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A hybrid model for ...
A hybrid model for diagnosing sever aortic stenosis in asymptomatic patients using phonocardiogram
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- Gharehbaghi, Arash (author)
- Mälardalens högskola,Inbyggda system,Malardalen University, Sweden
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- Ask, Per (author)
- Linköpings universitet,Fysiologisk mätteknik,Tekniska fakulteten
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- Nylander, Eva (author)
- Linköpings universitet,Avdelningen för kardiovaskulär medicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Fysiologiska kliniken US
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- Janerot-Sjoberg, Birgitta (author)
- KTH,Skolan för teknik och hälsa (STH),Karolinska Institutet, Stockholm, Sweden,Karolinska Institute, Sweden; Karolinska University Hospital, Sweden; KTH Royal Institute Technology, Sweden
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- Ekman, Inger (author)
- Linköpings universitet,Avdelningen för kardiovaskulär medicin,Medicinska fakulteten,Region Östergötland, Fysiologiska kliniken US
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- Lindén, Maria (author)
- Mälardalens högskola,Inbyggda system,Malardalen University, Sweden
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- Babic, Ankica (author)
- Linköpings universitet,Medicinsk informatik,Tekniska fakulteten,University of Bergen, Norway
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(creator_code:org_t)
- Cham : Springer, 2015
- 2015
- English.
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In: IFMBE Proceedings. - Cham : Springer. - 9783319193878 - 9783319193861 ; , s. 1006-1009
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Subject headings
Close
- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi -- Biomedicinsk laboratorievetenskap/teknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology -- Biomedical Laboratory Science/Technology (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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