Biomedical sensors and signals
Description: The discipline is devoted to the study of the foundations of modern biomedical technology, the principles of construction, diagnostics and research of the characteristics of complex physical and technical complexes and devices, sensors, microelectronic and nanoelectronic sensors.
Amount of credits: 5
Пререквизиты:
- Integral and Microprocessor-based Circuit Engineering
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 15 |
| Practical works | |
| Laboratory works | 30 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
| SAW (Student autonomous work) | 75 |
| Form of final control | Exam |
| Final assessment method | exam |
Component: Component by selection
Cycle: Base disciplines
Goal
- The purpose of the discipline is the acquisition by students of a complex of knowledge and practical skills in the analysis of various biomedical signals, images and data (using the example of various systems of the human body), as well as in the processing of images obtained using visualization and functional diagnostics methods (pre-filtration method, selection of characteristic points of the object, segmentation, method of registration of electrical potentials, etc.).
Objective
- Study of principles, methods and algorithms for processing and analyzing biomedical signals, images, as well as methods for synthesizing appropriate software-algorithmic tools used in medical systems.
- Consideration of modern trends in the development of information technologies and prospects for their use in medicine.
- Acquisition of skills to work with scientific literature for the independent solution of research and applied tasks in this field of knowledge.
Learning outcome: knowledge and understanding
- Describe signal quality issues and ways to eliminate artifacts.
- Describe modern methods of digital processing of biosignals (filtering, time-frequency analysis, predictive modeling).
Learning outcome: applying knowledge and understanding
- Use digital signal processing tools (Python, MATLAB).
- Filter biosignals and eliminate artifacts (network interference, signal drift, physiological noise).
- Analyze biosignals using Fourier and wavelet transforms.
Learning outcome: formation of judgments
- Analyze the quality of biomedical signals and assess the reliability of the obtained data.
- Select the best data processing methods depending on the type of sensor and the purpose of the study.
Learning outcome: communicative abilities
- Describe and explain the principles of operation of biomedical sensors and their application in medicine, both to specialists and non-specialists
Learning outcome: learning skills or learning abilities
- Master new methods of digital signal processing and machine learning.
- Apply modern analytical tools for processing biosignals.
Teaching methods
interactive lecture (use of the following active forms of learning: guided (controlled) discussion or conversation; moderation; demonstration of slides or educational films);
search and research (independent research activities of students during the learning process).
Assessment of the student's knowledge
Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.
| Period | Type of task | Total |
|---|---|---|
| 1 rating | Laboratory work "Biomedical signals in functional diagnostics tasks: registration and analysis " | 0-100 |
| Laboratory work "Storage and processing of biomedical signals in medical systems" | ||
| Laboratory work "Initial analysis of biomedical signal parameters before processing" | ||
| Boundary control 1 | ||
| 2 rating | Laboratory work "Calculation of biomedical signal parameters after processing" | 0-100 |
| Laboratory work "Fourier analysis in biomedical signal processing" | ||
| Laboratory work "Wavelet analysis for detecting anomalies in biomedical signals" | ||
| Boundary control 2 | ||
| Total control | Exam | 0-100 |
The evaluating policy of learning outcomes by work type
| Type of task | 90-100 | 70-89 | 50-69 | 0-49 |
|---|---|---|---|---|
| Excellent | Good | Satisfactory | Unsatisfactory | |
| IInterview for practical work | demonstrates system theoretical knowledge, owns terminology, logically and consistently explains the essence of phenomena and processes, makes reasoned conclusions and generalizations, gives examples, shows fluency in monologue speech and the ability to quickly respond to clarifying questions | demonstrates solid theoretical knowledge, owns terminology, logically and consistently explains the essence of phenomena and processes, makes reasoned conclusions and generalizations, gives examples, shows fluency in monologue speech, but at the same time makes insignificant mistakes that he corrects independently or with minor correction by the teacher | demonstrates shallow theoretical knowledge, shows poorly formed skills of analyzing phenomena and processes, insufficient ability to draw reasoned conclusions and give examples, shows insufficient fluency in monologue speech, terminology, logic and consistency of presentation, makes mistakes that can be corrected only when corrected by a teacher | demonstrates system theoretical knowledge, owns terminology, logically and consistently explains the essence of phenomena and processes, makes reasoned conclusions and generalizations, gives examples, shows fluency in monologue speech and the ability to quickly respond to clarifying questions |
Evaluation form
The student's final grade in the course is calculated on a 100 point grading scale, it includes:
- 40% of the examination result;
- 60% of current control result.
The final grade is calculated by the formula:
| FG = 0,6 | MT1+MT2 | +0,4E |
| 2 |
Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;
E is a digital equivalent of the exam grade.
Final alphabetical grade and its equivalent in points:
The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:
| Alphabetical grade | Numerical value | Points (%) | Traditional grade |
|---|---|---|---|
| A | 4.0 | 95-100 | Excellent |
| A- | 3.67 | 90-94 | |
| B+ | 3.33 | 85-89 | Good |
| B | 3.0 | 80-84 | |
| B- | 2.67 | 75-79 | |
| C+ | 2.33 | 70-74 | |
| C | 2.0 | 65-69 | Satisfactory |
| C- | 1.67 | 60-64 | |
| D+ | 1.33 | 55-59 | |
| D | 1.0 | 50-54 | |
| FX | 0.5 | 25-49 | Unsatisfactory |
| F | 0 | 0-24 |
Topics of lectures
- Modern biomedical sensors: principles of operation and classification
- Methods for recording biomedical signals: problems and solutions
- Wireless sensor systems and IoT solutions in medicine
- Multimodal systems and data integration
- Preprocessing of biomedical signals: filtering, normalization, artifact removal
- Time and frequency analysis of biomedical signals
- Methods for analyzing the variability of biosignals and nonlinear characteristics
- Predictive modeling based on biosignals
- Machine learning methods for analyzing biomedical signals
- Deep neural networks and their application in signal analysis
- Computer vision in biomedical image processing
- Data fusion and decision-making algorithms
- Learnable patient monitoring systems and predictive modeling
- Biometric sensors and their application in medicine
- Integration of biomedical sensors into wearable devices
- The Future of Biomedical Sensors: Promising Technologies and Challenges
Key reading
- Umnyashkin, S.V. Fundamentals of the theory of digital signal processing [Electronic resource] : textbook / S.V. Umnyashkin. — Electron. text data. — M. : Technosphere, 2016. — 528 p.
- Skvortsov, S.P. Fundamentals of the use of the wavelet transform for filtering and compressing biomedical data [Electronic resource] : textbook / S.P. Skvortsov. — Electron. text data. — M. : Bauman Moscow State Technical University, 2012. — 68 p.
- Smolentsev, N.K. Introduction to the theory of wavelets [Electronic resource] / N.K. Smolentsev. — Electron. text data. — Moscow, Izhevsk: Regular and chaotic dynamics, 2010. — 292 p.
- Frolov A.V. Cifrovaya obrabotka bilmedicinskih signalov i izobrajenii. _ Moskva_ MGTU im. N.E.Baumana _2020._ 400s.
Further reading
- Kublanov V.S._ Dolganov A.Yu._ Kostousov V.B.Biomedicinskie signali i izobrajeniya v cifrovom zdravoohranenii_ hranenie_ obrabotka i analiz. – Ekaterinburg_ UrFU_ 2021. – 350 s.
- Gordon S._ Holden S.Biomedicinskie sensori i ustroistva. – Sankt_Peterburg_ Piter_ 2019. – 280 s.
- Makarova N.V.Statisticheskii analiz mediko_biologicheskih dannih. – Moskva_ Nauchnii centr zdorovya detei RAMN_ 2018. – 320 s.
- Rabinovich_ E.V. Metodi i sredstva obrabotki signalov [Elektronnii resurs] _ uchebnoe posobie / E.V. Rabinovich. – Elektron. tekstovie dannie. – Novosibirsk_ Novosibirskii gosudarstvennii tehnicheskii universitet_ 2009. – 144 c.
