CS5540: Computational Techniques for Analyzing Clinical Data

Relevant Links and Papers

Lecture 1: (27 January) Introduction — AED, drug side effects, epilepsy diagnosis; includes “Computational Techniques Applicable to Medical Data: One Clinician's View” (lecture given by Gary S. Dorfman, M.D.)

RO Cummins et al. A new rhythm library for testing automatic external defibrillators: performance of three devices. J. Am. Coll. Cardiol. 1998; 11:597–602.
Kerber et al. Automatic External Defibrillators for Public Access Defibrillation: Recommendations for Specifying and Reporting Arrhythmia Analysis Algorithm Performance, Incorporating New Waveforms, and Enhancing Safety. Circulation 1997; 95:1677–1682.
Irena Jekova et al. Bench study of the accuracy of a commercial AED arrhythmia analysis algorithm in the presence of electromagnetic interferences. Physiol. Meas. 2009; 30:695–705.
Brady, Harrigan and Chan. A Tale of Two ‘Pulseless Electrical Activity’ Cardiac Arrest Rhythm. Consultant 2007; 47(1).

Lecture 2: (29 January) Introduction — Detection, Estimation, Classification

Jeffrey I. Berman et al. Probabilistic streamline q-ball tractography using the residual bootstrap. NeuroImage 2008; 39(1):215–222.
Ashish Raj et al. Bayesian Parallel Imaging With Edge-Preserving Priors. Magn. Reson. Med. 2007; 57(1):8–21.
Ashish Raj, Gurmeet Singh, Ramin Zabih. MRF's for MRI's: Bayesian Reconstruction of MR Images via Graph Cuts. CVPR 2006.
Marcus Kaiser. Brain Architecture: a design for natural computation. Phil. Trans. R. Soc. A. 2007.
Michael D. Fox et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U.S.A. 2005; 102(27):9673–9678.
Patric Hagmann et al. &lqduo;Mapping Human Whole-Brain Structural Networks with Diffusion MRI. PloS ONE 2007; 2(7):e597.
Yong He, Zhang J. Chen and Alan C. Evans. Small-World Anatomical Networks in the Human Brain Revealed by Cortical Thickness from MRI. Cerebral Cortex 2007; 17(10):2407–2419.

Lecture 3: (3 February) General Methods for Analyzing 1D Data — zero-crossings, local averaging, convolution, matched filters.

Coming soon!

Lecture 4: (5 February) Linear Time Invariance — convolution, random variables, limit theorems, linearity, edge detection.

Wikipedia article on cross-correlation.

Lecture 5: (10 February) Transforms — Fourier and Wavelets.

Coming soon!

Lecture 6: (12 February) Classification — k-NN, validation, statistical classification

Coming soon!

Lectures 7, 9, and 10: (17/24 February; 3 March) Estimation — Least Squares, Maximum Likelihood, Maximum Entropy.

Adam Berger. Maximum Entropy and Exponential Models.
Katherine M. Davis. Lecture 3: Introduction to Applications [of CWT].
E Mark Haacke. Magnetic resonance imaging: physical principles and sequence design.
Zhi-Pei Liang and Paul C Lauterbur. Principles of magnetic resonance imaging: a signal processing perspective.
Ian Kaplan. A linear Algebra View of the Wavelet Transform.
Steven Kay. Fundamentals of statistical signal processing.
Andrew Moore. Maximum Likelihood Estimation Tutorial.
In Jae Myung. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 2003; 47(1):90–100.
Robi Polikar. The Wavelet Tutorial.
Ashish Raj. Improvements in magnetic resonance imaging using information redundancy. Ph.D. Thesis, Cornell University, May 2005.
Ramin Zabih. Parallel Imaging Project.

Lecture 11: (5 March) Decisions Based on Densities — Expectation Maximization.

Carlo Tomasi. Estimating Gaussian Mixture Densities with EM — A tutorial.

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