A hybrid model for diagnosing sever aortic stenosis in asymptomatic patients using phonocardiogram
2015 (English)In: IFMBE Proceedings, Springer, 2015, 1006-1009 p.Conference paper (Refereed)Text
This study presents a screening algorithm for severe aortic stenosis (AS), based on a processing method for phonocardiographic (PCG) signal. The processing method employs a hybrid model, constituted of a hidden Markov model and support vector machine. The method benefits from a preprocessing phase for an enhanced learning. The performance of the method is statistically evaluated using PCG signals recorded from 50 individuals who were referred to the echocardiography lab at Linköping University hospital. All the individuals were diagnosed as having a degree of AS, from mild to severe, according to the echocardiographic measurements. The patient group consists of 26 individuals with severe AS, and the rest of the 24 patients comprise the control group. Performance of the method is statistically evaluated using repeated random sub sampling. Results showed a 95% confidence interval of (80.5%-82.8%) /(77.8%- 80.8%) for the accuracy/sensitivity, exhibiting an acceptable performance to be used as decision support system in the primary healthcare center.
Place, publisher, year, edition, pages
Springer, 2015. 1006-1009 p.
Aortic stenosis, Decision support, Hybrid model, Phonocardiogram, Primary healthcare centers, Algorithms, Artificial intelligence, Biomedical engineering, Blood vessels, Decision support systems, Diseases, Echocardiography, Health care, Hidden Markov models, Markov processes, Phonocardiography, Processing, Decision supports, Phonocardiograms, Primary healthcare, Diagnosis
Biomedical Laboratory Science/Technology
IdentifiersURN: urn:nbn:se:kth:diva-181542DOI: 10.1007/978-3-319-19387-8_245ScopusID: 2-s2.0-84944326372ISBN: 9783319193878OAI: oai:DiVA.org:kth-181542DiVA: diva2:907565
World Congress on Medical Physics and Biomedical Engineering, 2015, 7 June 2015 through 12 June 2015
QC 201602292016-02-292016-02-022016-02-29Bibliographically approved