Name
Abstract | ||
|---|---|---|
This paper introduces a novel method for classifying and predicting cardiac arrhythmia events via a special type of deterministic probabilistic finite-state automata (DPFA). The proposed method constructs the underlying state space and transition probabilities of the DPFA model directly from the input data. The algorithm was employed in the prediction of two types of cardiac events, supraventricular tachycardia (SVT) and atrial high-rate episodes (AHRE), with its performance compared to five other well-established methods. In all experiments, the proposed method achieved over 0.8 AUC for both SVT and AHRE prediction. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.bspc.2020.102200 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL |
Keywords | DocType | Volume |
Signal processing, Machine learning, Probabilistic finite-state automata, Cardiac arrhythmia prediction | Journal | 63 |
ISSN | Citations | PageRank |
1746-8094 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Li | 1 | 478 | 93.46 |
| Harm Derksen | 2 | 151 | 15.00 |
| Jonathan Gryak | 3 | 3 | 8.64 |
| Cheng Jiang | 4 | 1 | 2.72 |
| Zijun Gao | 5 | 0 | 0.68 |
| Winston Zhang | 6 | 0 | 0.68 |
| Hamid Ghanbari | 7 | 2 | 1.43 |
| Pujitha Gunaratne | 8 | 0 | 2.03 |
| Kayvan Najarian | 9 | 262 | 59.53 |