Convolutional Neural Networks for patient-specific ECG classification
Author | Kiranyaz, Serkan |
Author | Ince, Turker |
Author | Hamila, Ridha |
Author | Gabbouj, Moncef |
Available date | 2023-04-04T09:09:09Z |
Publication Date | 2015 |
Publication Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
Resource | Scopus |
Abstract | We 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. |
Language | en |
Publisher | IEEE |
Subject | algorithm 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 |
Type | Conference Paper |
Pagination | 2608-2611 |
Volume Number | 2015-November |
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Electrical Engineering [2568 items ]