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Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software

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Conference

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

Design in the Curriculum

Tagged Division

Biomedical

Page Count

11

Page Numbers

24.232.1 - 24.232.11

Permanent URL

https://peer.asee.org/20123

Download Count

410

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Paper Authors

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Felipe L. Carvalho Florida Atlantic University

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Felipe L. Carvalho is a graduating senior in the Electrical Engineering program at Florida Atlantic University (FAU), Boca Raton - FL. At FAU, he is a member of the Innovation Leadership Honors Program and as part of his undergraduate studies, is currently working on his Honors Project "Biomedical Signal Processing." Additionally, he is a co-op at BlackBerry, where he works closely with principles of telecommunications and software testing. He is a Tau Beta Pi certified member, where he holds the position of the Chair of Induction and Student Outreach Committee. Mr. Carvalho is also an IEEE student member for the 2013-2014 academic year. His research interests include Biomedical Engineering, Electromagnetics, Power, and Energy Systems.

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Ravi T. Shankar Florida Atlantic University

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Ravi Shankar is a senior professor with the engineering college at Florida Atlantic University (FAU), Boca Raton, FL. He has research and teaching interests in computer engineering and science, and electrical and biomedical engineering (see http://faculty.eng.fau.edu/shankar/ ). He is the director of a college-wide center that focuses on systems issues (http://csi.fau.edu/ ). In that capacity, he has built up teaching and research collaborations among professors and students from multiple colleges (arts and letters, business, education, engineering, K-12, and science). The collaborative work is documented at several subject specific FAU websites. He has a PhD from the University of Wisconsin, Madison, WI, and an MBA from Florida Atlantic University, Boca Raton, FL. His doctoral dissertation was on early and noninvasive detection of atherosclerosis. He has been issued 7 US Patents, with 3 of them in the biomedical area licensed for commercialization by Florida Atlantic University. He is a Fellow of the American Heart Association.

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Abstract

  1   Work in Progress—Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and SoftwareAbstract This paper describes a research project in the design of a Biomedical Signal Processing(BSP) Engineering Laboratory using low-cost hardware and software. This work in progress willbe considered successful upon the completion of seven laboratory guides that apply signalprocessing concepts to Biomedical Engineering (BME). We are using two boards from TexasInstruments (TI) Inc.: an OMAP L-138 Digital Signal Processing (DSP) board and a XDS100v2JTAG emulator. These two boards provide sufficient hardware functionality to design labexperiments for BSP. There is an unmet need to expose undergraduate students to BME in a simple andstraightforward manner. Application of digital signal processing to processing of biosignals(which are characterized by high common mode signal, low amplitude differential signal, lowfrequency noise, drift, and cycle to cycle variability) can be quite challenging and intimidating.A well designed and implemented lab course can suppress the details and provide easy to useinterfaces to get students started in the right direction. We hope this will enhance interest andembolden them to undertake further studies and research in the field in later years. Texas Instruments Inc (TI) has developed low cost boards and software that will simplifyexposure to DSP concepts [1]. TI’s DSP board and emulator are potentially free for anyuniversity developing and/or offering DSP labs; Our innovation here is in providing addedvalue. The developed lab guides will allow many institutions to quickly launch their own BSPlabs. All of the material developed throughout this project will be made available online on thegroup’s Smart Systems webpage. We have subdivided the lab into seven parts. We are designing each experiment to introducethe students to different aspects of BSP [2], in an incremental fashion, on the same original (a 3-lead electrocardiograph) signal. The lab particulars are provided below: 1. Emulation of typical external resources such as oscillators and oscilloscopes: This part of the project will be concerned with the utilization of software and low cost embedded hardware to generate electrical signals (thus replacing hardware oscillators) and to allow observation of electrical signals (thus replacing hardware oscilloscopes), respectively. If such functionality is incorporated, the cost of actual implementation of a BSP Lab will be substantially reduced. 2. Signal Transduction: Building an analog Electrocardiogram (ECG): This experiment will cover analog circuit instrumentation as relevant to BME. By the end of this experiment, the student will be familiar with analog circuits such as operational amplifiers, BJTs, electrodes, resistors and capacitors, and instrumentation concepts such as differential and common mode gain and low pass filtering. 3. ECG signal: Distinguishing normal sinus rhythm; This experiment will be designed to analyze the signals extracted from the ECG constructed in Experiment #2. For this purpose, the signal will be digitized and displayed on a PC-based oscilloscope. This will allow the student to observe the normal sinus rhythm in the ECG waveform and the naturally varying heart rate. 4. Signal Filtering: Reducing biosignal drift and offset; This experiment will cover the OMAP DSP board for filtering of a digital signal. This step will take the output of lab #3 for further processing.   2   5. Identification of the zero crossing points to average and display the averaged biosignal: This experiment will cover techniques for averaging the biosignal. Averaging is desired in order to visually observe normal and pathological ECG signals. We will also use it to extract features for automated pattern recognition. 6. Pattern recognition from the averaged signal: We will use the averaged signal to identify the normal sinus rhythm; this experiment will be an extension of Experiment #5. Pattern recognition is only possible after averaging the signal. 7. Simple diagnostics from pattern recognition: At the basic level, we wish to automatically detect cases of low and high heart rates. This is not pathological for a fairly broad range of heart rates. Students can achieve these via transcendental meditation (or deep and slow breathing) and in-place jogging, respectively. At the advanced level, we wish to extend the lab to identify pathological conditions such as myocardial infarction. For this, we will access available online databases to acquire such data. This experiment will cover techniques for feature extraction from biosignals and consequent diagnostics. This advanced part will serve to connect the lab work with real world applications. The anticipated outcomes of this project are: 1) To prepare the above listed tutorials and make them available online along with all the software and circuit schematics; and 2) To develop a comprehensive Biomedical DSP Engineering Lab that can be replicated easily. References [1] Welch, T.B., Wright, C.H.G. and Morrow, M.G., 2011, Real-time Digital Signal Processing from MATLAB to C with the TMS320C6x DSPs, CRC Press, Boca Raton, FL, 436p. [2] Tompkins, W.J. and Webster, J.G., 1981, Design of Microcomputer-Based Medical Instrumentation, Prentice Hall, Englewood Cliffs, NJ, 496p.

Carvalho, F. L., & Shankar, R. T. (2014, June), Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/20123

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