Syllabus

Ternopil Ivan Puluj National Technical University

Каф. біотехнічних систем

Biomedical Processes and Signal Modelling

syllabus

1. Educational programs for which discipline is mandatory:

# Educational stage Broad field Major Educational program Course(s) Semester(s)
1 16. Хімічна та біоінженерія 163. Біомедична інженерія (бакалавр) Біомедична інженерія 3 5

2. The course is offered as elective for all levels of higher education and all educational programs.

3. Information about the author of the course

Full name Шадріна Галина Михайлівна
Academic degree Cand. Sc.
Academic title Assoc. Prof.
Link to the teacher`s page on the official website of the University http://library.tntu.edu.ua/personaliji/a/sh/shadrina-halyna-myhajlivna/
Е-mail (in the domain tntu.edu.ua) shadrina@tntu.edu.ua

4. Information about the course

Study hours structure Lectures: 32
Practical classes: 0
Laboratory classes: 32

Amount of hours for individual work: 56
ECTS credits: 4
Teaching language english
Form of final examination exam
Link to an electronic course on the e-learning platform of the university https://dl.tntu.edu.ua/bounce.php?course=5383

5. Program of discipline

Description of academic discipline, its goals, subject of study and learning outcomes

Purpose and objectives of the discipline
1. purpose: to prepare students for the study of complex systems and processes based on mathematical modeling methods.
2. objectives: to reveal the content of the basic concepts, object, methods and principles of modeling; give an idea of the types of modeling and the main hikes to constructing mathematical models of systems; research and optimization of biological processes and systems at different levels of their organization

The place of academic discipline in the structural and logical scheme of study according to the educational program

Prerequisites. List of disciplines, or knowledge and skills, possession of which students needed (training requirements) for successful discipline assimilation

Anatomy and physiology, Higher mathematics, Programming and computer technology

List of disciplines based on learning results from this discipline

Biomedical Signal Processing

Contents of the academic discipline

Lectures (titles/topics)

1 A Model Concept. A model. A little history. What is mathematical modeling? Why mathematical modeling? Basic numerical tasks. Numerical linear algebra. Numerical analysis. Numerical data analysis. Numerical functional analysis. Non-numerical algorithms. The modeling diagram. What are some limitations of mathematical models? How are mathematical models derived? Model’s adequacy degrees. Some models examples.
2 Some Theoretical Basis of Modeling. Modeling for prediction. Modeling methods. Biological processes and modeling. The modeling process. Validation and verification of the model.
3 Types of Models. Decision in a model designing. Degrees of a mathematical model complexity. Types of models. Computational models. Main computational modeling approaches. Graphs in modeling. Boolean/qualitative networks. Spatio-temporal models.
4 Biosignals and biological processes. What is biosignal? Permanent biosignals. Induced biosignals. Static biosignal. Dynamic biosignal. Classifications of biosignals.
5 Methods of biosignals models creation and processing. Types of signal to be modeled. How to create amodel for your problem. Using the modeling diagram. Constructing block diagram as a human organism state model. Analysis of block diagram. Mathematical modeling. Stages of mathematical modeling. Limitations and advantages of the mathematical modeling method. Basic concepts of digital signal processing. Fourier methods in signal processing. Filtration in signal processing and modeling. General methodology for time-frequency - (t,f) bio-signal analysis. Models as dynamic systems. Continuous models. Discrete models. The parameter problem. Measuring and calculating.
6 An Example of biosignal model creation and analysis. Biosignals learning and synthesis using deep neural networks. Deep neural networks and gated recurrent units. Character-level language model.
7 Electromyographic signals selection and pre-processing for a hand bio-controlled prosthesis. Electromiographic electrodes construction. Experimental results.
8 An electroencephalographic and electromyographic signals mathematical model for a task of a human communicative function renewal. Statement of a problem. Analysis of available investigations. Results of the investigation. Verification of the mathematical model. Conclusions.
9 A human communicative function indirect restoration method. Introduction. A method of human communicative function indirect restoration. Experimental results.
10 Detection of biomedical signals disruption using a sliding window. Introduction. The survey of available investigations. A problem statement. Results of the researches. Conclusion.
11 The speech process signs determination in electroencephalographic signals structure. Introduction. A problem statement. Criterion of the speech process beginning and completion time moments determination using electroencephalographic signals. Evaluation f the EEG signal processing results reliability. Conclusion.
12 Vocal signals mathematical model for the tasks of human vocal apparatus diagnostics. Introduction. Materials and methods. Grounding of a VFS mathematical model. Results.
13 Theoretical basis of phonocardiosignal modeling. A problem formulation. Resent researches analysis. Research results. Conclusions.
14 Twenty four hours pulse signal simulation algorithm. What is a pulse signal. A problem formulation. The twenty-four hours pulse signal structure. The twenty-four hours pulse signal simulation model and algorithm. Software for the twenty four hours pulse signal simulation. Conclusions.
15 How to develop new biosignals based products. What is PLUX. Accessing biosignals. Design and development process. PLUX ‘s methodological approach and toolkits. PLUX approach to design new products based on human biosignals. Quality assessment of biosignals.
16 Analysis of peripheral physiological data. Materials and methods. Electrocardiogram. Electrodermal activity. Electromyography. Continuous blood pressure. Impedance cardiography.

Practical classes (topics)

-

Laboratory classes (topics)

Safety instructions. Introduction to MATLAB.
Operators of control of the computing process in the environment of processing of biomedical information.
Operation with vectors and matrices.
Create non-simple file functions (procedures) in the MATLAB environment.
Functions of functions.
Graphic design of the results of processing biomedical signals in the form of 2d-graphs.
Graphic design of biomedical signal processing results in the form of 3d graphs.
Load biosignals and save processing results to a file.
Correlation processing of biosignals.
Spectral processing of biosignals.
Spectral correlation processing of biosignals.
Statistical processing of biosignals.
Wavelet processing of biosignals.
Single-phase processing.
Component method for processing biosignals.
Final discussion.

Individual work of a student / postgraduate student

Self study

№ Name of work Number of hours

1. Preparation for laboratory classes 14
Working out individual sections that are not presented at the lecture: 20
1. The concept of process, system, model. Model Properties. 2
2. Classification of types of modeling. Comparative analysis of types of modeling. 2
3. Simulation models of processes and systems. Terms of imitation modeling. 1
4. General scheme of the modeling process. Formalization and algorithmization of information processes. 1
5. Conceptual models of biological processes and systems. 1
6. Mathematical methods of modeling biological processes and systems. Logical structure of models. 2
7. Modeling in biology and medicine: the biological object of modeling, the properties of the bioprocess model and biosystem. 2
8. Modeling in biology and medicine: examples of models of biological processes and systems. 1
9. Estimation of accuracy and reliability of modeling results. Evaluate the stability of the model. 1
10. Estimation of accuracy and reliability of results of modeling. Assessment of the sensitivity of the model. 1
11. Review of computer programs of simulation and mathematical modeling 1
12. Properties of models of biomedical processes and signals. 1
13. Methods and methods of measurement, measurement errors. 1
14. Classification of research methods 1
15. Polynomial models, their calculation. 1
16. General scheme of the modeling process. Formalization and algorithmization of information processes. 1
Realization of course projects (works) 30
Preparation and preparation of the test, testing:
- Examination
- Test # 1
- Test # 2
- Intermediate control 2
0.5
0.5
0.5
0.5

Total hours 66

Learning materials and resources

1. Abakumov V.G. Biomedical signals. Genesis, processing, monitoring. Kyiv, 2001. 516 p.
2. Abakumov V.G. Biomedical signals and their processing. Kyiv, 1997. 352 p.
3. Orlenko N.S. Simulation modeling: teaching method. manual for self. studied dist. Kyiv, 1999. 208 p.
4. Filatova N.N. Modeling of biotechnical systems: a tutorial. Tver, 2008.144 p.
5. V.S. Pudov Modeling of processes and systems: met. decree. to laboratory work. Novosibirsk, 2005.32 p.
6. Fisher J, Henzinger TA: Executable Biology. Executable Biology. Proc. of the 2006 Winter Simulation Conference – Track on Modeling and Simulation in Computational Biology. 2006, 1675-1682.

6. Policies and assessment process of the academic discipline

Policies of the discipline

The policy of the subject is determined by the system of requirements that the teacher makes to the student when studying the discipline "Biomedical Processes and Signal Modelling " and is based on the principles of academic virtue.
Requirements relate to attending classes (inadmissibility of absences, delays, etc.); rules of conduct in the classroom (active participation, fulfillment of the required minimum of educational work, etc.) motivation and penalties (for which points can be awarded or deducted, etc.).
The policy of the academic discipline "Biomedical Processes and Signal Modelling " is built taking into account the norms of Ukrainian legislation regarding academic virtue, the Statute, the provisions of TNTU:
1 Regulations on the organization of the educational process at the Ternopil National Technical University named after Ivan Pulyui - order No. 4 / 7-340 of 05/21/2015 as amended on 06/25/2019 - order No. 4 / 7-622 of 06/27/2019 and of 04/14/2020 - order No. 4 / 7-243 dated 04/15/2020
2 Regulations on the individual curriculum of a student of Ternopil National Technical University named after Ivan Pulyuy (new edition) - order No. 4 / 7-669 of 09/25/2020
3 Regulations on academic mobility of participants in the educational process of Ternopil National Technical University named after Ivan Pulyuy
4 Regulations on the assessment of applicants for higher education of the Ternopil National Technical University named after Ivan Pulyuy (new edition) - order No. 4 / 7-670 of 09/25/2020
5 Provisions on academic mobility of students of TNTU named after I. Puluy - order No. 4 / 7-454 of 16.07.2013
6 Regulations on the settlement of conflict situations in the Ternopil National Technical University named after Ivan Pulyuy - order No. 4 / 7-164 of 03/01/2021
7 Regulations on the final semester control of the learning outcomes of students of the Ternopil National Technical University named after Ivan Pulyuy - order No. 4 / 7-122 of February 17, 2020
8 Temporary procedure for semester control and certification of applicants for higher education of Ternopil National Technical University named after Ivan Pulyuy - order No. 4 / 7-350 of 05/25/2020
Regulations on the prevention of academic plagiarism at the Ternopil National Technical University named after Ivan Pulyuy - order No. 4 / 7-964 dated 01.11.2019 as amended on 19.12.2019 order No. 4 / 7-114 dated 12.02.2020, as amended on 26.01.2021 - order No. 4 / 7-72 dated 02.02.2021
Regulations on the academic virtue of the participants in the educational process of the Ternopil National Technical University named after Ivan Pulyuy - order No. 4 / 7-969 dated 01.11.2019
Charter of Ternopil National Technical University. I. Pulyuya (new edition) - order of the Ministry of Education and Science No. 248 of February 25, 2019

Assessment methods and rating system of learning results assessment

Criteria of students’ progress assessment
Module 1 – 40 points:
10 points – test,
30 points – lab works.
Module 2 – 35 points:
10 points – test,
25 points – lab works.
Form of final control – examination – max 25 points


Table of assessment scores:

Assessment scale
VNZ
(100 points)
National
(4 points)
ECTS
90-100 Excellent А
82-89 Good B
75-81 C
67-74 Fair D
60-66 E
35-59 Poor FX
1-34 F

7. Additional Information

1 A Model Concept. A model. A little history. What is mathematical modeling? Why mathematical modeling? Basic numerical tasks. Numerical linear algebra. Numerical analysis. Numerical data analysis. Numerical functional analysis. Non-numerical algorithms. The modeling diagram. What are some limitations of mathematical models? How are mathematical models derived? Model’s adequacy degrees. Some models examples.
2 Some Theoretical Basis of Modeling. Modeling for prediction. Modeling methods. Biological processes and modeling. The modeling process. Validation and verification of the model.
3 Types of Models. Decision in a model designing. Degrees of a mathematical model complexity. Types of models. Computational models. Main computational modeling approaches. Graphs in modeling. Boolean/qualitative networks. Spatio-temporal models.
4 Biosignals and biological processes. What is biosignal? Permanent biosignals. Induced biosignals. Static biosignal. Dynamic biosignal. Classifications of biosignals.
5 Methods of biosignals models creation and processing. Types of signal to be modeled. How to create amodel for your problem. Using the modeling diagram. Constructing block diagram as a human organism state model. Analysis of block diagram. Mathematical modeling. Stages of mathematical modeling. Limitations and advantages of the mathematical modeling method. Basic concepts of digital signal processing. Fourier methods in signal processing. Filtration in signal processing and modeling. General methodology for time-frequency - (t,f) bio-signal analysis. Models as dynamic systems. Continuous models. Discrete models. The parameter problem. Measuring and calculating.
6 An Example of biosignal model creation and analysis. Biosignals learning and synthesis using deep neural networks. Deep neural networks and gated recurrent units. Character-level language model.
7 Electromyographic signals selection and pre-processing for a hand bio-controlled prosthesis. Electromiographic electrodes construction. Experimental results.
8 An electroencephalographic and electromyographic signals mathematical model for a task of a human communicative function renewal. Statement of a problem. Analysis of available investigations. Results of the investigation. Verification of the mathematical model. Conclusions.
9 A human communicative function indirect restoration method. Introduction. A method of human communicative function indirect restoration. Experimental results.
10 Detection of biomedical signals disruption using a sliding window. Introduction. The survey of available investigations. A problem statement. Results of the researches. Conclusion.
11 The speech process signs determination in electroencephalographic signals structure. Introduction. A problem statement. Criterion of the speech process beginning and completion time moments determination using electroencephalographic signals. Evaluation f the EEG signal processing results reliability. Conclusion.
12 Vocal signals mathematical model for the tasks of human vocal apparatus diagnostics. Introduction. Materials and methods. Grounding of a VFS mathematical model. Results.
13 Theoretical basis of phonocardiosignal modeling. A problem formulation. Resent researches analysis. Research results. Conclusions.
14 Twenty four hours pulse signal simulation algorithm. What is a pulse signal. A problem formulation. The twenty-four hours pulse signal structure. The twenty-four hours pulse signal simulation model and algorithm. Software for the twenty four hours pulse signal simulation. Conclusions.
15 How to develop new biosignals based products. What is PLUX. Accessing biosignals. Design and development process. PLUX ‘s methodological approach and toolkits. PLUX approach to design new products based on human biosignals. Quality assessment of biosignals.
16 Analysis of peripheral physiological data. Materials and methods. Electrocardiogram. Electrodermal activity. Electromyography. Continuous blood pressure. Impedance cardiography.

Approved by the department
Biotechnical systems
(protocol №
10
on «
17
»
03
2020
y.).