NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Development of Professional Communication Skills Throughout the BME Curriculum Abstract A sequence of six design courses are required in the undergraduate biomedical engineering curriculum at the University of Wisconsin-Madison. This sequence of courses provide a platform for students to develop and improve their oral and written communication skills. After taking a freshman engineering design course, each student admitted to biomedical engineering in the sophomore year does a team design project each semester for six sequential semesters. The teams work on progressively more challenging real-world projects submitted by clients from around the university and from industry. While advancing their technical and problem-solving skills through successive projects, the students also learn interpersonal and public communication skills through this experience. Introduction Beginning in 1998, we started teaching a sequence of design courses to all students in biomedical engineering beginning when they are admitted to the B.S. degree program in the first semester of their sophomore year1,2,3. Design courses throughout the curriculum form a unique feature of the BME undergraduate degree program. Every BME student registers for a design course and works on a client-based design project every semester for six consecutive semesters. These six design courses constitute a total of eight degree credits. All the courses are one credit except the 3-credit capstone design course in the first semester of the senior year. These design courses are supervised by faculty advisers. Each faculty member has a weekly two-hour meeting in a computer lab with his/her teams. The courses provide a platform for professional communication throughout the curriculum as well as a relevant structure to discuss many issues related to design including intellectual property, professionalism, entrepreneurialism, engineering ethics, and the need for lifelong learning. All the design projects are client-based, real-world design problems, solicited primarily from the medical and life sciences faculty around the university, as well as from biomedical engineering companies. Also we do projects with individuals who have specific rehabilitation needs. The design faculty team reviews the proposed projects and chooses those that are well matched to the students’ abilities and likely to result in physical prototypes. Once a team of four students is formed and chooses a project, the team interacts with their client and advisor to define the specifications for their project and maintains a dialog with their client throughout the course. The client provides meaningful feedback as the design progresses as well as access to the appropriate clinical or research setting. Faculty are fully responsible for all aspects of the design courses. We do not use teaching assistants. The Figure shows relationships among the six design courses. As part of the overall goals of learning the design process and creating a physical prototype, each of the courses has different individual goals.

Electrocardiogram (ECG) is the record of the heart muscle electric impulses. Received and processed ECG signal could be analyzed, and results could be used for detection and diagnostics of cardiovascular diseases (CVD).One of the important cardiovascular diseases is arrhythmia.This paper deals with improved ECG signal features Extraction using Wavelet Transform Techniques which may be employed for Arrhythmia detection. This improvement is

The purpose of this paper is to develop a new approach for R-peak detection in ECG and compare it with the most effective and existing algorithms. The proposed approach is based on DWT and envelope for the first stage of preprocessing and decision or detecting it was achieved by adaptive thresholding. The proposed algorithm is compared with Pan & Tompkins, Savitzky-Golay smoothing filter, Hilbert and wavelet transforms as well as fast Fourier transform algorithm to investigate these techniques of R peak detection and evaluate the wavelet-based algorithm comparing with them. The algorithms are evaluated in the experimental section using ECG signals from the MIT-BIH database. The results of detection algorithms show that the proposed wavelet-based algorithm gets the highest sensitivity by 99.9% with higher reliability compared to other algorithms, also by analyzing the precision of them, it’s come to light that FFT improved the highest precision with 99.7%.

Biomedical Engineering (BME) students at the University of Wisconsin-Madison participate in team-based design throughout the curriculum for six sequential semesters. Student teams work on hands-on, client-based, real-world biomedical design problems solicited from healthcare professionals, local industry, community members, and life sciences and clinical faculty. Through the design process, the students learn a variety of professional skills on topics including engineering notebooks, written and oral reports, engineering ethics, intellectual property, FDA approval, and animal/human subjects testing. The students also have the opportunity to learn as they are needed, various technical skills including computer-aided design, finite element analysis, machining/fabrication, electronics and electrical measurement and design, LabVIEW, MATLAB and microcontroller programming, mechanical testing, and basic laboratory techniques related to biomaterials and tissue engineering. As our student population has grown, we have had an increasing challenge to informally and effectively teach our students these cutting-edge skills that will enable them to be better engineers. In addition, our BME Student Advisory Committee (BSAC) has expressed interest in having more formal, directed training in a guided fashion early in the curriculum.In order to effectively teach these important professional, technical, and life-long skills, we developed a new sophomore-level lecture/laboratory course, BME 201, "Biomedical Engineering Fundamentals and Design." We offered it for the first time in Spring 2012, and it has been taught twice so far. The weekly lecture focuses directly on professional skills, and introduces students to the department's five areas of study (bioinstrumentation, biomedical imaging, biomechanics, biomaterials/cellular/tissue engineering, and healthcare systems) through lectures by faculty in those areas. These lectures were recorded during the first offering so that the videos can be viewed outside of class, and the lecture time can be repurposed for a more blended learning experience in future offerings thus creating weekly modules.The weekly laboratory period focuses on directly training the students in technical skills, such as those listed above that were previously offered on an ad hoc basis, in order train students to solve a multidisciplinary guided design project using these skills in teams. The laboratories were designed and are taught in conjunction with BME faculty instructors by undergraduate BME student assistants (SAs), allowing them to gain valuable teaching experience while giving our sophomore students an opportunity to learn from and interact with their peers. The guided design project requires the student teams to incorporate the knowledge and hands-on skills they learn during the semester to design and fabricate a bioreactor to measure the mechanical properties of soft biomaterials that they synthesize in our tissue engineering teaching lab. Throughout the project, the students maintain design notebooks, prepare product design specifications, create and present oral presentations, and communicate their design and results by preparing a technical report.As this is the only course where all sophomore BME students are together, we have had the unique opportunity to teach them in an open forum led by their upperclassmen peers. Through this multidisciplinary, blended, hands- on approach, early in the curriculum, students have obtained the skills they need to be successful in their future projects, to make informed decisions about their BME area of study and careers, and to enable them to become better engineers.

At our institution, the Biomedical Engineering Department implements design throughout the curriculum using six sequential, client-driven design courses. This affords us unique opportunities and challenges to assess student achievement of our educational outcomes. We have devised a novel assessment strategy that quantifies sophomore-, junior-and senior-level student achievement of nine educational outcomes, which are well aligned with ABET criteria. We now have five years of experience with this assessment strategy and five years-worth of data on student learning in our curriculum. In this paper, we present our strategy and a summary of our data to date. We also describe the process by which we make improvements to our curriculum through the assessment process. Finally, we suggest aspects of our approach that may be useful in more traditional BME curricula.

I loved your password-protected “Editorial” column. I couldn't agree with you more. Registering for the Annual IEEE Sarnoff Symposium in Princeton is very problematic. The Princeton Section Web site sends you off to some nationwide conference-registration company. Because I register only once a year, I certainly don't remember what my username and password are (although I could write it in my IEEE...

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.

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.

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.

Critical care nurses regard the ECG recording as an essential diagnostic tool for the immediate assessment of patients suffering from chest pain and for the routine screening of cardiac pathologies. In the same way, general nurses should perceive the ECG as another means of expanding their scope of professional practice which benefits the patients in their care.

Disease-related atrophy of the tongue muscles can lead to diminished lingual strength and swallowing difficulties. The devastating physical and social consequences resulting from this condition of oropharyngeal dysphagia have prompted investigators to study the effects of tongue exercise in improving lingual strength. We developed the Madison Oral Strengthening Therapeutic (MOST) device, which provides replicable mouth placement, portability, affordability, and a simple user interface. Our study (1) compared the MOST to the Iowa Oral Performance Instrument (IOPI), a commercial pressure-measuring device, and (2) identified the optimal tongue pressure sampling rate for isometric exercises. While initial use of the MOST is focused on evaluating and treating swallowing problems, it is anticipated that its greatest impact will be the prevention of lingual muscle mass and related strength diminishment, which occurs even in the exponentially increasing population of healthy aging adults.

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.

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.

We have studied the electrocardiogram (ECG) as a potential biometric for human identity verification. This research investigates the relationship between ECG biometric features and body mass index (BMI) using correlation analysis and linear regression methods. Using our ECG database of 168 normal healthy people (113 females and 55 males), we studied normalized features extracted from a one-lead, resting, palm ECG. The results showed that normalized ECG biometric features explain 25.3% of the variability of the BMI. ECG features of males better correlate with the BMI model than those of females. Furthermore, we calculated correlation coefficients and R-square changes to analyze the correlations between extracted features and the BMI and to indicate the most significant feature as a predictor of BMI among all ECG biometric features.

This work presents a technique for synthesizing realistic electrocardiogram (ECG) signals by morphing two different real ECG signals. The two parent signals are interpolated using spline approximation and then over-sampled. The characteristic feature points are extracted manually from the signals and they are partitioned into component curves between the feature points. The component curves are sampled to obtain the same number of data points for each curve and the feature points are matched. One-dimensional morphing is performed to generate intermediate signals. The intermediate signals are constrained within the physiological bounds of the two real ECG signals. The realistic ECG synthesized could find application for testing an ECG-based biometric identification system and for evaluating ECG signal processing algorithms.

In this study, we developed a wavelet-based algorithm for detecting and classifying four types of ventricular arrhythmias. We implemented the algorithm using four different wavelets and compared each result. For extracted arrhythmia episodes from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases, a Daubechies wavelet of length four gave the best result of the four different wavelets studied. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data.