A Multi-feature based Morphological Algorithm for ST Shape Classification
文献类型：会议论文
| 作者 | Shuqiong Fan; Fen Miao; Ruiqing Ma; Ye Li |
| 出版日期 | 2015 |
| 会议名称 | IEEE EMBC 2015 |
| 会议地点 | Milano |
| 英文摘要 | Abnormal ST segment is considered as an important parameter for the diagnosis of myocardial ischemia and other heart diseases. As most abnormal ST segments sustain for only a few seconds, it’s impractical for the doctors to detect and classify abnormal ones manually on time. Even though many ST segment classification algorithms are proposed to meet the rising demand of automatic myocardial ischemia diagnosis, they are often with lower recognition rate. The aim of this study is to detect abnormal ST segments precisely and classify them into more categories, and thus provide more detailed category information to help the clinicians make decisions. This study sums up ten common abnormal ST segments according to the clinical ECG records and proposes a morphological classification algorithm of ST segment based on multi-features. This algorithm consists of two parts: Feature points extraction and ST segment classification. In the first part, R wave is detected by using the 2B-spline wavelet transform, and mode-filtering method and morphological characteristics are used for other feature points extraction. In the ST segment classification process, ST segment level, variance, slope value, number of convex/concave points and other feature parameters are employed to classify the ST segment. This algorithm can classify abnormal ST segments into ten categories above. We applied the algorithm to the data provided from the European ST databases. The global recognition rate with 92.7% and the best accuracy of the algorithm with 97% demonstrated the effectiveness of the proposed solution. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/7282]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Shuqiong Fan,Fen Miao,Ruiqing Ma,et al. A Multi-feature based Morphological Algorithm for ST Shape Classification[C]. 见:IEEE EMBC 2015. Milano. |
入库方式： OAI收割
来源：深圳先进技术研究院
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