A patient-specific 1D Convolutional Neural Network achieved superior classification performance for the detection of ventricular and supraventricular ectopic beats.
Does an adaptive 1D Convolutional Neural Network improve the detection of ventricular and supraventricular ectopic beats in patient-specific ECG classification?
A patient-specific 1D Convolutional Neural Network approach provides fast and accurate detection of ventricular and supraventricular ectopic beats on ECG.
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).
Kıranyaz et al. (Sat,) conducted a other in Ventricular and supraventricular ectopic beats. 1D Convolutional Neural Networks (CNNs) was evaluated on Detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). A patient-specific 1D Convolutional Neural Network achieved superior classification performance for the detection of ventricular and supraventricular ectopic beats.