scholarly journals Regional Signal Recognition of Body Sounds

2021 ◽  
pp. 157-160
Author(s):  
Osman Balli ◽  
Yakup Kutlu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Neural Network Model ◽  
External Environment ◽  
The Body ◽  
Educational Success ◽  
Digital Analysis ◽  
Signal Recognition ◽  
Audio Signals ◽  
Different Parts

One of the most important signals in the field of biomedicine is audio signals. Sound signals obtained from the body give us information about the general condition of the body. However, the detection of different sounds when recording audio signals belonging to the body or listening to them by doctors makes it difficult to diagnose the disease from these signals. In addition to isolating these sounds from the external environment, it is also necessary to separate their sounds from different parts of the body during the analysis. Separation of heart, lung and abdominal sounds will facilitate digital analysis, in particular. In this study, a dataset was created from the lungs, heart and abdominal sounds. MFCC (Mel Frekans Cepstrum Coefficient) coefficient data were obtained. The obtained coefficients were trained in the CNN (Convolution Neural Network) model. The purpose of this study is to classify audio signals. With this classification, a control system can be created. In this way, erroneous recordings that may occur when recording physicians' body voices will be prevented. When looking at the results, the educational success is about 98% and the test success is about 85%.

Digital twin of the chip formation process

2020 ◽  
pp. 81-86
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
A.M. Kuz'mishina
Keyword(s):  
Neural Network ◽  
Control System ◽  
Crystal Lattice ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Formation Process ◽  
Chip Formation ◽  
Intelligent Control System ◽  
The Relationship

The formation of chips during the processing of various materials was studied. The relationship between the type of chips, the type of crystal lattice of the material and the number of sliding systems is shown. A neural network model of chip formation is developed, which allows predicting the type of chips. An intelligent control system for the process of chip formation during cutting is proposed. Keywords: chip formation, crystal lattice, neural network model, type of chips. [email protected]

scholarly journals Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Qun Lu ◽  
Aiyan Du ◽  
Yinghua Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dry Weight ◽  
Hemodialysis Patients ◽  
Normal Weight ◽  
The Body ◽  
Functional Link ◽  
Patient Will ◽  
Better Than

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient’s dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

Research on Classification of Gait Signal Based on Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 1081-1084
Author(s):  
Jing Yin
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Back Propagation ◽  
Healthy People ◽  
Signal Recognition ◽  
Characteristic Parameters ◽  
Error Back Propagation ◽  
The Neural Network

To effectively recognize gait signal between healthy people and patients with Parkinson, a gait signal recognition model is established based on neural network of error back propagation (EBP), and a method is proposed to effectively extract characteristic parameters. In this paper, coefficient of variation is applied in the research of gait-pressure multi-characteristic parameters through gait-pressure signal, and the neural network model can automatically recognize gait-pressure characteristics between healthy people and patients with Parkinson. This can contribute to the recognition and diagnosis of patients with Parkinson. Experiment results show a recognition rate of 90%.

scholarly journals An Efficient Neural Network Model for the Identification of Stress using Electrocardiogram

2019 ◽  
Vol 8 (2S6) ◽  
pp. 502-507
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
The Body ◽  
Mental Wellbeing ◽  
Daily Routine ◽  
Back Propagation Algorithm ◽  
Suggested Technique

People are facing numerous pressures in their daily routine in the latest society. Stress has traditionally has been described as action from a calm state to an emotional state in order to preserve the integrity of organism. Stress observation is very important for mental wellbeing and early identification of stress related disorders. Stress is to learn the body response in stressful state, whenever the body reaction is activated that means the heart rate and blood pressure will raise and several hormones enter our bloodshed. These hormones and bodily changes may increases our performances to a particular extent. Everyone's response to stress is discreet, and not all stress is bad. Someone may discover a significant condition of pressure to be enjoyable, while others may find it stressful. However, individuals also have different stress symptoms. stress area can also recognize using frequency and excitation of a speech signal, Since the biomedical signals are consistently related to central nervous system, therefore physiological parameters are the best way to understand the human emotions. The present work is focused on stress identification from Electrocardiogram using ECG physiologic net database, then entire environment of ECG signal characteristics i.e. mean heart rate variability (HRV), standard deviation of all R-R interval (SDNN), square root mean of the sum of the square difference between R-R interval (RMSSD) and number of consecutive R-R interval variations greater than 50ms (NN50), these features are extracted using Pan-Tompkins algorithm, then it is trained and validated to machine learning using back-propagation algorithm in neural network model. With the help of these features (mean HRV, SDNN, RMSSD and NN50), the study can be analyzed whether a person is under stress or not. Thus how the suggested technique provides the subjective information which helps the doctor to find out whether the person is under stress or not.

The Application of Neural Network in the Bionic Control System of an Artificial Heart Based on Neuroshell

2013 ◽  
Vol 709 ◽  
pp. 862-866
Author(s):  
Teng Jing ◽  
Fang Gun Wang ◽  
Kun Xi Qian
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Heart ◽  
Pressure Head ◽  
Activation Function ◽  
Rotating Speed ◽  
The Neural Network ◽  
Blood Pumps

With the development of heart pumps, more and more commercial artificial heart have been applied to clinic use. However, most of the products produce some discomfort to human body, which coudn’t meet the physiological requirements of patients. Therefore, further improvement and enhancement for these products are needed and adopting bionic control using neural network is an effective method to improve heart pumps’ comfort. A neural network model was established in this paper according to the relationship among the pressure head, the motor power and the rotating speed using using Based on neural network software Neuroshell2. Based on the defined appropriate activation function, a lot of data were studied and trained for optimization of the neural network model and determination of weights and deviations . Finally, the bionic control system was built and the experiments of the control system were conducted. The results reveal that the error measured values and the actual values was within 5% and acceptable and that the bionic control system using neural network is proved to improve the the comfort after implantation of blood pumps.

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