Key result
A classification method using time-frequency techniques on short segments of surface electrocardiograms discriminated between atrial fibrillation subtypes with a global accuracy of 80%.
Why the study?
Does a classification method using the General Fourier-family Transform accurately discriminate between subtypes of atrial fibrillation using short ECG segments?
Does a classification method using the General Fourier-family Transform accurately discriminate between subtypes of atrial fibrillation using short ECG segments?
A classification method using time-frequency techniques on short ECG segments can discriminate between atrial fibrillation subtypes with 80% accuracy, showing potential for semiautomated diagnostic systems.
Atrial fibrillation patients can be classified into paroxysmal, persistent and permanent attending to the temporal pattern of this arrhythmia. The surface electrocardiogram hides this differentiation. A classification method to discriminate between the different subtypes of atrial fibrillation by using short segments of electrocardiograms recordings is presented. We will process the electrocardiograms (ECGs) using time-frequency techniques with a global accuracy of 80%. Real cases are evaluated showing promising results for an implementation in a semiautomated diagnostic system.
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Ortigosa et al. (2013) studied Atrial fibrillation. General Fourier-family Transform (time-frequency techniques) was evaluated on Classification of atrial fibrillation subtypes (paroxysmal, persistent, permanent). A classification method using time-frequency techniques on short segments of surface electrocardiograms discriminated between atrial fibrillation subtypes with a global accuracy of 80%.
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