Biomedical Signal and Image Processing
There will NOT be an additional class session to make up for the snow day on Thursday, Feb 9.
1. We will discuss the missed material during lab sessions on Wednesday and Friday, Feb 15 and 17, from 10am-1pm. You only need to attend one session.
. Three ECG articles (by Wiggers, Reisner et al., and Fisch) plus the slides prepared for the cancelled lecture are posted here. Before attending lab next week, please read the Wiggers article and pages 1-5 of the Lab 1 handout.
3. Problem Set 1 is due on Thursday, Feb 16.
4. Hard copies of the three ECG articles and Problem Set 1 can be picked up in E25-518 on Friday, Feb 9 or in class on Tuesday, Feb 14.
Information regarding lab sessions:
1. A MATLAB self-test was emailed to the class list on Wednesday, Feb 8. If you cannot complete the self-test in 10-15 minutes, then you are encouraged to attend office hours on Friday, Feb 10, from 10am-noon in 14-0637.
2. Lab assignments: Preferences were evenly split between Wed/Fri, so we will not make formal assignments. Please choose one and attend consistently on that day.
3. 10AM lab talks: There will usually be an introduction to key concepts and MATLAB functions on both Wednesday and Friday. Please be present unless you have already informed us of a conflict.
4. Lab partners and collaboration policy: You can work on the labs individually or in pairs. You can choose a lab partner or we will help you find one. Regardless of whether you work in pairs or alone, you must write up all labs independently. Full guidelines are here.
|Lectures:||Time: Tuesday and Thursday, 9:30 - 11:00am
Location: 56-154 (map)
|Labs:||Section 1: Wednesday, 10:00am - 1:00pm
Section 2: Friday, 10:00am - 1:00pm
Location: 14-0637 (map)
|Staff:||Instructor: Julie Greenberg
Instructor: William (Sandy) Wells
Instructor: Elfar Adalsteinsson
|Overview:||This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.|
|Values:||In this class, we aim to serve a diverse community of students by creating an inclusive and supportive learning environment. Collectively, our behavior and actions should always reflect MIT's shared values of excellence, openness, integrity, and mutual respect. Moreover, a student's well being is always our first concern; academic accomplishments should never come at the expense of one's mental or physical health.|
|Assignments:||Problem sets and lab reports may be submitted as either:
Please do NOT submit both hard copy and electronic versions of the same assignment.Extensions: Requests for extensions beyond the original due date should be made in advance via email to firstname.lastname@example.org. Please explain the circumstances necessitating the extension and propose a revised due date. Here are some examples of the types of circumstances that will generally be met with sympathy and flexibility: illness, conference travel, interview travel, multiple major assignments due in other classes on the same day. Late Penalty: In the absence of an approved extension, late assignments will be penalized one full point for each two days past the original deadline. (Problem sets are graded out of 4 points; labs are graded out of 10 points.)
60% lab reports (5 total)
25% quizzes (2 total)
10% problem sets (5 total)
5% class participation
Problem sets are graded as follows:4: Few to no errors, indicating a thorough understanding of the material.
3: Some errors, suggesting an adequate understanding of the material.
2: Numerous errors, suggesting significant gaps in understanding of the material.
1: Incomplete, that is, some sections not attempted.
0: Missing or submitted late without prior arrangement.