{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T13:01:44Z","timestamp":1784984504712,"version":"3.55.0"},"reference-count":31,"publisher":"IOP Publishing","issue":"8","license":[{"start":{"date-parts":[[2017,7,31]],"date-time":"2017-07-31T00:00:00Z","timestamp":1501459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2017,7,31]],"date-time":"2017-07-31T00:00:00Z","timestamp":1501459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Grant Agency of the Czech Republic and by MEYS CR","award":["LO1212"],"award-info":[{"award-number":["LO1212"]}]},{"name":"MEYS CR and EC","award":["CZ.1.05\/2.1.00\/01.0017"],"award-info":[{"award-number":["CZ.1.05\/2.1.00\/01.0017"]}]},{"name":"Czech Academy of Sciences","award":["RVO:68081731"],"award-info":[{"award-number":["RVO:68081731"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2017,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    : This paper describes a method for automated discrimination of heart sounds recordings according to the Physionet Challenge 2016. The goal was to decide if the recording refers to normal or abnormal heart sounds or if it is not possible to decide (i.e. \u2018unsure\u2019 recordings).\n                    <jats:italic>Approach<\/jats:italic>\n                    : Heart sounds S1 and S2 are detected using amplitude envelopes in the band 15\u201390 Hz. The averaged shape of the S1\/S2 pair is computed from amplitude envelopes in five different bands (15\u201390 Hz; 55\u2013150 Hz; 100\u2013250 Hz; 200\u2013450 Hz; 400\u2013800 Hz). A total of 53 features are extracted from the data. The largest group of features is extracted from the statistical properties of the averaged shapes; other features are extracted from the symmetry of averaged shapes, and the last group of features is independent of S1 and S2 detection. Generated features are processed using logical rules and probability assessment, a prototype of a new machine-learning method.\n                    <jats:italic>Main results<\/jats:italic>\n                    : The method was trained using 3155 records and tested on 1277 hidden records. It resulted in a training score of 0.903 (sensitivity 0.869, specificity 0.937) and a testing score of 0.841 (sensitivity 0.770, specificity 0.913). The revised method led to a test score of 0.853 in the follow-up phase of the challenge.\n                    <jats:italic>Significance<\/jats:italic>\n                    : The presented solution achieved 7th place out of 48 competing entries in the Physionet Challenge 2016 (official phase). In addition, the PROBAfind software for probability assessment was introduced.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/aa7620","type":"journal-article","created":{"date-parts":[[2017,5,31]],"date-time":"2017-05-31T12:17:11Z","timestamp":1496233031000},"page":"1685-1700","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Heart sounds analysis using probability assessment"],"prefix":"10.1088","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2875-3541","authenticated-orcid":false,"given":"F","family":"Plesinger","sequence":"first","affiliation":[{"name":"Institute of Scientific Instruments of the Czech Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I","family":"Viscor","sequence":"additional","affiliation":[{"name":"Institute of Scientific Instruments of the Czech Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J","family":"Halamek","sequence":"additional","affiliation":[{"name":"Institute of Scientific Instruments of the Czech Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J","family":"Jurco","sequence":"additional","affiliation":[{"name":"Institute of Scientific Instruments of the Czech Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P","family":"Jurak","sequence":"additional","affiliation":[{"name":"Institute of Scientific Instruments of the Czech Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2017,7,31]]},"reference":[{"key":"pmeaaa7620bib001","doi-asserted-by":"publisher","first-page":"809","DOI":"10.22489\/CinC.2016.235-109","type":"other","article-title":"Nonnegative matrix factorization and random forest for classification of heart sound recordings in the spectral domain","author":"Antink","year":"2016"},{"key":"pmeaaa7620bib002","doi-asserted-by":"publisher","first-page":"629","DOI":"10.22489\/CinC.2016.184-315","type":"other","article-title":"A tensor approach to heart sound classification","author":"Bobillo","year":"2016"},{"key":"pmeaaa7620bib003","doi-asserted-by":"publisher","first-page":"549","DOI":"10.22489\/CinC.2016.158-329","type":"other","article-title":"Automated classification of normal and abnormal heart sounds using support vector machines","author":"Bouril","year":"2016"},{"key":"pmeaaa7620bib004","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1016\/j.eswa.2007.08.078","type":"journal-article","article-title":"Detection of valvular heart disorders using wavelet packet decomposition and support vector machine","volume":"35","author":"Choi","year":"2008","journal-title":"Expert Syst. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2017-01-27","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2017-05-31","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2017-07-31","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}