Classifying heart conditions based on class probability output networks
| Autor: | Rhee Man Kil, Hee Yong Youn, Minseop Park, Han Bin Bae |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
medicine.diagnostic_test Computer science business.industry Cognitive Neuroscience Pattern recognition 02 engineering and technology Heart activity Signal Class (biology) Computer Science Applications Support vector machine 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Gamma distribution medicine 020201 artificial intelligence & image processing Artificial intelligence business Electrocardiography |
| Zdroj: | Neurocomputing. 360:198-208 |
| ISSN: | 0925-2312 |
| Popis: | This paper presents a novel method of classifying heart conditions from an electrocardiography (ECG) signal. For this purpose, the R-R intervals of ECG signal are analyzed by Gamma distribution parameters and classified into normal (NR) or abnormal (AN) ECG waves. For the normal ECG waves, the heart condition is further investigated by analyzing the dynamic behavior of heart activity based on the correlation between successive R-R intervals and long-term analysis. The classification of heart conditions is made by estimating the conditional class probabilities using class probability output networks (CPONs). The simulation for classifying heart conditions using the MIT-BIH data sets reveals that the proposed approach is effective for classifying heart conditions and allows more accurate classification than the existing classifiers such as the k-NN and SVM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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