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A system for heart sounds classification.

Redlarski G, Gradolewski D, Palkowski A - PLoS ONE (2014)

Bottom Line: The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods.Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue.The respective system is compared with four different major classification methods, proving its reliability.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland.

ABSTRACT
The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases - one of the major causes of death around the globe - a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.

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Example heart sounds used in the tests.A – early systolic murmur, B – S4, C – pansystolic murmur, D – S3, E – late systolic murmur, F – normal split S2, G – normal split S1, H – ejection click, I – diastolic rumble, J – opening snap.
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pone-0112673-g007: Example heart sounds used in the tests.A – early systolic murmur, B – S4, C – pansystolic murmur, D – S3, E – late systolic murmur, F – normal split S2, G – normal split S1, H – ejection click, I – diastolic rumble, J – opening snap.

Mentions: The proposed system was implemented in Matlab. The LPC algorithm was used to create a database containing eight types of pathological heart sounds (early systolic murmur, pansystolic murmur, late systolic murmur, normal split S2, normal split S1, ejection click, diastolic rumble and opening snap) and four normal heart sounds (S1, S2, S3, S4). The considered waveforms of heart sounds were taken from a public database [32] and their examples are presented in Figure 7. The database consisted of six waveforms of each type, all originating from an unknown human source. Initial test were carried out by dividing in half all samples to create a set of training and testing samples (Table 1). However, due to the fact that only seventy two PCG samples were available, a leave-one-out testing strategy was also adopted to improve statistical significance of the results (Table 2).


A system for heart sounds classification.

Redlarski G, Gradolewski D, Palkowski A - PLoS ONE (2014)

Example heart sounds used in the tests.A – early systolic murmur, B – S4, C – pansystolic murmur, D – S3, E – late systolic murmur, F – normal split S2, G – normal split S1, H – ejection click, I – diastolic rumble, J – opening snap.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231067&req=5

pone-0112673-g007: Example heart sounds used in the tests.A – early systolic murmur, B – S4, C – pansystolic murmur, D – S3, E – late systolic murmur, F – normal split S2, G – normal split S1, H – ejection click, I – diastolic rumble, J – opening snap.
Mentions: The proposed system was implemented in Matlab. The LPC algorithm was used to create a database containing eight types of pathological heart sounds (early systolic murmur, pansystolic murmur, late systolic murmur, normal split S2, normal split S1, ejection click, diastolic rumble and opening snap) and four normal heart sounds (S1, S2, S3, S4). The considered waveforms of heart sounds were taken from a public database [32] and their examples are presented in Figure 7. The database consisted of six waveforms of each type, all originating from an unknown human source. Initial test were carried out by dividing in half all samples to create a set of training and testing samples (Table 1). However, due to the fact that only seventy two PCG samples were available, a leave-one-out testing strategy was also adopted to improve statistical significance of the results (Table 2).

Bottom Line: The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods.Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue.The respective system is compared with four different major classification methods, proving its reliability.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland.

ABSTRACT
The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases - one of the major causes of death around the globe - a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.

Show MeSH
Related in: MedlinePlus