Heartbeat disease diagnosis using text-based approaches

Heart sound signals are the important asset for heart examination in primary healthcare centers to aid significantly in the diagnosis of heart diseases. Interpretation of heart sounds is a problematic and difficult skill that requires cardiology specialists. The diagnosis of heart disease from heart...

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Bibliographic Details
Main Author:	Khorasani, Ehsan Safar
Format:	Thesis
Language:	English English
Published:	2011
Subjects:	Heart - Diseases - Diagnosis - Programmed instruction Heart - Sounds - Programmed instruction Heart - Examination - Programmed instruction
Online Access:	http://psasir.upm.edu.my/id/eprint/27375/1/FSKTM%202011%2014R.pdf
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id	my-upm-ir.27375
record_format	uketd_dc
spelling	my-upm-ir.273752014-02-27T01:17:18Z Heartbeat disease diagnosis using text-based approaches 2011-12 Khorasani, Ehsan Safar Heart sound signals are the important asset for heart examination in primary healthcare centers to aid significantly in the diagnosis of heart diseases. Interpretation of heart sounds is a problematic and difficult skill that requires cardiology specialists. The diagnosis of heart disease from heart sound can differ between cardiologists and would require more detailed and expensive tests. However, heart disease diagnosis by heartbeat is preferable and still widely used as the first step to diagnosis. Computer aided auscultation has emerged as a costeffective technique to analyze and interpret the heart sounds. Digital heart sound recordings with background noise, similarity among heart diseases, recording environment conditions, auscultation body points makes detection of heart diseases complicated. There are several methods for automated detection and classification of heart diseases and heart sound analysis that have been proposed. Some of them used Artificial Neural Network method for detection and classification of heart sounds. Another technique that it used for diagnosis the heart problem is Hidden Markov Model (HMM) that they suggest HMM for segmentation of heart sound recorded for clinical and classification purpose. However, to the best knowledge of the researcher, no prior study has encoded heart sound to text string. In this study, we propose a feasible technique for developing a heartbeat sound retrieval system using text encoding techniques which is useful towards automated heart disease detection. The audio format of heart sound recordings are preprocessed and transcribed into the MIDI format. The MIDI files are then encoded to text strings using the pitch and duration information based on n-gram, these text strings then form musical words. These text strings are then indexed and tested for retrieval using both database and Information Retrieval (IR) systems. The Longest Common Subsequence (LCS) matching algorithm was used for identifying similarities from the database. With IR, full text indexing of the recordings was used and retrieved using known item searches from a search engine. The feasibility of these text encoding techniques were shown from retrieval experiments with around 100 digital heart sound recordings. Overall, experimental results performed clearly showed the feasibility of using proposed text encoding techniques for diagnosing heart problems. Thus, it can be said that the results presented for heart sound retrieval system are very promising for queries in Normal and Abnormal heart sound categories. Heart - Diseases - Diagnosis - Programmed instruction Heart - Sounds - Programmed instruction Heart - Examination - Programmed instruction 2011-12 Thesis http://psasir.upm.edu.my/id/eprint/27375/ http://psasir.upm.edu.my/id/eprint/27375/1/FSKTM%202011%2014R.pdf application/pdf en public masters Universiti Putra Malaysia Heart - Diseases - Diagnosis - Programmed instruction Heart - Sounds - Programmed instruction Heart - Examination - Programmed instruction Faculty of Computer Science and Information Technology English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Heart - Diseases - Diagnosis - Programmed instruction Heart - Sounds - Programmed instruction Heart - Examination - Programmed instruction
spellingShingle	Heart - Diseases - Diagnosis - Programmed instruction Heart - Sounds - Programmed instruction Heart - Examination - Programmed instruction Khorasani, Ehsan Safar Heartbeat disease diagnosis using text-based approaches
description	Heart sound signals are the important asset for heart examination in primary healthcare centers to aid significantly in the diagnosis of heart diseases. Interpretation of heart sounds is a problematic and difficult skill that requires cardiology specialists. The diagnosis of heart disease from heart sound can differ between cardiologists and would require more detailed and expensive tests. However, heart disease diagnosis by heartbeat is preferable and still widely used as the first step to diagnosis. Computer aided auscultation has emerged as a costeffective technique to analyze and interpret the heart sounds. Digital heart sound recordings with background noise, similarity among heart diseases, recording environment conditions, auscultation body points makes detection of heart diseases complicated. There are several methods for automated detection and classification of heart diseases and heart sound analysis that have been proposed. Some of them used Artificial Neural Network method for detection and classification of heart sounds. Another technique that it used for diagnosis the heart problem is Hidden Markov Model (HMM) that they suggest HMM for segmentation of heart sound recorded for clinical and classification purpose. However, to the best knowledge of the researcher, no prior study has encoded heart sound to text string. In this study, we propose a feasible technique for developing a heartbeat sound retrieval system using text encoding techniques which is useful towards automated heart disease detection. The audio format of heart sound recordings are preprocessed and transcribed into the MIDI format. The MIDI files are then encoded to text strings using the pitch and duration information based on n-gram, these text strings then form musical words. These text strings are then indexed and tested for retrieval using both database and Information Retrieval (IR) systems. The Longest Common Subsequence (LCS) matching algorithm was used for identifying similarities from the database. With IR, full text indexing of the recordings was used and retrieved using known item searches from a search engine. The feasibility of these text encoding techniques were shown from retrieval experiments with around 100 digital heart sound recordings. Overall, experimental results performed clearly showed the feasibility of using proposed text encoding techniques for diagnosing heart problems. Thus, it can be said that the results presented for heart sound retrieval system are very promising for queries in Normal and Abnormal heart sound categories.
format	Thesis
qualification_level	Master's degree
author	Khorasani, Ehsan Safar
author_facet	Khorasani, Ehsan Safar
author_sort	Khorasani, Ehsan Safar
title	Heartbeat disease diagnosis using text-based approaches
title_short	Heartbeat disease diagnosis using text-based approaches
title_full	Heartbeat disease diagnosis using text-based approaches
title_fullStr	Heartbeat disease diagnosis using text-based approaches
title_full_unstemmed	Heartbeat disease diagnosis using text-based approaches
title_sort	heartbeat disease diagnosis using text-based approaches
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/27375/1/FSKTM%202011%2014R.pdf
_version_	1747811588496162816

Heartbeat disease diagnosis using text-based approaches

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