Abstract
Most researchers use features of diastolic murmurs to identify coronary artery disease. However, the diastolic murmurs of coronary artery disease are usually very weak and are easily contaminated by noise and valvular murmurs. Therefore, the diagnostic accuracy of coronary artery disease when only using diastolic murmurs is not well. An algorithm for improving the accuracy in the identification of coronary artery disease by combining the features of the first heart sound and diastolic murmurs was proposed. Firstly, a first heart sound feature extraction algorithm was used to identify coronary artery disease from noncoronary artery disease. Secondly, the Empirical Wavelet Transform algorithm was used to decompose the diastolic heart sound into three modes, and the spectral energy of each mode was calculated to distinguish coronary artery disease from noncoronary artery disease. Then, the features of the fist heart sound, the second diastolic spectral energy, and the parameter P3, which was used to discriminate the diastolic murmurs in coronary artery disease and in valvular disease, were combined together to improve the diagnostic accuracy of coronary artery disease. The comparison experiment results show that the accuracy of the proposed algorithm is superior to some state-of-the-art methods when they are used to diagnose coronary artery disease.
Highlights
Coronary artery disease (CAD), identified by the World Health Organization as the “number one killer”, kills about 17.3 million people each year globally, and it is expected to increase to 23.6 million by 2030 [1]
Pathological heart sounds often contain all kinds of systolic and diastolic murmurs or S3 and S4 both in CAD and in valvular heart disease, which will make the segmentation of diastolic heart sound become difficult and greatly affect the accuracy of CAD’s identification based on the feature extraction of diastolic murmurs
According to the different frequency distribution ranges of heart sounds and murmurs, the Empirical Wavelet Transform (EWT) is used to separate the murmurs and heart sounds, and the double threshold segmentation algorithm is used to extract the pure diastolic heart sounds, and the original diastolic heart sounds with murmurs can be extracted according to the position of the pure diastolic heart sounds
Summary
Coronary artery disease (CAD), identified by the World Health Organization as the “number one killer”, kills about 17.3 million people each year globally, and it is expected to increase to 23.6 million by 2030 [1]. One-third of the patients with CAD encountered myocardial infarction, heart failure, or other serious events when they first presented clinical symptoms of CAD and died without the opportunity to receive targeted treatment [3]. The reason of this is that the commonly used CAD detection methods are usually expensive, invasive, inconvenient, and unable to realize the accurate early detection of CAD [4–9]. With the successful development of electronic stethoscopes and the digital signal processing technology, the extraction of heart sound characteristic parameters to assist the diagnosis of various cardiovascular diseases has once again become a research hotspot globally [11–13]
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