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  • 42 426: Biosensors and BioMEMS

    9.00 Credits
    Carnegie Mellon University

    This course emphasizes the principles of biomolecule-based sensing, including molecular recognition, biomolecular binding kinetics and equilibrium; methods of detection and signal transduction, including optical, colorimetric, fluorescence, potentiometric, and gravimetric techniques; statistical principles of high throughout screening; microfluidic and microarray device design principles and fabrication technologies; molecular motors. Prerequisites: 03-231 OR 03-232 Biochemistry.
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  • 42 431: Introduction to Biomedical Imaging and Image Analysis

    12.00 Credits
    Carnegie Mellon University

    This course gives an overview of tools and tasks in various biological and biomedical imaging modalities, such as microscopy, magnetic resonance imaging, x-ray computed tomography, ultrasound and others. Students will be exposed to the major underlying principles in modern imaging systems as well as state of the art methods for processing biomedical images such as deconvolution, registration, segmentation, pattern recognition, etc. The discussion of these topics will draw on approaches from many fields, including physics, statistics, signal processing, and machine learning. As part of the course, students will be expected to complete an independent project. Students will have the opportunity to visit laboratory to see real biomedical imaging devices in action. Prerequisites: 18-290 Signals and Systems or permission of the instructor, working knowledge of Matlab, and some image processing experience. Cross-listed courses: 18-496
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  • 42 441: Cardiovascular Biomechanics

    9.00 Credits
    Carnegie Mellon University

    This course covers the solid and fluid mechanics of the heart and vascular system as well as the mechanics of medical devices used to assist or replace cardiovascular function. Prerequisite: 42-341 Introduction to Biomechanics.
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  • 42 444: Medical Devices

    9.00 Credits
    Carnegie Mellon University

    This course is an introduction to the engineering, clinical, legal and regulatory aspects of medical device performance and failure. Topics covered include a broad survey of the thousands of successful medical devices in clinical use, as well as historical case studies of devices that were withdrawn from the market. In-depth study of specific medical devices will include: cardiovascular medicine, orthopedics, and general medicine. We will study the principles of operation (with hands-on examples), design evolution, and modes of failure. Additional lectures will provide basic information concerning biomaterials used for implantable medical devices (metals, polymers, ceramics) and their biocompatibility, mechanisms of failure (wear, corrosion, fatigue, fretting, etc.). The level of technical content will require junior standing for MCS and CIT students, a degree in science or engineering for non-MCS or non-CIT graduate students, or permission of the instructor for all other students.
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  • 42 447: Rehabilitation Engineering

    9.00 Credits
    Carnegie Mellon University

    Rehabilitation engineering involves the application of engineering principles to design, develop, adapt, and apply technology to problems confronted by individuals with disabilities. It differs from classical biomedical engineering by its focus on improving the quality of people's lives, rather than improving medical treatment. The course surveys assistive technologies for various functional limitations - including mobility, hearing, vision, communication, and cognition - applied to activities associated with independent living, education, employment, and integration into the community. We consider human factors and market forces that make some innovative technologies successful and others commercial failures. Engineering innovation by itself - without considering other factors ? means that some innovative technologies don?t become or remain available to aid people with disabilities. Pre-req: Completion of any engineering course with a grade of ?B? or better, or permission of the instructor for students without any engineering background. This course requires participation in day-long simulations of disabilities that may impact enrolled students? participation in their other courses on the days they simulate a disability (e.g. blindness or paraplegia)
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  • 42 511: Introduction to Molecular Biomaterials

    12.00 Credits
    Carnegie Mellon University

    This course will cover structure-processing-property relationships in biomaterials for use in medicine. The vast majority of this course will focus on natural biopolymers, synthetic polymers, and soft materials with additional minor treatment of metals and ceramics. Topics include basic chemical principles, macromolecular design, processing, characterization, and biodegradation mechanisms associated with synthetic polymeric materials. Applications of these materials in drug delivery and tissue engineering will also be discussed. Knowledge gained during the course will be applied in a team-based project in which students must design a novel synthetic polymer to address a specific clinical need. Pre-requisites: Senior in CIT or permission of instructor. Instructor has final permission on student enrollment. Also cross-listed as 27-511.
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  • 42 512: Basic Statistics for Biomedical Research

    9.00 Credits
    Carnegie Mellon University

    This is a lecture/seminar course designed to cover medical experimental design, types of statistical error and the mechanics of commonly used statistical methods. Emphasis will be placed on use of appropriate statistical tools as opposed to the mathematical underpinnings of the statistical tests themselves. Students will be expected to solve statistical problems derived from clinical practice as well as the medical literature. Web-based resources as well as a statistical software package will be provided. There is no textbook for the course. The biostatistics software package to be used for the course is Medcalc which is available as a free download for 25 uses (PC platform only) at www.medcalc.be. Students will also be directed to public-domain web sites which run Java applets capable of performing most of the problems presented in class. The instructor is Matthew R Quigley, MD., Associate Professor of Neurosurgery, Drexel University and staff neurosurgeon at Allegheny General Hospital. Dr Quigley has taught the Graduate Medical Education Biostatistics course at Allegheny General Hospital for the last 5 years and obtained extensive hands-on experience with experimental design and data analysis as the Chair of the Institutional Review Board, the oversight committee for all human research performed at the hospital.
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  • 42 540: Introduction to Biomedical Signal Processing

    9.00 Credits
    Carnegie Mellon University

    This course is geared towards graduate students who have not been exposed to signal processing before. The aim is to introduce the basic signal processing tools for analysis and mining of biomedical signals. These will include an introduction to digital sequences (1D and multiD), systems, and analysis tools (Fourier and wavelet). We will cover some basic tasks used in various biomedical processing applications. Students will team up in semester-long projects. Basic knowledge of Matlab is recommended but not required. Basic mathematics for engineers including basic linear algebra, or permission of the instructor, is required. This course is open to graduate students only.
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  • 42 580: Bioinstrumentation

    12.00 Credits
    Carnegie Mellon University

    This course aims to build the foundation of basic principles, applications and design of medical instrumentation. Topics covered include biosignals recording, transducers for biomedical application, action potentials EMG, EEG, ECG, amplifiers and signal processing, blood flow and pressure measurements, data acquisition and signal conditioning, spectral analysis of data, filtering, and safety aspects of electrical measurements. Ultimately, students will learn (1) how to apply basic circuit theory to perform measurement of biosignals, (2) be familiar and use common measurement devices, such as multimeter and oscilloscope, (3) be familiar with Op-amps circuits, (4) how to acquire and analyze a signal using time and frequency techniques, and (5) how to filter a signal to remove noise. Pre-requirements: Junior standing in CIT, 33-107 (Physics II for Engineers), or permission of the instructor.
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  • 42 590: Special Topics: Neural Signal Processing

    12.00 Credits
    Carnegie Mellon University

    The brain is among the most complex systems ever studied. Underlying the brain's ability to process sensory information and drive motor actions is a network of 1011 neurons, each making 103 connections with other neurons. Modern statistical and machine learning tools are needed to interpret the plethora of neural data being collected, both for (1) furthering our understanding of how the brain works, and (2) designing biomedical devices that interface with the brain. This course will cover a range of statistical methods and their application to neural data analysis. The statistical topics include latent variable models, dynamical systems, point processes, dimensionality reduction, Bayesian inference, and spectral analysis. The neuroscience applications include neural decoding, firing rate estimation, neural system characterization, sensorimotor control, spike sorting, and field potential analysis. Prerequisites: 18-290; 36-217, or equivalent introductory probability theory and random variables course; an introductory linear algebra course; senior or graduate standing. No prior knowledge of neuroscience is needed.
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