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AuthorKiranyaz, Mustafa Serkan
AuthorZabihi M.
AuthorRad A.B.
AuthorInce T.
AuthorHamila R.
AuthorGabbouj M.
Available date2022-04-26T12:31:19Z
Publication Date2020
Publication NameNeurocomputing
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.neucom.2020.05.063
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087283369&doi=10.1016%2fj.neucom.2020.05.063&partnerID=40&md5=df9b732effb714b7f914a0ba280bdddd
URIhttp://hdl.handle.net/10576/30603
AbstractThe heart sound signals (Phonocardiogram ? PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess heart hemodynamic status. The need for an automated and accurate anomaly detection method for PCG has thus become imminent. To determine the state-of-the-art PCG classification algorithm, 48 international teams competed in the PhysioNet (CinC) Challenge in 2016 over the largest benchmark dataset with 3126 records with the classification outputs, normal (N), abnormal (A) and unsure ? too noisy (U). In this study, our aim is to push this frontier further; however, we focus deliberately on the anomaly detection problem while assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using 1D Convolutional Neural Networks trained with a novel data purification approach, we aim to achieve the highest detection performance and real-time processing ability with significantly lower delay and computational complexity. The experimental results over the high-quality subset of the same benchmark dataset show that the proposed approach achieves both objectives. Furthermore, our findings reveal the fact that further improvements indeed require a personalized (patient-specific) approach to avoid major drawbacks of a global PCG classification approach.
Languageen
PublisherElsevier B.V.
SubjectBenchmarking
Biomedical signal processing
Classification (of information)
Convolution
Convolutional neural networks
Purification
Signal to noise ratio
Anomaly detection methods
Benchmark datasets
Classification algorithm
Classification approach
Detection performance
High signalto-noise ratios (SNR)
International team
Realtime processing
Anomaly detection
adult
article
convolutional neural network
human
outlier detection
phonocardiography
signal noise ratio
TitleReal-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks
TypeArticle
Pagination291-301
Volume Number411
dc.accessType Abstract Only


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