Statistical analysis of signals from an intracavitary probe in a diseased heart.
A model study introduces the use of statistical signal processing to analyse the signals from an intracavitary probe. A complete derivation is given for the detection of one type of arrhythmogenic substrate, myocardial infarctions (MIs). Both the use of statistical signal processing and the detection of VT substrates, as opposed to activation maps, are unique. A quasi-stationary electromagnetic model with simplified geometry is presented. The model is used to simulate ventricular pacing in the presence of MI. The likelihood ratio is used for detection. A tabulation of the results from this model shows that an intracavitary probe can be used to detect MIs as small as 400 mm2 in 1 mV of noise with a detectability index of 0.495, where 0.5 indicates perfect detection. Sensitivity to noise can be reduced by analysing multiple heart beats. The results are only slightly affected by changing the probe from a cage frame design, which mechanically supports the electrodes on thin spokes, to a balloon design, which supports the electrodes on the surface of an insulating balloon.
Duke Scholars
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Related Subject Headings
- Signal Processing, Computer-Assisted
- Myocardial Infarction
- Models, Cardiovascular
- Humans
- Electrocardiography
- Computer Simulation
- Biomedical Engineering
- 4611 Machine learning
- 4603 Computer vision and multimedia computation
- 4003 Biomedical engineering
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Signal Processing, Computer-Assisted
- Myocardial Infarction
- Models, Cardiovascular
- Humans
- Electrocardiography
- Computer Simulation
- Biomedical Engineering
- 4611 Machine learning
- 4603 Computer vision and multimedia computation
- 4003 Biomedical engineering