title: Convolutional Neural Networks for patient-specific ECG classification.
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).
authors.forename: Serkan
authors.initials: S
authors.lastname: Kiranyaz
authors.forename: Turker
authors.initials: T
authors.lastname: Ince
authors.forename: Ridha
authors.initials: R
authors.lastname: Hamila
authors.forename: Moncef
authors.initials: M
authors.lastname: Gabbouj
mesh_terms: D000465:Algorithms; D018880:Atrial Premature Complexes / Q000503:physiopathology; D004562:Electrocardiography; D006801:Humans; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D018879:Ventricular Premature Complexes / Q000503:physiopathology