Preface

Summary

Biomedical engineering programs have exploded in popularity and number over the past 20 years. In many programs, the fundamentals of engineering science are taught from textbooks borrowed from other, more traditional, engineering fields: statics, transport phenomena, circuits. Other courses in the biomedical engineering curriculum are so multidisciplinary (think of tissue engineering, Introduction to BME) that this approach does not apply; fortunately, excellent new textbooks have recently emerged on these topics. On the surface, numerical and statistical methods would seem to fall into this first category, and likely explains why biomedical engineers have not yet contributed textbooks on this subject. I mean … math is math, right? Well, not exactly.

There exist some unique aspects of biomedical engineering relevant to numerical analysis. Graduate research in biomedical engineering is more often hypothesis driven, compared to research in other engineering disciplines. Similarly, biomedical engineers in industry design, test, and produce medical devices, instruments, and drugs, and thus must concern themselves with human clinical trials and gaining approval from regulatory agencies such as the US Food & Drug Administration. As a result, statistics and hypothesis testing play a bigger role in biomedical engineering and must be taught at the curricular level. This increased emphasis on statistical analysis is reflected in special “program criteria” established for biomedical engineering degree programs by the Accreditation Board for Engineering and Technology (ABET) in the USA.