Name
Title | ||
|---|---|---|
A Patient-Specific Methodology For Prediction Of Paroxysmal Atrial Fibrillation Onset |
Abstract | ||
|---|---|---|
In spite of the progress in management of Atrial Fibrillation (AF), this arrhythmia is one of the major causes of stroke and heart failure. The progression of this pathology from a silent paroxysmal form (PAF) into a sustained AF can be prevented by predicting the onset of PAF episodes. Moreover, since AF is caused by heterogeneous mechanisms in different patients, as we demonstrate in this paper, a patient-specific approach offers a promising solution. In this work, we consider two ECG recordings, one close to PAF onset and one far away from any PAF episode. For each patient, we extract two 5-minute ECG segments approximately 20 minutes apart. Next, we train a linear Support Vector Machine (SVM) classifier using patient-specific sets of time- and amplitude-domain features. In particular, we consider the P-waves and the QRS complexes in short windows of 5 consecutive heart beats. Finally, we validate the method on the PAF Prediction Challenge (2001) PhysioNet database predicting the onset with an F1 score of 97.1%, sensitivity of 96.2% and specificity of 98.1%. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.22489/CinC.2017.285-191 | 2017 COMPUTING IN CARDIOLOGY (CINC) |
Field | DocType | Volume |
Heart failure,Internal medicine,Management of atrial fibrillation,Cardiology,Anesthesia,Stroke,QRS complex,Paroxysmal atrial fibrillation,Medicine | Conference | 44 |
ISSN | Citations | PageRank |
2325-8861 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabetta De Giovanni | 1 | 4 | 1.77 |
| Amir Aminifar | 2 | 78 | 12.31 |
| Luca, A. | 3 | 1 | 2.05 |
| Sasan Yazdani | 4 | 26 | 5.50 |
| Jean-Marc Vesin | 5 | 201 | 32.09 |
| D. Atienza | 6 | 182 | 24.26 |