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Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/5684
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dc.contributor.authorPachori, Ram Bilasen_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:43:16Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:43:16Z-
dc.date.issued2019-
dc.identifier.citationGhosh, S. K., Tripathy, R. K., Ponnalagu, R. N., & Pachori, R. B. (2019). Automated detection of heart valve disorders from the PCG signal using time-frequency magnitude and phase features. IEEE Sensors Letters, 3(12) doi:10.1109/LSENS.2019.2949170en_US
dc.identifier.issn2475-1472-
dc.identifier.otherEID(2-s2.0-85082634516)-
dc.identifier.urihttps://doi.org/10.1109/LSENS.2019.2949170-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/5684-
dc.description.abstractIn this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix from the segmented cycles of the PCG signal. From the time-frequency matrix, the magnitude and phase features are extracted. The random forest (RF) classifier is used for the classification. The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes. © 2017 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceIEEE Sensors Lettersen_US
dc.subjectDecision treesen_US
dc.subjectDimensional stabilityen_US
dc.subjectPhonocardiographyen_US
dc.subjectWavelet transformsen_US
dc.subjectAortic stenosisen_US
dc.subjectAutomated detectionen_US
dc.subjectHeart valvesen_US
dc.subjectMitral regurgitationen_US
dc.subjectPhonocardiogramsen_US
dc.subjectRandom forest classifieren_US
dc.subjectSensor signalsen_US
dc.subjectSynchrosqueezingen_US
dc.subjectBiomedical signal processingen_US
dc.titleAutomated Detection of Heart Valve Disorders from the PCG Signal Using Time-Frequency Magnitude and Phase Featuresen_US
dc.typeJournal Articleen_US
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