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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pei-Chann Chang Jui-Chien Hsieh Jyun-Jie Lin Yen-Hung Chou Chen-Hao Liu |
| Copyright Year | 2009 |
| Abstract | This study presented a new diagnosis system with integrating 12-lead ECG data into a density model for increasing accuracy rate and flexibility of diseases detection. A hybrid system with HMMs and GMMs was employed for data classification. For myocardial infarction, data of lead-V1, V2, V3 and V4 were selected and HMMs were used not only to find the ECG segmentations but also to calculate the log-likelihood value which was treated as statistical feature data of each heartbeat’s ECG complex. The 4-dimension feature vector was clustered by GMMs and different numbers of distribution (disease and normal data) were examined in experiment. The main idea in this study relied on the multiple ECG channels which could be combined. There were total 1129 samples of heartbeats from clinical data, including 582 data with myocardial infarction and 547 normal data. The sensitivity of this diagnosis system achieved 79% and predictivity achieved 68.70% statistically. |
| Starting Page | 110 |
| Ending Page | 116 |
| File Size | 503872 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424446001 |
| DOI | 10.1109/HPCC.2009.66 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-25 |
| Publisher Place | Korea (South) |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cardiac disease Stochastic processes Information management Gaussian Mixture Models Support vector machines Myocardial Infarction High performance computing Hidden Markov models Myocardium Electrocardiography Cities and towns 12-Lead ECG Hidden Markov Models Cardiovascular diseases |
| Content Type | Text |
| Resource Type | Article |
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