Skip to Main Content
 

Global Search Box

 
 
 
 

ETD Abstract Container

Abstract Header

Time-frequency and Hidden Markov Model Methods for Epileptic Seizure Detection

Zhu, Dongqing

Abstract Details

2009, MS, University of Cincinnati, Engineering : Electrical Engineering.
Epilepsy is a severe brain disease that affects more than 50 million individuals world-wide, which are about 1% of the world's population. A method capable of detecting seizures early enough to allow prompt medical treatment would greatly improve the life quality of patients with epilepsy. This thesis proposes two seizure detection methods based on Discrete Wavelet Transform (DWT) and Hidden Markov Model (HMM). 96.6% classification accuracy and good early seizure detection results (0.25 minute to 6 minutes before seizure onset) are achieved on two different data sets. Our methods are compared with two existing seizure detection algorithms and demonstrate great potentials for clinical applications in seizure detection and prediction.
Howard Fan, PhD (Committee Chair)
Carla Purdy, PhD (Committee Member)
William Wee, PhD (Committee Member)
115 p.

Recommended Citations

Citations

  • Zhu, D. (2009). Time-frequency and Hidden Markov Model Methods for Epileptic Seizure Detection [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1242070584

    APA Style (7th edition)

  • Zhu, Dongqing. Time-frequency and Hidden Markov Model Methods for Epileptic Seizure Detection. 2009. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1242070584.

    MLA Style (8th edition)

  • Zhu, Dongqing. "Time-frequency and Hidden Markov Model Methods for Epileptic Seizure Detection." Master's thesis, University of Cincinnati, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1242070584

    Chicago Manual of Style (17th edition)