{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T10:23:05Z","timestamp":1782210185053,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"Abstract<\/jats:title>A novel intelligent diagnostic system is proposed to diagnose heart sounds (HS<\/jats:bold>s). The innovations of this system are primarily reflected in the automatic segmentation and extraction of the first complex sound $$({ CS }_{1})$$<\/jats:tex-math>\n \n (<\/mml:mo>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 1<\/mml:mn>\n <\/mml:msub>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and second complex sound $$({ CS }_{2})$$<\/jats:tex-math>\n \n (<\/mml:mo>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>; the automatic extraction of the secondary envelope-based diagnostic features $$\\gamma _{_1}$$<\/jats:tex-math>\n \n \u03b3<\/mml:mi>\n \n \n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, $$\\gamma _{_2}$$<\/jats:tex-math>\n \n \u03b3<\/mml:mi>\n \n \n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and $$\\gamma _{_3}$$<\/jats:tex-math>\n \n \u03b3<\/mml:mi>\n \n \n 3<\/mml:mn>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> from $${ CS }_{1}$$<\/jats:tex-math>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $${ CS }_{2}$$<\/jats:tex-math>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>; and the adjustable classifier models that correspond to the confidence bounds of the Chi-square ($$\\chi ^{2}$$<\/jats:tex-math>\n \n \u03c7<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msup>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) distribution and are adjusted by the given confidence levels (denoted as $$\\beta$$<\/jats:tex-math>\n \u03b2<\/mml:mi>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>). The three stages of the proposed system are summarized as follows. In stage 1, the short time modified Hilbert transform (STMHT<\/jats:bold>)-based curve is used to segment and extract $${ CS }_{1}$$<\/jats:tex-math>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $${ CS }_{2}$$<\/jats:tex-math>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. In stage 2, the envelopes $${ CS _{1}}_{\\mathrm{F_{E}}}$$<\/jats:tex-math>\n \n \n C<\/mml:mi>\n \n S<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n \n F<\/mml:mi>\n E<\/mml:mi>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $${ CS _{2}}_{\\mathrm{F_{E}}}$$<\/jats:tex-math>\n \n \n C<\/mml:mi>\n \n S<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n \n F<\/mml:mi>\n E<\/mml:mi>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for periods $${ CS }_{1}$$<\/jats:tex-math>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $${ CS }_{2}$$<\/jats:tex-math>\n \n \n CS<\/mml:mi>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> are obtained via a novel method, and the frequency features are automatically extracted from $${ CS _{1}}_{\\mathrm{F_{E}}}$$<\/jats:tex-math>\n \n \n C<\/mml:mi>\n \n S<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n \n F<\/mml:mi>\n E<\/mml:mi>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and $${ CS _{2}}_{\\mathrm{F_{E}}}$$<\/jats:tex-math>\n \n \n C<\/mml:mi>\n \n S<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n \n F<\/mml:mi>\n E<\/mml:mi>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> by setting different threshold value ($$Thv$$<\/jats:tex-math>\n \n Thv<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) lines. Finally, the first three principal components determined based on principal component analysis (PCA<\/jats:bold>) are used as the diagnostic features. In stage 3, a Gaussian mixture model (GMM<\/jats:bold>)-based component objective function $$f_{ et }(\\mathbf{x })$$<\/jats:tex-math>\n \n \n f<\/mml:mi>\n \n et<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n \n (<\/mml:mo>\n x<\/mml:mi>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is generated. Then, the $$\\chi ^{2}$$<\/jats:tex-math>\n \n \u03c7<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msup>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> distribution for component k<\/jats:italic> is determined by calculating the Mahalanobis distance from $${\\mathbf{x }}$$<\/jats:tex-math>\n x<\/mml:mi>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> to the class mean $$\\mu _{_k}$$<\/jats:tex-math>\n \n \u03bc<\/mml:mi>\n \n \n k<\/mml:mi>\n <\/mml:msub>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for component k<\/jats:italic>, and the confidence region of component k<\/jats:italic> is determined by adjusting the optimal confidence level $$\\beta _{k}$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n k<\/mml:mi>\n <\/mml:msub>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and used as the criterion to diagnose HS<\/jats:bold>s. The performance evaluation was validated by sounds from online HS<\/jats:bold> databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other state-of-the-art methods, and the highest classification accuracies of $$99.43\\%$$<\/jats:tex-math>\n \n 99.43<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, $$98.93\\%$$<\/jats:tex-math>\n \n 98.93<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, $$99.13\\%$$<\/jats:tex-math>\n \n 99.13<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, $$99.85\\%$$<\/jats:tex-math>\n \n 99.85<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, $$98.62\\%$$<\/jats:tex-math>\n \n 98.62<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, 99.67$$\\%$$<\/jats:tex-math>\n %<\/mml:mo>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and 99.91$$\\%$$<\/jats:tex-math>\n %<\/mml:mo>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in the detection of MR<\/jats:bold>, MS<\/jats:bold>, ASD<\/jats:bold>, NM<\/jats:bold>, AS<\/jats:bold>, AR<\/jats:bold> and VSD<\/jats:bold> sounds were achieved by setting $$\\beta _{k}(k=1, 2, \\ldots , 7)$$<\/jats:tex-math>\n \n \n \u03b2<\/mml:mi>\n k<\/mml:mi>\n <\/mml:msub>\n \n (<\/mml:mo>\n k<\/mml:mi>\n =<\/mml:mo>\n 1<\/mml:mn>\n ,<\/mml:mo>\n 2<\/mml:mn>\n ,<\/mml:mo>\n \u2026<\/mml:mo>\n ,<\/mml:mo>\n 7<\/mml:mn>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively.<\/jats:p>","DOI":"10.1038\/s41598-021-04136-4","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T11:03:10Z","timestamp":1643108590000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel intelligent system based on adjustable classifier models for diagnosing heart sounds"],"prefix":"10.1038","volume":"12","author":[{"given":"Shuping","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiguang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongdong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"4136_CR1","doi-asserted-by":"publisher","unstructured":"Tan, Z., Wang, W., Zong, R., Pan, J. & Yang, H. 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