Hands-on curriculum teaches biomedical engineering concepts to home-schooled students.

University level outreach has increased over the last decade to stimulate K-12 student interest in engineering related fields. Home schooling students are one of the groups that are valued for engineering admissions due to diligent study habits and high achievement scores. However, home schooled students have inadequate access to science, math, and engineering related resources, which precludes the development of interdisciplinary teaching methods. To address this problem, we have developed a hands-on, STEM based curriculum as a safe and comprehensive supplement to current home schooling curricula. The ultimate goal is to stimulate university-student relations and subsequently increase engineering recruitment opportunities. Our pre and post workshop survey comparisons demonstrate that integrating disciplines, via the manner presented in this study, provides a K-12 student-friendly engineering learning method.

Software for biomedical engineering signal processing laboratory experiments.

In the early 1990's we developed a special computer program called UW DigiScope to provide a mechanism for anyone interested in biomedical digital signal processing to study the field without requiring any other instrument except a personal computer. There are many digital filtering and pattern recognition algorithms used in processing biomedical signals. In general, students have very limited opportunity to have hands-on access to the mechanisms of digital signal processing. In a typical course, the filters are designed non-interactively, which does not provide the student with significant understanding of the design constraints of such filters nor their actual performance characteristics. UW DigiScope 3.0 is the first major update since version 2.0 was released in 1994. This paper provides details on how the new version based on MATLAB! works with signals, including the filter design tool that is the programming interface between UW DigiScope and processing algorithms.

Evolution of microcomputer-based medical instrumentation.

This paper provides a historical review of the evolution of the technologies that led to modern microcomputer-based medical instrumentation. I review the history of the microprocessor-based system because of the importance of the microprocessor in the design of modern medical instruments. I then give some examples of medical instruments in which the microprocessor has played a key role and in some cases has even empowered us to develop new instruments that were not possible before. I include a discussion of the role of the microprocessor-based personal computer in development of medical instruments.

R-peak detection and signal averaging for simulated stress ECG using EMD.

This study used empirical mode decomposition (EMD) for R-peak detection in electrocardiogram signals in the presence of electromyogram-like noise. The EMG was modeled as random white Gaussian noise with a signal-to-noise ratio (SNR) in the range of around -10 dB to -20 dB. The EMD-based R-peak detection technique gives results comparable to those obtained with the Pan-Tompkins algorithm. The EMD technique is implemented for filtering of noisy ECG signals and is further compared with a traditional low-pass filtering approach. Finally signal averaging is performed using the EMD-based R-peak detection and filtering approach and compared with the standard signal averaging technique. We conclude that the EMD based technique for R-peak detection and filtering shows promise for enhancement of the stress ECG.

EMD-based 60-Hz noise filtering of the ECG.

This study used empirical mode decomposition (EMD) for filtering power line noise in electrocardiogram signals. When the signal-to-noise (SNR) is low, the power line noise is separated out as the first intrinsic mode function (IMF), but when the SNR is high, a part of the signal along with the noise is decomposed as the first IMF. To overcome this problem, we add a pseudo noise at a frequency higher than the highest frequency of the signal to filter out just the power line noise in the first IMF. The results are compared with traditional IIR-based bandstop filtering. This technique is also implemented for filtering power line noise during enhancement of stress ECG signals.