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A hybrid model for diagnosing sever aortic stenosis in asymptomatic patients using phonocardiogram

Gharehbaghi, Arash (author)
Mälardalens högskola,Inbyggda system,Malardalen University, Sweden
Ask, Per (author)
Linköpings universitet,Fysiologisk mätteknik,Tekniska fakulteten
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
Ekman, Inger (author)
Linköpings universitet,Avdelningen för kardiovaskulär medicin,Medicinska fakulteten,Region Östergötland, Fysiologiska kliniken US
Lindén, Maria (author)
Mälardalens högskola,Inbyggda system,Malardalen University, Sweden
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.
In: IFMBE Proceedings. - Cham : Springer. - 9783319193878 - 9783319193861 ; , s. 1006-1009
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • 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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