Classification of heart sound recordings with continuous wavelet transform based algorithm
| Autor: | Mehmet Feyzi Aksahin, Busra Kubra Karaca, Erkin Kilic, Tugce Kantar, Burcu Oltu, Aykut Erdamar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Sound (medical instrument)
Phonocardiogram medicine.medical_specialty 020205 medical informatics medicine.diagnostic_test business.industry Nearest neighbour algorithm 02 engineering and technology Disease Auscultation Audiology Statistical classification Heart sounds otorhinolaryngologic diseases 0202 electrical engineering electronic engineering information engineering Medicine 020201 artificial intelligence & image processing business Continuous wavelet transform |
| Zdroj: | SIU |
| DOI: | 10.1109/siu.2018.8404450 |
| Popis: | Cardiovascular diseases are the major cause of death in the world. Early diagnosis of heart diseases provide an effective treatment. Heart diseases can be diagnosed using data obtained from heart sounds. Heart sounds are listened by a physician with auscultation method and the disease diagnosis can vary depending on the physician's experience and hearing ability. For this reason, automatic detection of anomalies in heart sounds can give more objective results. In this study, features were obtained by processing phonocardiogram signals taken from Physionet database. The heart sounds are classified as normal and abnormal using these features and the k - nearest neighbor method. As a result, sensitivity, specificity and accuracy were determined as 100%, 96.1% and 98.2%, respectively. |
| Databáze: | OpenAIRE |
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