Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks
| Autor: | Fernando Rios-Gutierrez, Rocio Alba-Flores, Glenn Nordehn, Stanley G. Burns, Khaled Ejaz, Nicholas Andrisevic |
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| Rok vydání: | 2005 |
| Předmět: |
Engineering
Sound Spectrography Noise reduction Biomedical Engineering Sensitivity and Specificity Pattern Recognition Automated Wavelet Artificial Intelligence Physiology (medical) Digital image processing medicine Humans Diagnosis Computer-Assisted Block (data storage) Principal Component Analysis Heart Murmurs Artificial neural network business.industry Reproducibility of Results Signal Processing Computer-Assisted Pattern recognition Heart sounds Principal component analysis Heart murmur Neural Networks Computer Artificial intelligence medicine.symptom business Algorithms Heart Auscultation |
| Zdroj: | Journal of Biomechanical Engineering. 127:899-904 |
| ISSN: | 1528-8951 0148-0731 |
| Popis: | This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%. |
| Databáze: | OpenAIRE |
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