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- Ghareh Baghi, Ghareh BaghiLinköpings universitet,Institutionen för medicinsk teknik,Tekniska högskolan (författare)
Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
- BokEngelska2014
Förlag, utgivningsår, omfång ...
- Linköping :Linköping University Electronic Press,2014
- 81 s.
- printrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:liu-110182
- ISBN:9789175192529
- https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110182URI
Kompletterande språkuppgifter
- Språk:engelska
- Sammanfattning på:engelska
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Klassifikation
Serie
- Linköping Studies in Science and Technology. Dissertations,0345-7524 ;1616
Anmärkningar
- Aortic stenosis (AS) is one of the most prevalent valvular heart diseases in elderly people. According to the recommendations of both the American Heart Association and the European Society of Cardiology, severity assessment of AS is primarily based on echocardiographic findings. The experience of the investigator here play important roles in the accuracy of the assessment, and therefore in the disease management. However, access to the expert physicians could be limited, especially in rural health care centers of developing countries.This thesis aims to develop processing algorithms tailored for phonocardiographic signal with the intension to obtain a noninvasive diagnostic tool for AS assessment and severity grading. The algorithms employ a phonocardiogram as input signal and perform analysis for screening and diagnostics. Such a decision support system, which we call “the intelligent phonocardiography”, can be widely used in primary healthcare centers.The main contribution of the thesis is to present innovative models for the phonocardiographic analysis by taking the segmental characteristics of the signal into consideration. Three novel methodologies are described, based on the presented models, to perform robust classification. In the first attempt, a novel pattern recognition framework is presented for screening of AS-related murmurs. The framework offers a hybrid model for classifying cyclic time series in general, but is tailored to detect the murmurs as a special case study. The time growing neural network is another method that we use to classify short time signals with abrupt frequency transition. The idea of the growing frames is extended to the cyclic signals with stochastic properties for the screening purposes. Finally, a combined statistical and artificial intelligent classifier is proposed for grading the severity of AS.The study suggests comprehensive statistical validations not only for the evaluation and representation of systolic murmurs but also for setting the methodology design parameters, which can be considered as one of the significant features of the study. The resulting methodologies can be implemented by using web and mobile technologies to be utilized in distributed healthcare system.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Ask, Per,ProfessorLinköpings universitet,Institutionen för medicinsk teknik,Tekniska högskolan(Swepub:liu)peras02 (preses)
- Janerot Sjöberg, Birgitta,ProfessorDepartments of Clinical Science, Intervention and Technology, Karolinska Institutet & Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden (preses)
- Wiklund, Urban,DocentInst. för Strålningsvetenskaper, Medicinsk teknik, Umeå Universitet, Sverige (opponent)
- Linköpings universitetInstitutionen för medicinsk teknik (creator_code:org_t)
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