Revision as of 20:35, 1 May 2011

UNDER CONSTRUCTION

Signals and Systems in Biomedical Engineering:

1. Physiological Systems Modeling – Physiological processes can often be modeled mathematically. An organism's environment (or the output from another body system) can be viewed as a signal, x(t).  The physiological system being modeled, h(t), will convert this signal into output, y(t).  The blood clotting mechanism can be illustrated as a system cascade:
   x(t) = the amount of protein released by damaged endothelium (skin) cells
   h1(t) = amplifying system
   w1(t) = the amount of clotting factor VII
   h2(t) = amplifying system
   w2(t) = the amount of factor XI converted to XIa
   and so forth... check out http://themedicalbiochemistrypage.org/blood-coagulation.html ... until the final output
   y(t) = amount of cross-linked fibrin (that makes the blood clot)
   Would you consider this systemmemoryless? Causal?
2. Image and Image Processing – X-ray, ultrasound, MRI (magnetic resonance imaging), and CT (computerized tomography) scans all convert analog physical signals to discrete computer signals. Systems (maybe even Fourier/Laplace/Z transforms we know!) are utilized to convert, filter, and combine these signals and produce the images used in diagnostics.
3. Bioinformatics – Gene sequencing requires techniques to translate DNA from chromosomes to digitally recordable information. Think of each nucleic acid as n = 0, 1, 2, ... for the discrete signal of the entire DNA sequence x[n]. (Maybe x[n] = 1, 2, 3, and 4 for adenine, guanine, cytosine, and thymine.) This signal must be broken into distinct genes and decoded.  These genes can be matched to their potential phenotypes (physical traits/functions) using the stored sequences. 
4. Proteomics – A proteome is the set of proteins produced by a species, similar to a set of genes in a genome. The study of proteomes is important in understanding cellular processes and infection, and the patterns related to diseases. In proteomics, engineers must develop hardware to measure protein levels rapidly and accurately. In this case, the CT signal may be the level of a specific protein, and a system may be necessary to convert this biological signal into a DT, digital signal.
5. Wireless and Mobile Technologies – Just as our phones and computers have gone wireless, so are our medical equipment, sensors, and surgical devices. These critical signals must be properly transmitted, so it is important to remember Professor Mimi’s lectures on signal carriers and CT/DT conversions, among other topics in ECE301.
6. BioSIGNALS – The electrical signals produced by the body are extremely useful in diagnostics:
   Electrocardiogram (ECG) – used to measure the electrical activity of the heart and diagnose conditions including blockages and arrhythmias. For more detailed information on diagnosis with ECGs, check out this website: http://www.bem.fi/book/19/19.htm
   Electromyogram (EMG) – used to measure the electrical activity of muscles and nerves to diagnose conditions including carpal tunnel syndrome and sciatica. Research at Northwestern University uses these signals to allow amputees to control mechanical limbs with their natural nerve signals. Targeted Muscle Reinnervation link.
   Electroencephalogram (EEG) – used to measure the electrical activity of the brain to diagnose conditions including epilepsy and comas. Biomedical research attempts to use these signals to allow people with paralysis to control a computer. <<cool link to electroencephalophone>>
   All of these signals require filtering of background noise and, often times, conversion from continuous time (CT) to discrete time (DT).
   

These and more applications of biomedical engineering can be found at http://www.embs.org/docs/careerguide.pdf

___________________

DTFT example problem:

Example: Consider the case of a single neuron with 2 inputs and 1 output: (neuron firing is all or nothing, so the sum of inputs must reach a certain threshold)

V1,in(t) 

(+)  V(t)  h(t) = 1 for V(t) > VT  Vout(t)

V2,in(t)  0 for V(t) < VT

Uses for Signals and Systems in Biomedical Industry

• Wireless monitoring - Athena GTX
• Modeling biological systems - Bloomberg (not financial)
• Filtering noise from biological systems - Cyberonics
  

Purdue Biomedical Research with Signals and Systems

Electrical Engineering

https://engineering.purdue.edu/ECE/Research/Areas/BiomedIS.whtml

https://www.projectrhea.org/rhea/index.php/Vaccine_Posters

Biophotonics and Medical Imaging

https://engineering.purdue.edu/BME/Research/BIO

Neuroengineering

https://engineering.purdue.edu/BME/Research/NE

Pedro Irazoqui

Systems Science and Engineering

https://engineering.purdue.edu/BME/Research/HCS

Rundell

Lawley

Other Purdue courses that use Signals and Systems related to Biomedical Engineering

These descriptions were taken from the myPurdue course catalog and Purdue BME course resources

• BME 495/420 - Control for Biomedical and Healthcare Engineering (Fall) - This course will present modern control theory fundamentals from the biomedical engineering perspective. The concepts of feedback control and open loop control will be presented with an emphasis on biological and healthcare systems. Theory for linear state space models and feedback controller design will be taught. Examples will be drawn from physiological regulation of cardiac output and ventilation, pacemeaker design, automated insulin delivery, and patient scheduling.

• BME 495/430 - Biomedical Imaging Modalities (Spring) - This course covers basic principles and modes of bioimaging methods for biomedical sciences. Topics include interaction of electromagnetic radiation with tissue, basic concepts in imaging and detection, basic modes of imaging modalities (e.g. reflection, transmission, absorption, and emission), and basic image processing/analysis. Model systems to be used to teach the topics include conventional imaging modalities such as optical imaging, optical microscopy, X-ray, computed tomography, ultrasound, magnetic resonance imaging, etc. This course also includes hands-on exercise that reinforces important concepts.

• BME 595/510 - Analog Integrated-Circuit Design (Fall) - new, taught by Dr. Pedro Irazoqui

• BME 595/511 - Biosignal Processing (Fall) - An introduction to the application of digital signal processing to practical problems involving biomedical signals and systems. Topic include: examples of biomedical signals; analysis of concurrent, coupled, and correlated processes; filtering for removal of artifacts; event detection, analysis of waveshape and waveform complexity; frequency domain characterization of signals and systems; modeling biomedical signal-generating processes and systems; analysis of nonstationary signals; pattern classification and diagnostic decision. MATLAB will be used throughout to provide numerous opportunities for hands-on application of the theory and techniques discussed to real-life biomedical signals.

• BME 521/ ABE 560 - Biosensors: Fundamentals and Applications (Fall) - An introduction to the field of biosensors and an in-depth and quantitative view of device design and performance analysis. An overview of the current state of the art to enable continuation into advanced biosensor work and design. Topics emphasize biomedical, bioprocessing, environmental, food safety, and biosecurity applications.

• BME 528/ ECE 528 - Measurement and Stimulation of the Nervous System (Spring) - Engineering principles addressing questions of clinical significance in the nervous system: neuroanatomy, fundamental properties of excitable tissues, hearing, vision, motor function, electrical and magnetic stimulation, functional neuroimaging, disorders of the nervous system, development and refinement of sensory prostheses.

• BME 560 - Modeling and Analysis of Physiological and Healthcare Systems - Introduces students to healthcare engineering research through a variety of delivery decision problems that can be formulated and analyzed with engineering techniques such as simulation and linear programming.

• ECE 438 - Digital Signal Processing with Applications - The course is presented in five units. Foundations: the review of continuous-time and discrete-time signals and spectral analysis; design of finite impulse response and infinite impulse response digital filters; processing of random signals. Speech processing; vocal tract models and characteristics of the speech waveform; short-time spectral analysis and synthesis; linear predictive coding. Image processing: two-dimensional signals, systems and spectral analysis; image enhancement; image coding; and image reconstruction. The laboratory experiments are closely coordinated with each unit. Throughout the course, the integration of digital signal processing concepts in a design environment is emphasized. Prof. Mimi teaches this one sometimes: https://www.projectrhea.org/rhea/index.php/2010_Fall_ECE_438_Boutin
  

• ECE 473 - Intro to Artificial Intelligence (Spring) - The course introduces fundamental areas of artificial intelligence: knowledge representation and reasoning; machine learning; planning; game playing; natural language processing; and vision.

• ECE 511/ PSY 511 - Psychophysics (Fall) - An examination of the relationship between physical stimuli and perception (visual, auditory, haptics, etc.). Includes a review of various methods for studying this relationship and of the mathematical and computational tools used in modeling perceptual mechanisms.
• 'ECE' 38200 - Feedback System Analysis And Design - In this course, classical concepts of feedback system analysis and associated compensation techniques are presented. In particular, the root locus, Bode diagram, and Nyquist criterion are used as determinants of stability.
• ECE 44500 - Modern Filter Design - Solution to the filtering approximation problem via Butterworth, Chebyshev, Elliptic, etc., approaches. Transfer function scaling and type transformations. Effects of A/D and D/A conversion. Digital filter design methods. Active filter design using operational amplifiers. Operation and design of switched capacitor filters. A laboratory for the construction of digital filters is provided.
• ECE 48300 - Digital Control Systems Analysis And Design - The course introduces feedback computer controlled systems, the components of digital control systems, and system models on the z-domain (z-transfer functions) and on the time domain (state variable representations.) The objectives for system design and evaluation of system performance are considered. Various discrete-time controllers are designed including PID-controllers, state and output feedback controllers, and reconstruction of states using observers. The systems with the designated controllers are tested by simulations.
• ECE 51300 - Diffraction, Fourier Optics, And Imaging - Modern theories of diffraction and Fourier optics for imaging, optical communications, and networking. Imaging techniques involving diffraction and/or Fourier analysis with application to tomography, magnetic resonance imaging, synthetic aperture radar, and confocal microscopy. Additional topics in optical communications and networking, including wave propagation in free space, fiber, integrated optics, and related design issues. Simulation studies, using Matlab and other software packages for analysis and design.
• ECE 52100 - Acoustics In Engineering And Medicine - An introduction to the uses of acoustics in medical imaging, flaw detection, blood flow measurement, and signal processing. Topics include physical acoustics, bulk, surface and plate waves, transducer design, ultrasonic lenses and mirrors, pulse echo and ultrasonic doppler systems, bulk and surface wave signal processing devices, holographic imaging, clinical applications of ultrasonic imaging, and acoustic flaw detection.
• ECE 52200 - Problems In The Measurement Of Physiological Events - Lectures devoted to the methods used to measure physiological events with demonstrations and laboratory exercises to emphasize the practical aspects of quantitative measurements on living subjects. The systems covered are cardiovascular, respiratory, central and peripheral nervous, gastrointestinal, and renal.
• ECE 52600 - Fundamentals Of MEMS And Micro-Integrated Systems (BME 58100) - Key topics in micro-electro-mechanical systems (MEMS) and biological micro-integrated systems; properties of materials for MEMS; microelectronic process modules for design and fabrication. Students will prepare a project report on the design of a biomedical MEMS-based micro-integrated system.
• ECE 53800 - Digital Signal Processing I - Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing.
• ECE 57000 - Artificial Intelligence - Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence

• ME 375 - System Modeling and Analysis - Introduction to modeling electrical, mechanical, fluid, and thermal systems containing elements such as sensors and actuators used in feedback control systems. Dynamic response and stability characteristics. Closed loop system analysis including proportional, integral, and derivative elements to control system response.