Key result
A customized convolutional neural network with channel-wise attention classified heartbeats with >99% average accuracy and specificity, and 95.4% sensitivity for ventricular ectopic beats.
Why the study?
An automated patient-specific heartbeat classification method was needed to reduce cardiologists' workload and accelerate cardiovascular disease diagnosis during long-term ECG analysis.
A novel CNN-based automated heartbeat classification method demonstrated high accuracy and specificity (>99%) in detecting pathological heartbeats on the MIT-BIH arrhythmia database.
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Advances automated ECG classification on benchmarks; leaves open prospective validation before clinical adoption.
Long-term Electrocardiogram (ECG) analysis has become a common means of diagnosing cardiovascular diseases. In order to reduce the workload of cardiologists and accelerate diagnosis, an automated patient-specific heartbeat classification method based on a customized convolutional neural network (CNN) is proposed in this paper. The parallel convolutional layers with kernels of different receptive fields in the network are responsible for extracting multi-spatial deep features of the heartbeats, and the channel-wise attention module is adopted to selectively emphasize the informative features, which are beneficial to distinguish different classes of beats. To facilitate the extraction and emphasis of the important features, each heartbeat is segmented and stacked to form the multi-channel network input according to the basic temporal characteristics of the main components (P wave, QRS complex, and T wave) in the ECG. Besides, to further improve the network generalization and achieve better performance on various ECG of new patients, a method of intra-record sample clustering is proposed to select the representative heartbeats to construct the training set. The proposed method classifies heartbeats into five classes (normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unclassifiable beat (Q)). Validated on the MIT-BIH arrhythmia database, our approach demonstrates performance superior to several state-of-the-art methods. In addition to an average accuracy and specificity of over 99%, this method achieves a sensitivity of 95.4% and a positive predictivity of 97.1% for VEB class, and a sensitivity of 81.1% and a positive predictivity of 90.0% for SVEB class. With high classification performance and pathological heartbeat detection accuracy, the proposed method is promising for clinical device applications.
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Li et al. (2019) studied Arrhythmia. Customized convolutional neural network (CNN) with channel-wise attention vs. State-of-the-art methods was evaluated on Heartbeat classification performance (accuracy, specificity, sensitivity, positive predictivity). A customized convolutional neural network with channel-wise attention classified heartbeats with >99% average accuracy and specificity, and 95.4% sensitivity for ventricular ectopic beats.
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