| Autor: |
Domy Kristomo, Indah Soesanti, Risanuri Hidayat, Adi Kusjani |
| Rok vydání: |
2016 |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings. |
| ISSN: |
0094-243X |
| Popis: |
Heart auscultation is a screening method done by listening using a stethoscope for early diagnosis of heart disease, it is low cost and non-invasive, but it has a limitation of human hearing. This paper presents a noise-robust feature extraction method by combining and selecting a heart sound (HS) feature in time and frequency domain. Wavelet decomposition (WD) is used for noise removal. Features are extracted by AR-PSD and used as inputs for classification. The nine types of abnormal HS that were taken from Michigan Heart Sound Database were classified into nine categories. In this research, statistics features in time and frequency domain are used as additional features. Correlation-based Feature Selection (CFS) is used to select the best feature among 13 features extracted. The performance of a new feature set is called Feature Set 3 compared to CFS. The result shows that the proposed feature set achieves the highest level of accuracy. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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