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AuthorKiranyaz, Serkan
AuthorInce, Turker
AuthorHamila, Ridha
AuthorGabbouj, Moncef
Available date2023-04-04T09:09:09Z
Publication Date2015
Publication NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ResourceScopus
URIhttp://dx.doi.org/10.1109/EMBC.2015.7318926
URIhttp://hdl.handle.net/10576/41640
AbstractWe propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). 2015 IEEE.
Languageen
PublisherIEEE
Subjectalgorithm
artificial neural network
electrocardiography
heart ventricle extrasystole
human
pathophysiology
physiologic monitoring
supraventricular premature beat
Algorithms
Atrial Premature Complexes
Electrocardiography
Humans
Monitoring, Physiologic
Neural Networks (Computer)
Ventricular Premature Complexes
TitleConvolutional Neural Networks for patient-specific ECG classification
TypeConference Paper
Pagination2608-2611
Volume Number2015-November
dc.accessType Abstract Only


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