UNDER CONSTRUCTION

Signals and Systems in Biomedical Engineering:

1.     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.

    All of these signals require filtering of background noise and, often times, conversion from continuous time (CT) to discrete time (DT).

2.     Physiological SYSTEMS Modeling – Physiological processes can often be modeled mathematically. One part of the body, or the environment, will produce a signal, x(t), which is converted to a new signal or action using a system, h(t).

3.     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. Algorithms – systems – must convert, filter, and combine these signals to produce the images we all know.

4.     Bioinformatics – Gene sequencing requires techniques to translate DNA from chromosomes to digitally recordable information. In terms of signals, think of each nucleic acid as n = 0, 1, 2, ... for the signal of the entire discrete sequence x[n]. (Maybe x[n] = 1, 2, 3, and 4 for adenine, guanine, cytosine, and thymine.) Furthermore, this information must be decoded into distinct genes, and these genes can be matched to their functions using the stored sequences. This is complex signals and systems beyond my undergrad capability...

5.     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 especially important in understanding cellular processes and infection, and the patterns related to diseases. In proteomics, hardware must measure protein levels rapidly and accurately. Engineers who have taken ECE 301 would be able to do this CT to DT conversion.

6.     Wireless and Mobile Technologies – Just as our phones and computers have gone wireless, so will 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.

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<span id="fck_dom_range_temp_1304134951377_19" />

Electrical Engineering

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

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 (for Biomedical Engineering)

• BME 495/420 - Control for Biomedical and Healthcare Engineering (Fall)
• BME 495/430 - Biomedical Imaging Modalities (Spring)
• BME 595/510 - Analog Integrated-Circuit Design (Fall)
• BME 595/511 - BIOSIGNAL PROCESSING (Fall)
• 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
• ECE 368 - Data Structures
• 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.

• ECE 441 - Distributed Parameter Systems
• 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 510 at IUPUI - Introduction to Biometrics
• 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.

• 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.

• ME 413 - Noise Control: Fundamentals of Acoustic Waves

Fundamentals of acoustic waves. Psychoacoustics and theories of hearing. Environmental and building acoustics. Measurement methods and common instrumentation. Noise control methods. Machinery noise. Community reaction. Legal aspects. Design-oriented semester project. Course work in differential equations.

• ME 588 - Mechatronics: Integrated Design of Electro-Mechanical Systems (Fall)

Electronic and interfacing techniques for design and control of electro-mechanical systems. Basic digital and analog design with applications to electro-mechanical interfacing via hands-on laboratory experience. Commonly used actuators and sensors and corresponding interfacing techniques. Realistic and integrated product development experience provided through a comprehensive final project where working prototypes are built to defined specifications.

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
(BIOL 563, VPH 522) 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 52800 - Measurement And Stimulation Of The Nervous System
(BME 52800) 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.

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