Project Description - Biomedical Signal Processing

  • Pulse Oximetry

    Designed and implemented a more accurate and cost-effective portable pulse oximeter using spectral analysis. The pulse oximeter, a standard equipment in operating rooms, critical care units, and emergency health care, measures the percent oxygen saturation (SpO2) in hemoglobin.

    The main objective and the resulting contributions of this research addressed two aspects of designing a portable pulse oximeter. The first was to show the use of spectral analysis to calculate SpO2 values is a practical solution. The second was to identify alternate methods to Weighted Moving Average algorithm that is currently used to compute these values. The digital signal processing algorithms using Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT) were designed and evaluted to provide higher accuracy and improved response.

    For Details See the Relevant Publications

  • T. Rusch, R. Sankar, and J. Scharf, Signal Processing Methods for Pulse Oximetry, Computers in Biology and Medicine, Vol. 26, No. 2, pp. 143-159, March 1996.

  • Biomedical variables section: Blood Chemistry (with T. Rusch), The Measurement, Instrumentation and Sensors Handbook, J. G. Webster (Editor), CRC Press, 1996.

  • T. Rusch, J. Scharf, and R. Sankar, Alternate Pulse Oximetry Algorithms for SpO2 Computation, Proc. 16th Annual International Conf. of the IEEE Engineering in Medicine and Biology Society, Baltimore, MD, November 1994, pp. 848-849.

  • T. Rusch, Implementation and Design of a Portable Pulse Oximeter Using Spectral Analysis, M.S. Thesis, November 1994.

  • T. L. Rusch, R. Sankar, and J. E. Scharf, The Development of a Portable Pulse Oximeter for the Detection of Critical Hypoxemic Events in Non-surgical Patients, Group Technologies, Technical Report, December 1994.

  • EEG Signal Processing

    Developed algorithms for automatic detection of transients in EEG and enhanced using classification techniques. The transients considered include spikes and spike and wave bursts which are abnormal phenomena associated with epileptic activity. The classification was enhanced using both patient-dependent and patient-independent analyses.

    For Details See the Relevant Publications

  • R. Sankar and J. Natour, Automatic Computer Analysis of Transients in EEG, Computers in Biology and Medicine, Vol. 22, No. 6, pp. 407-422, November 1992.

  • R. Sankar and J. Goldstein, Computer-Aided Diagnosis of Epileptiform Transients in EEG, Proc. 21st IEEE Southeastern Symposium on System Theory, Tallahassee, FL, March 1989.

  • J. D. Natour, Automatic Detection and Classification of Epileptiform Transients in EEG, April 1988.

  • R. Sankar and J. Goldstein, Signal Processing and Pattern Recognition Approach to Transient Detection and Classification of EEG for the Diagnosis of Epilepsy, Proc. IEEE Southeastcon '87, Tampa, FL, April 1987, pp. 405-408.