Probability and Applications in BME
BME 32200/ 3 Cr.
Probability theory and statistical methods are developed for life science applications. Analytical tools such as hypothesis testing, estimation of moments, sampling theory, correlation and spectral analysis are developed and applied to identifying underlying processes in biological systems, developing realistic models of physiological processes, designing experiments, and interpreting biological data.
- Available Online: No
- Credit by Exam: No
- Laptop Required: No
Prerequisites/Co-requisites:
P: BME 33100 and BME 33400. C: None.
Textbooks
Intuitive Probability and Random Processes using MATLAB by Steven Kay (2006), Springer. ISBN: 0-387-24157-4. Both electronic and printed handouts will also be distributed throughout the semester.
Instruction Goal
This course provides the foundational skills for advanced statistical analysis of biological signals. The basic analytical concepts of probability theory, statistical design of experiments and data analysis and representation of biological variables as random processes are demonstrated and practiced through computer based analysis of biologically relevant data sets provided throughout the course. All computational homework assignments are carried out using MATLAB. All laboratory exercises are carried out using LabVIEW.
Outcomes
Upon completion of the course, students will be able to:
- Solve probability problems with biomedical engineering applications using the basic axioms of probability [1]
- Describe the fundamental properties of probability density functions with applications to single and multivariate random variables [1,6]
- Describe the functional characteristics of probability density function frequently encountered in life-science research such as the Binomial, Uniform, Gaussian, and Poisson [1,6]
- Calculate confidence intervals and levels of statistical significance using fundamental measures of expectation and variance for a given biological data set [6]
- Quantify the correlation between sets of random processes for given mathematical functions and biological data sets [1,6]
- Identify the distribution function underlying a given biological process or medical application [1,6]
- Model and describe behavior of biological systems by means of random processes [1.3]
- Apply basic statistical concepts including descriptive statistics, Bayes Theorem, statistical inference, and hypothesis testing to biological and medical systems [1]
- Design a statistical experiment using appropriate control and test conditions [1,6]
- Apply statistical tools (e.g. Student’s t-test) to interpret experimental results [1,6]
- Apply statistics testing to the evaluation and acceptance of medical processes, devices, and systems [1]
Topics
BME 32200 is comprised of three interrelated subject areas, all involving the use of mathematical and computational tools to distill biological data into meaningful statistical representations. The first subject area broadly introduces the topic of probability theory (e.g. relative-frequency, set theory, and axioms of probability, conditional probability, independence, and Bernoulli trials) as related to sampled data. This leads to the introduction of random variables and distribution functions (e.g. probability density functions, mean values and moments, Gaussian random variables, density functions conditional density functions, joint distributions, covariance, sums of random variables) along with sampling and estimation theory (e.g. point and interval estimation, sampling distributions, estimation of means and variances, hypothesis testing, regression analysis and goodness-of-fit tests). The second subject area focuses upon random process definitions and measurement of random processes from biological signal sources (e.g. correlation, cross-correlation and applications to analysis of random processes from multiple biological sources). The third subject area utilizes recent articles from the scientific literature demonstrating the application of these and other mathematical processing techniques ( e.g. spectral density, properties of spectral density, and mean-square values from spectral density) in the study of biological signals and physiological systems. Refer to the lecture schedule for specific topics and dates.
