Name
Abstract | ||
|---|---|---|
Due to the high mortality associated with heart disease, there is an urgent demand for advanced detection of abnormal heart beats. The use of dynamic electrocardiogram (DCG) provides a useful indicator of heart condition from long-term monitoring techniques commonly used in the clinic. However, accurately distinguishing sparse abnormal heart beats from large DCG data sets remains difficult. Herein, we propose an efficient fine solution based on 11 geometrical features of the DCG PQRST(P-T) waves and an improved hierarchical clustering method for arrhythmia detection. Data sets selected from MIT-BIH are used to validate the effectiveness of this approach. Experimental results show that the detection procedure of arrhythmia is fast and with accurate clustering. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10916-016-0519-0 | J. Medical Systems |
Keywords | Field | DocType |
Abnormal heart beats,Arrhythmia detection,Clustering,DCG,Feature vector | Hierarchical clustering,Feature vector,Data set,Computer science,Support vector machine,Artificial intelligence,Cluster analysis,Machine learning,Heart disease | Journal |
Volume | Issue | ISSN |
40 | 7 | 1573-689X |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rongfang Bie | 1 | 150 | 7.29 |
| Shuaijing Xu | 2 | 0 | 1.69 |
| Guangzhi Zhang | 3 | 2 | 2.05 |
| Meng Zhang | 4 | 0 | 0.34 |
| Xianlin Ma | 5 | 0 | 1.01 |
| Xialin Zhang | 6 | 0 | 0.34 |