The Howard Gittis Student Center at Temple University
The IEEE Signal Processing in Medicine and Biology Symposium (SPMB14)

Howard Gittis Student Center, Temple University, 1755 N. 13th Street, Philadelphia, Pennsylvania, USA

December 13, 2014



  Call For Papers






The Philadelphia Section of the IEEE and the Philadelphia Section of the IEEE Signal Processing Society invite you to participate in a single-day symposium designed to promote synergy between the healthcare industries and signal processing researchers. This year's program features a special emphasis on big data applications. Featured topics include:

  • Analysis of Biomedical Signals
  • Medical Imaging and Analysis
  • Signal Processing in Bioinformatics
  • Brain-Computer Interfaces
  • Big Data Applications in Bioengineering
  • Machine Learning for Big Data
  • Nonlinear Dynamics and System Models
  • Data Resources and Evaluation Frameworks
Two plenary talks will be given:

Papers of general interest to the disciplines of signal processing and bioengineering are also welcome. The symposium format will include two plenary talks, oral presentations and poster sessions. We particularly encourage graduate students to participate and present their current research. Live technology demonstrations and exhibits are also encouraged.

Paper Submission:
Early Registration:
Final Submission:
Final Program:
September 1, 2014
October 15, 2014
November 15, 2014
November 25, 2014
December 1, 2014

The symposium is sponsored by IEEE-USA, Temple University's College of Engineering, The Neural Engineering Data Consortium, IEEE Region 2, and the Philadelphia Section of the IEEE.
Organizing Committee:

 General Chairs:
  Joseph Picone (Temple)
  Ivan Selesnick (NYU-Poly)

 Conference Co-Chair:
  Charles Rubenstein (Pratt)

 Program Chairs:
  Iyad Obeid (Temple)
  Mike Mayor (IEEE SPS)
  Gail Rosen (Drexel)

 Publications Chair:
  Georgios Lazarou (USA)

 Local Arrangements:
  Walt Wolansky (Temple)

IEEE Region 2  
IEEE Region 2 Philaldephia Section  
Temple University  
Temple University College of Engineering  
The Neural Engineering Data Consortium  

Please direct questions or comments to