A system for heart sounds classification.
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.
Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland.
Grzegorz
G
Redlarski
Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland.
Dawid
D
Gradolewski
Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland.
Aleksander
A
Palkowski
D006321:Heart / Q000502:physiology / Q000503:physiopathology*; D006347:Heart Sounds / Q000502:physiology*; D006801:Humans; D010701:Phonocardiography / Q000295:instrumentation; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D054160:Systolic Murmurs / Q000175:diagnosis* / Q000503:physiopathology