On methods for pattern recognition with application to epileptic electroencephalograms

Research output: ThesisDoctoral Thesis (compilation)

Abstract

The thesis treats methods for pattern recognition in multichannel electroencephalogram (EEG) signals, for application to diagnostics of epilepsy. Parts I-IV treat methods for feature extraction and clustering of EEG spikes, occurring between epileptic seizures, and part V presents a method for filtering seizure onset EEG signals. In part I, Hermite functions are used for parametric description of multichannel spikes, and the L-means method for clustering. The method is evaluated on real life signal sets. Part II treats the problem of making clustering methods statistically robust, and evaluates the fuzzy L-means method and the k-component graph-theoretic method in this respect. The effect of incorporation of the physical geometry of the electrode positions is also studied. In part III, the problem of alignment of the signals that are to be clustered is treated. Two new estimators are suggested and evaluated on simulated as well as real life signals. Part IV treats denoising of signal matrices under the assumption that the signals have low rank and the noise has full rank. A method is suggested consisting of representation in a basis of unit rank matrices, and algorithms for determination of such bases are given. Applicability to real life signals is demonstrated. Part V describes a technique for approximate Wiener filtering of non-stationary seizure onset EEG signals. The method relies on a time-frequency domain identity which is approximately valid for underspread stochastic processes, and estimates the Weyl symbol of the Wiener filter with aid of coherence functions between channels.

Details

Authors
  • Patrik Wahlberg
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Electrical Engineering, Electronic Engineering, Information Engineering

Keywords

  • clustering, EEG signal processing, epilepsy, statistical robustness, feature extraction, signal alignment, denoising, Wiener filtering, time-frequency analysis., Signal processing, Signalbehandling
Original languageEnglish
QualificationDoctor
Awarding Institution
Supervisors/Assistant supervisor
  • [unknown], [unknown], Supervisor, External person
Award date1999 Oct 15
Publisher
  • Patrik Wahlberg, Department of Applied Electronics, Lund University, P.O. Box 118, S-221 00 Lund, Sweden,
Publication statusPublished - 1999
Publication categoryResearch

Bibliographic note

Defence details Date: 1999-10-15 Time: 13:15 Place: Room E:1406, E-huset, LTH, Lund External reviewer(s) Name: Koski, Timo Title: Docent Affiliation: [unknown] ---