Name
Abstract | ||
|---|---|---|
The advent of portable cardiac monitoring devices has enabled real-time analysis of cardiac signals. These devices can be used to develop algorithms for real-time detection of dangerous heart rhythms such as ventricular arrhythmias. This paper presents a Markov model based algorithm for real-time detection of ventricular tachycardia, ventricular flutter, and ventricular fibrillation episodes. The algorithm does not rely on any noise removal pre-processing or peak annotation of the original signal. When evaluated using ECG signals from three publicly available databases, the model resulted in an AUC of 0.96 and F1-score of 0.91 for 5-second long signals and an AUC of 0.97 and F1-score of 0.93 for 2-second long signals. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856504 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
Ventricular Tachycardia, Ventricular Fibrillation, Machine Learning, Markov model, ECG, signal processing | Markov process,Computer science,Ventricular tachycardia,Artificial intelligence,Electrocardiography,Noise removal,Ventricular flutter,Cardiac monitoring,Computer vision,Ventricular fibrillation,Markov model,Internal medicine,Cardiology | Conference |
Volume | ISSN | Citations |
2019 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Li | 1 | 478 | 93.46 |
| Harm Derksen | 2 | 151 | 15.00 |
| Jonathan Gryak | 3 | 3 | 8.64 |
| Mohsen Hooshmand | 4 | 0 | 0.68 |
| Alexander Wood | 5 | 0 | 1.01 |
| Hamid Ghanbari | 6 | 0 | 0.68 |
| Pujitha Gunaratne | 7 | 0 | 2.03 |
| Kayvan Najarian | 8 | 262 | 59.53 |