**Friday, June 9, 2017 - Friday, June 30, 2017 from 10:00am to 11:50am**

One Capitol Square - 5009, Off-Campus

Welcome! This course is offered through the Virginia Commonwealth University, Department of Biostatistics. Course Logistics: Course number: BIOS 691-801, CRN #37377, Instructor: Shanshan Chen, Ph.D., 1 credit, Meeting dates: Fridays, June 9, 16, 23 and 30, 2017. Meeting time: 10:00 a.m. - 11:50 a.m. in One Capitol Square, 830 East Main Street, 5th floor, room 5009.
Motivation:
Recent advances in the collection of human physiological and behavioral data via electronic sensors and mobile devices has created a unique opportunity for medical research by enabling truly continuous, longitudinal monitoring through real-time time series measurement. Inherently different from the conventional (sparse in time) data usually encountered in biostatistics, this type of large dataset must be dealt with using signal processing techniques, requiring both a knowledge of the fundamentals of signal processing and programming skills.
Course Goals and Objectives:
To broaden the skill sets of students of biostatistics, this course aims to bridge the gap between sensor data collection and statistical analysis – namely biomedical signal processing. It is designed as a one credit course in four modules, to be offered in Summer 2017. The first module covers the basics of programming skills in Matlab. The second module covers the basics of signal processing. The third and fourth modules cover the physiological signals commonly observed in medical research. The specific topics covered in each module are listed below, although the content may slightly vary based on student feedback.
1. Introduction to Matlab
a. Pros and Cons of Matlab – when to use Matlab
b. Data structure and indexing – organize your data
c. Matrix operation and vectorization of code – speed up your code in Matlab
d. Functions, scripts, regular expressions and batch processing – organize your code
e. Parallel loops – speed up your iterations
f. Data plotting and visualization – data interpretation and sanity check
g. Statistical toolbox and curve fitting – tools for statistics students
2. Digital signal processing
a. Time series and digital signals – similarities and difference
b. Digital sampling and the Nyquist-Shannon theorem – how fast should one sample
c. Analog to digital converters (ADC) and quantization – getting data from electronics
d. Fourier Transforms and time and frequency domain representations – cornerstone transform in signal processing
e. Noise and filtering – how to remove noise in the sensor data
f. Windowing and feature extraction – how to separate a continuous stream of data for analysis
3. Biomedical signal processing: Part I – Inertial sensors
a. Basics of inertial sensors – some fun physics
b. Physical activities and energy expenditure – how your activity trackers work
c. Gait and movement analysis – how to analyze your walking patterns
d. Posture
4. Biomedical signal processing: Part II-ExG
a. ECG – signals of heart activities
b. EMG – signals of muscle activities
c. EEG – signals of brain activities
At the end of this course, students will be able to:
1. Employ basic programming techniques in Matlab, including: a) Importing heterogeneous datasets into Matlab using regular expressions and file reading functions; b) Becoming familiar with different types of data structures in Matlab and their usage c) Automating the processing work by actively writing functions
2. Explain the fundamental concepts of digital signals, such as sampling rate and signal to noise ratio
3. Recall the key filtering techniques and know the effects of filters on signals
4. Recognize and describe the commonly observed physiological signals
5. Acquire the basic skills required to extract features from biomedical signals
Computing Requirements
This course requires computing resources in class, thus will ideally take place in a computer-equipped classroom. Citrix Receiver must be pre-installed on the computers in order to access the VCU computing cluster and use Matlab. Detailed instructions for setting up the software tools will be sent out prior to the beginning of the class so that students can access the software on their personal computers as well.
Homework, Examinations and Grading
This is a one credit pass or fail course. A data processing problem will be posted after each module, to be solved by applying and integrating the programming skills demonstrated in the in-class exercises. For Module 1, 2 and 3, an inertial sensor dataset will be given to demonstrate how to import data to Matlab using automated functions (Module 1), how to visualize the dataset and how to use basic filters to remove noise (Module 2), and how to extract simple gait features from the data (Module3). For Module 4, a 3-lead ECG dataset will be given for heart rate extraction and a few QRS features extraction.
The homework should be submitted before the next class and will be graded as pass or fail depending on whether the program achieves the stated data processing goal. The homework will also be critiqued by the lecturer on coding style and efficiency. Each homework makes up 25% of the final grade. The students must achieve 75% (i.e. solve three homework problems correctly) in order to pass the course.
Plagiarism
Students are allowed to search on the Internet to find similar solutions and adapt the code for their own use, or discuss homework with their classmates. However, they are not allowed to copy and paste code from each other or directly from the Internet. Therefore, to demonstrate their independent thinking, they must comment their code in their own words. They must also include a ReadMe file to explain their code in their homework submission.
Textbook
Students are not required to purchase textbooks, although certain amount of literature reading will be given prior to each module, and students who are interested in the topics are encouraged to read the following textbooks:
1. Biomedical signal processing and signal modeling, Eugene N Bruce, New York: Wiley, 2001.
2. ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. Gacek A and Witold Pedrycz (Eds). Springer Science & Business Media; 2011 Sep 18. Available online via VCU network
3. The scientist and engineer's guide to digital signal processing, Steven W Smith SW. Available online.

- Sponsor(s)
- Medicine:Biostatistics
- Speaker(s)
- Shanshan Chen, Ph.D., Assistant Professor, Department of Biostatistics, Virginia Commonwealth University, Richmond, VA
- Audience
- All ( Open to the public )