al- Haidri, S. Pertsov
{"title":"Detection of the false and truthful state of a person’s brain based on the wavelet transform of the electroencephalogram of different brain structures","authors":"E. A. Yumatov, N. Karatygin, E. Dudnik, O. Glazachev, A.I. Filipchenko, L. T. Sushkova, R. V. Isakov, V.A. Al- Haidri, S. Pertsov","doi":"10.18127/j15604136-202202-09","DOIUrl":"https://doi.org/10.18127/j15604136-202202-09","url":null,"abstract":"","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117164288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202205-02
M. Yangirov, D. A. Kolesnikov, M. Al-harosh
{"title":"Wearable device for arterial pressure monitoring","authors":"M. Yangirov, D. A. Kolesnikov, M. Al-harosh","doi":"10.18127/j15604136-202205-02","DOIUrl":"https://doi.org/10.18127/j15604136-202205-02","url":null,"abstract":"","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"84 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120886986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202204-10
T. Vityazeva
{"title":"Methods of multi-rate signal processing in the problem of heart rate variability analysis","authors":"T. Vityazeva","doi":"10.18127/j15604136-202204-10","DOIUrl":"https://doi.org/10.18127/j15604136-202204-10","url":null,"abstract":"","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115278369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202102-05
L. Morozova, S. V. Savel’ev
{"title":"Physical properties of water solutions at high dilutions of basic substances","authors":"L. Morozova, S. V. Savel’ev","doi":"10.18127/j15604136-202102-05","DOIUrl":"https://doi.org/10.18127/j15604136-202102-05","url":null,"abstract":"","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132087431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202102-02
V. Gridin, I. Kuznetsov, A. Gazov, E. Sirota
The paper considers an integrated approach for constructing models for predicting the perioperative parameters of laparoscopic kidney resections, which include the duration of the operation, the time of thermal ischemia, and the glomerular filtration rate 24 hours after the operation. The approach is based on the principle of expanding the feature space, extracted from the analysis of the surgeon's "learning curve" data when mastering laparoscopic kidney resections. The aim of this work is to predict the main perioperative parameters that have the most significant impact on the surgical tactics of treatment at the stage of planning surgery. New methods have been developed for identifying significant parameters that take into account the complexity of the operation and the qualifications of the surgeon based on his “learning curve”. The parameters to be distinguished include: “complexity of the operation” based on nephrometric indices (RENAL, PADUA and C-index); the average value of the predicted perioperative parameters of surgical interventions depending on the complexity; slope and standard error based on the regression line of predicted perioperative parameters. Models were developed for predicting the perioperative parameters of laparoscopic organ-preserving kidney interventions using modern approaches based on machine learning, which are based on the algorithms “decision trees”, “multilayer perceptron”, “Naïve Bayes”, “logistic regression”. A comparative analysis of the quality of the developed models was carried out, as a result of which the best result was obtained using the “logistic regression” algorithm. The F-measure was used as a metric. A comparative analysis of the developed models was carried out to assess the impact on the final quality of the new selected features. For the predicted parameter “time of thermal ischemia” the increase was from 9.68% to 16.68%; for the predicted parameter “duration of surgery” the increase was from 2.76% to 4.08%. At the same time, for the predicted parameter “GFR in 24 hours” there was no significant increase, and for the “multilayer perceptron” algorithm it turned out to be negative. The obtained forecasting models can be used in applied software solutions that act as decision support systems in determining the surgical tactics of treating patients with localized formations of the renal parenchyma. Such software solutions can be implemented as a web service or as a separate program.
{"title":"Prediction of perioperative parameters of laparoscopic organ-sparing interventions on the kidney taking into account the surgeon's \"learning curve\"","authors":"V. Gridin, I. Kuznetsov, A. Gazov, E. Sirota","doi":"10.18127/j15604136-202102-02","DOIUrl":"https://doi.org/10.18127/j15604136-202102-02","url":null,"abstract":"The paper considers an integrated approach for constructing models for predicting the perioperative parameters of laparoscopic kidney resections, which include the duration of the operation, the time of thermal ischemia, and the glomerular filtration rate 24 hours after the operation. The approach is based on the principle of expanding the feature space, extracted from the analysis of the surgeon's \"learning curve\" data when mastering laparoscopic kidney resections. The aim of this work is to predict the main perioperative parameters that have the most significant impact on the surgical tactics of treatment at the stage of planning surgery. New methods have been developed for identifying significant parameters that take into account the complexity of the operation and the qualifications of the surgeon based on his “learning curve”. The parameters to be distinguished include: “complexity of the operation” based on nephrometric indices (RENAL, PADUA and C-index); the average value of the predicted perioperative parameters of surgical interventions depending on the complexity; slope and standard error based on the regression line of predicted perioperative parameters. Models were developed for predicting the perioperative parameters of laparoscopic organ-preserving kidney interventions using modern approaches based on machine learning, which are based on the algorithms “decision trees”, “multilayer perceptron”, “Naïve Bayes”, “logistic regression”. A comparative analysis of the quality of the developed models was carried out, as a result of which the best result was obtained using the “logistic regression” algorithm. The F-measure was used as a metric. A comparative analysis of the developed models was carried out to assess the impact on the final quality of the new selected features. For the predicted parameter “time of thermal ischemia” the increase was from 9.68% to 16.68%; for the predicted parameter “duration of surgery” the increase was from 2.76% to 4.08%. At the same time, for the predicted parameter “GFR in 24 hours” there was no significant increase, and for the “multilayer perceptron” algorithm it turned out to be negative. The obtained forecasting models can be used in applied software solutions that act as decision support systems in determining the surgical tactics of treating patients with localized formations of the renal parenchyma. Such software solutions can be implemented as a web service or as a separate program.","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.18127/j15604136-202101-01
S. Pertsov, E. A. Yumatov, N. Karatygin, E. Dudnik, A. Khramov, V. Grubov
It is a well-known fact that mental activity of the brain can be presented by two different states, i.e., the true state and the false state. A promising method of the electroencephalogram (EEG) wavelet transform has been developed over recent years. Using this method, we evaluated the principle possibility for direct objective registration of mental activity in the human brain. Previously we developed and described (published) a new experimental model and software for recognizing the true and false mental responses of a person with the EEG wavelet transform. The developed experimental model and software-and-data support allowed us to compare (by EEG parameters) two mental states of brain activity, one of which is the false state, while another is the true state. The goal of this study is to develop an absolutely new information technology for recognizing the true and false states in mental activity of the brain by means of the EEG wavelet transform. Our study showed that the true and false states of the brain can be distinguished using the method of continuous wavelet transform and calculation of the EEG wavelet energy. It was revealed that the main differences between truthful and false mental responses are observed in the delta and alpha ranges of the EEG. In the EEG delta rhythm, the wavelet energy is much higher under conditions of the false response as compared to that in the true response. In the EEG alpha rhythm, the wavelet energy is significantly higher with the true answer than in the false one. These data open a new principal possibility of revealing the true and false mental state of the brain by means of continuous wavelet transform and calculation of the EEG wavelet energy.
{"title":"Recognition of true and false states of the brain by means of the electroencephalogram wavelet analysis","authors":"S. Pertsov, E. A. Yumatov, N. Karatygin, E. Dudnik, A. Khramov, V. Grubov","doi":"10.18127/j15604136-202101-01","DOIUrl":"https://doi.org/10.18127/j15604136-202101-01","url":null,"abstract":"It is a well-known fact that mental activity of the brain can be presented by two different states, i.e., the true state and the false state. A promising method of the electroencephalogram (EEG) wavelet transform has been developed over recent years. Using this method, we evaluated the principle possibility for direct objective registration of mental activity in the human brain. Previously we developed and described (published) a new experimental model and software for recognizing the true and false mental responses of a person with the EEG wavelet transform. The developed experimental model and software-and-data support allowed us to compare (by EEG parameters) two mental states of brain activity, one of which is the false state, while another is the true state. The goal of this study is to develop an absolutely new information technology for recognizing the true and false states in mental activity of the brain by means of the EEG wavelet transform. Our study showed that the true and false states of the brain can be distinguished using the method of continuous wavelet transform and calculation of the EEG wavelet energy. It was revealed that the main differences between truthful and false mental responses are observed in the delta and alpha ranges of the EEG. In the EEG delta rhythm, the wavelet energy is much higher under conditions of the false response as compared to that in the true response. In the EEG alpha rhythm, the wavelet energy is significantly higher with the true answer than in the false one. These data open a new principal possibility of revealing the true and false mental state of the brain by means of continuous wavelet transform and calculation of the EEG wavelet energy.","PeriodicalId":169108,"journal":{"name":"Biomedical Radioelectronics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126585257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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