Principal component analysis (PCA) approach to segment primary components from pathological phonocardiogram

Heart auscultation (interpretation of heart sounds) is the primary tool used in screening patients for heart pathology, and they are usually found in the primary health care. In this paper, a method based on principal component analysis is proposed for segmenting heart sounds. Firstly, the signal is...

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Vydáno v:2014 International Conference on Communication and Signal Processing : 3-5 April 2014 s. 910 - 914
Hlavní autoři: Sankar, D Sandeep Vara, Roy, Lakshi Prosad
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2014
Témata:
Cardiac cycle
Feature extraction
Frequency measurement
Heart
heart auscultation
Hidden Markov models
Noise
Pathology
principal component analysis
segmentation
Shannon energy
Silicon
ISBN:1479933570, 9781479933570
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Abstract Heart auscultation (interpretation of heart sounds) is the primary tool used in screening patients for heart pathology, and they are usually found in the primary health care. In this paper, a method based on principal component analysis is proposed for segmenting heart sounds. Firstly, the signal is filtered to remove low frequency noises and decimated to consider only the frequencies which are of clinical significance. Then principal component analysis is used to extract the feature set which is envelope extracted using Shannon energy and sub-divided into individual cardiac cycles using variance based algorithm. Finally, the envelope is segmented by using cardiac periods of the signal. Any false segmentation is eliminated according to the subjective knowledge of the heart sounds. Experimental results show that the proposed statistical approach performs well for both normal and pathological heart sounds with segmentation accuracy of 97.7%.
AbstractList Heart auscultation (interpretation of heart sounds) is the primary tool used in screening patients for heart pathology, and they are usually found in the primary health care. In this paper, a method based on principal component analysis is proposed for segmenting heart sounds. Firstly, the signal is filtered to remove low frequency noises and decimated to consider only the frequencies which are of clinical significance. Then principal component analysis is used to extract the feature set which is envelope extracted using Shannon energy and sub-divided into individual cardiac cycles using variance based algorithm. Finally, the envelope is segmented by using cardiac periods of the signal. Any false segmentation is eliminated according to the subjective knowledge of the heart sounds. Experimental results show that the proposed statistical approach performs well for both normal and pathological heart sounds with segmentation accuracy of 97.7%.
Author Roy, Lakshi Prosad
Sankar, D Sandeep Vara
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  givenname: Lakshi Prosad
  surname: Roy
  fullname: Roy, Lakshi Prosad
  organization: Electronics and Communication Department in National Institute of Technology, Rourkela, India
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Snippet Heart auscultation (interpretation of heart sounds) is the primary tool used in screening patients for heart pathology, and they are usually found in the...
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StartPage 910
SubjectTerms Cardiac cycle
Feature extraction
Frequency measurement
Heart
heart auscultation
Hidden Markov models
Noise
Pathology
principal component analysis
segmentation
Shannon energy
Silicon
Title Principal component analysis (PCA) approach to segment primary components from pathological phonocardiogram
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