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AuthorInce T.
AuthorZabihi M.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
Available date2022-04-26T12:31:23Z
Publication Date2017
Publication NameIFMBE Proceedings
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-981-10-5122-7_138
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85021707201&doi=10.1007%2f978-981-10-5122-7_138&partnerID=40&md5=bf002b382943d5f754046284369c7b90
URIhttp://hdl.handle.net/10576/30630
AbstractIn this study, in order to find out the best ECG classification performance we realized comparative evaluations among the state-of-the-art classifiers such as Convolutional Neural Networks (CNNs), multi-layer perceptrons (MLPs) and Support Vector Machines (SVMs). Furthermore, we compared the performance of the learned features from the last convolutional layer of trained 1-D CNN classifier against the handcrafted features that are extracted by Principal Component Analysis, Hermite Transform and Dyadic Wavelet Transform. Experimental results over the MIT-BIH arrhythmia benchmark database demonstrate that the single channel (raw ECG data based) shallow 1D CNN classifier over the learned features in general achieves the highest classification accuracy and computational efficiency. Finally, it is observed that the use of the learned features on either SVM or MLP classifiers does not yield any performance improvement.
Languageen
PublisherSpringer Verlag
SubjectBiochemical engineering
Biomedical engineering
Computational efficiency
Convolution
Education
Electrocardiography
Neural networks
Principal component analysis
Support vector machines
Wavelet transforms
Comparative evaluations
Convolutional neural network
Dyadic wavelet transform
Ecg beat classifications
Ecg classifications
Learned and hand-crafted features
Multi-layer perceptrons (MLPs)
Support vector machine (SVMs)
Classification (of information)
TitleLearned vs. hand-designed features for ECG beat classification: A comprehensive study
TypeConference Paper
Pagination551-554
Volume Number65
dc.accessType Abstract Only


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