Signal Characterization of Atrial Arrhythmias using the Surface ECG
Author
Summary, in English
This doctoral thesis is comprised of five parts which deal with different signal processing problems in ECG-based characterization of atrial arrhythmias. Such characterization requires that the ventricular activity has first been cancelled in the ECG. In Part I, a new method for cancellation of the QRST complexes in recordings with atrial fibrillation is presented. The method is based on a spatiotemporal signal model which accounts for rapid variations in QRS morphology. The results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction.
In Part II, time--frequency analysis is considered for characterization of ECG signals with atrial fibrillation. Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions being appropriate to either short-- or long--term variations.
A new method for a detailed sequential characterization of atrial tachy-arrhythmias is presented in Part III. The method describes the prevailing signal waveform by a spectral profile which is used for robust estimation of frequency and amplitude. The harmonic pattern of the spectral profile is parameterized in order to further quantify the signal structure.
In Part IV, spectral analysis of a time series of frequency estimates is performed for the purpose of detecting autonomic modulation in patients with permanent atrial fibrillation. Autonomic modulation was detected in two of the eight patients included in the study.
Part V introduces two new tools - an atrial rhythm feature tracker and a frequency trend detector - which are based on the detailed description offered by signal parameters developed in Part III. The feature tracker summarizes the detailed signal parameters into more general features in order to provide an overview of the atrial signal, whereas the trend detector detects long-term changes in atrial repetition rate.
Together, the five parts constitute a system for atrial signal analysis, suitable for implementation as an add-on software to existing ECG systems. With the new methods, different mechanisms of atrial tachy-arrhythmias can be investigated and different treatments strategies can be evaluated.
In Part II, time--frequency analysis is considered for characterization of ECG signals with atrial fibrillation. Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions being appropriate to either short-- or long--term variations.
A new method for a detailed sequential characterization of atrial tachy-arrhythmias is presented in Part III. The method describes the prevailing signal waveform by a spectral profile which is used for robust estimation of frequency and amplitude. The harmonic pattern of the spectral profile is parameterized in order to further quantify the signal structure.
In Part IV, spectral analysis of a time series of frequency estimates is performed for the purpose of detecting autonomic modulation in patients with permanent atrial fibrillation. Autonomic modulation was detected in two of the eight patients included in the study.
Part V introduces two new tools - an atrial rhythm feature tracker and a frequency trend detector - which are based on the detailed description offered by signal parameters developed in Part III. The feature tracker summarizes the detailed signal parameters into more general features in order to provide an overview of the atrial signal, whereas the trend detector detects long-term changes in atrial repetition rate.
Together, the five parts constitute a system for atrial signal analysis, suitable for implementation as an add-on software to existing ECG systems. With the new methods, different mechanisms of atrial tachy-arrhythmias can be investigated and different treatments strategies can be evaluated.
Publishing year
2003
Language
English
Document type
Dissertation
Topic
- Electrical Engineering, Electronic Engineering, Information Engineering
Keywords
- Signalbehandling
- Signal processing
- Atrial fibrillation
- Atrial arrhythmias
- Frequency estimation
- Time-frequency analysis
- QRST cancellation
- ECG
Status
Published
Supervisor
- [unknown] [unknown]
Defence date
22 April 2003
Defence time
10:15
Defence place
Room E:1406, E-building, Lund Institute of Technology
Opponent
- Guy Carrault (Prof)