Name
Abstract | ||
|---|---|---|
Bundle branch block (BBB) is a common conduction block disease and can be diagnosed using electrocardiogram (ECG) signal in clinical practice. In this paper, a novel method was proposed to detect two types of BBB: right BBB (RBBB) and left BBB (LBBB) based on the combination of deep features and several kinds of expert features. We evaluated the proposed method on the MIT-BIH Arrhythmia database (AR) and China Physiological Signal Challenge 2018 database (CPSC). The proposed method achieved an accuracy of 99.96% (AR) in the class-oriented evaluation and an accuracy of 98.76% (AR) and 97.88% (CPSC) in the subject-oriented evaluation, better than the baseline methods. Experimental results show that our method would be a good choice for the detection of the BBB. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857485 | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Arrhythmias, Cardiac,Bundle-Branch Block,China,Electrocardiography,Humans | Computer vision,Heart beat,Computer science,Clinical Practice,Feature extraction,Artificial intelligence,Electrocardiography,Bundle branch block,Wavelet | Conference |
Volume | ISSN | ISBN |
2019 | 1557-170X | 978-1-5386-1312-2 |
Citations | PageRank | References |
1 | 0.40 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jing hu | 1 | 22 | 13.68 |
| wei zhao | 2 | 71 | 28.69 |
| Dongya Jia | 3 | 4 | 4.80 |
| Cong Yan | 4 | 3 | 2.09 |
| Hongmei Wang | 5 | 31 | 13.44 |
| Zhenqi Li | 6 | 4 | 3.11 |
| Tianyuan You | 7 | 1 | 1.07 |