Data Integration for Medical Information Management

We present a new method for data integration and security by mixing medical waveforms and images with encrypted patient identifiers and unencrypted ancillary information, such as acquisition parameters, diagnostic comments and notes in textual, pictorial, and voice forms. We vary the sampling rate according to the instantaneous frequency of the signal. Redundant samples (or pixels) are eliminated and replaced by associative data which are labeled using a status string encoded based on the Huffman and run-length techniques. This method achieves both data compression and integration simultaneously, allows synchronized presentation of information from different sources by using multimedia technology, and provides data security features. Mingui Sun received a B.S. degree from the Shenyang Chemical Engineering Institute, China, in 1982, and M.S. and Ph. D. degrees in Electrical Engineering from the University of Pittsburgh in 1986 and 1989, respectively. He was a Graduate Student Researcher from 1985 to 1989 working on signal and image processing projects. Currently, he is a Associate Professor and an Associate Director of the Center for Clinical Neurophysiology in the Department of Neurosurgery at the University of Pittsburgh, and a Director of Research at Computational Diagnostics, Inc. His current research and development interests include advanced biomedical electronic devices, biomedical signal and image processing, sensors and transducers, biomedical instruments, artificial neural networks, wavelet transforms, time-frequency analysis, and the inverse problem of neurophysiological signals. He has over 160 publications in these areas. Qiang Liu received his B.S. and M.S. degrees in electrical engineering from Xidian University, Xian, China, in 1996 and 1999 respectively. He is currently a Ph.D. student at the University of Pittsburgh, Pittsburgh, USA. His further research interests include biomedical signal processing, medical imaging, and image/video segmentation, coding and transmission. Robert J. Sclabassi received the B.S.E. degree from Loyola University, Los Angeles, the M.S.E.E., Engineer in Electrical Engineering, and Ph.D. degrees in electrical engineering from the University of Southern California, and the M.D. degree from the University of Pittsburgh. He was employed in the Advanced Systems Laboratory at TRW, Los Angeles, and was a postdoctoral fellow at the Brain Research Institute at the University of California, Los Angeles. He was on the faculties of Department of Neurology and Biomathematics at UCLA until he joined the University of Pittsburgh. Dr. Sclabassi is currently a Professor of Neurological Surgery, Psychiatry, Electrical Engineering, Mechanical Engineering, Psychiatry, and Behavioral Neuroscience at the University of Pittsburgh. Dr. Sclabassi has published over 400 papers, chapters and conference proceedings. Dr. Sclabassi is a Registered Professional Engineer.