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. 10.18260/1-2--20123

\bibitem{asee_peer_20123}
  Carvalho, F. L., \& Shankar, R. T. (2014, June), \emph{Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software}
  Paper presented at 2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana.
  10.18260/1-2--20123

Felipe L. Carvalho and Ravi T. Shankar.  "Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software".  2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana, 2014, June.  ASEE Conferences, 2014.  https://peer.asee.org/20123 Internet.  18 Apr, 2024

\bibitem{asee_peer_20123}
  Felipe L. Carvalho and Ravi T. Shankar.
  "Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software".
  \emph{2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana, 2014, June}.
  ASEE Conferences, 2014.
  https://peer.asee.org/20123 Internet.  18 Apr, 2024

@INPROCEEDINGS{asee_peer_20123
author = "Felipe L. Carvalho and Ravi T. Shankar"
title = "Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software"
booktitle = "2014 ASEE Annual Conference \& Exposition"
year = "2014"
month = "June"
address = "Indianapolis, Indiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/20123}
number = {10.18260/1-2--20123}
}

TY  - CPAPER
AB  -                                                                                            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.
AU  - Felipe L. Carvalho
AU  - Ravi T. Shankar
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Biomedical Signal Processing: Designing an Engineering Laboratory Course Using Low-Cost Hardware and Software
UR  - https://peer.asee.org/20123
DO  - 10.18260/1-2--20123
ER  -