A classification system combining Linear Predictive Coding and a Support Vector Machine with a Modified Cuckoo Search optimizer achieved 93.23% accuracy in simultaneously identifying 12 different heart sound classes.
A novel heart sound classification system combining Linear Predictive Coding, Support Vector Machine, and Modified Cuckoo Search achieved >93% accuracy across 12 heart sound classes.
Absolute Event Rate: 93.23% vs 71.83%
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.
Redlarski et al. (Thu,) conducted a other in Heart sounds classification (n=72). Support Vector Machine with Modified Cuckoo Search (SVM-MCS) classifier vs. Artificial Neural Network and standard SVM classifiers was evaluated on Classification accuracy for 12 simultaneous heart sound classes. A classification system combining Linear Predictive Coding and a Support Vector Machine with a Modified Cuckoo Search optimizer achieved 93.23% accuracy in simultaneously identifying 12 different heart sound classes.
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