The Deep Multi-Scale Fusion convolutional neural network (DMSFNet) achieved higher F1 scores for multi-class arrhythmia detection compared to previous state-of-the-art approaches.
Does the DMSFNet architecture improve multi-class arrhythmia detection on ECG datasets compared to previous approaches?
A novel Deep Multi-Scale Fusion Neural Network (DMSFNet) demonstrates state-of-the-art performance for multi-class arrhythmia detection across different ECG lead configurations.
Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from raw ECG signals for fine-grained diseases classification is still a challenging problem today due to variable abnormal rhythms and noise distribution. For ECG analysis, the previous research works depend mostly on heartbeat or single scale signal segments, which ignores underlying complementary information of different scales. In this paper, we formulate a novel end-to-end Deep Multi-Scale Fusion convolutional neural network (DMSFNet) architecture for multi-class arrhythmia detection. Our proposed approach can effectively capture abnormal patterns of diseases and suppress noise interference by multi-scale feature extraction and cross-scale information complementarity of ECG signals. The proposed method implements feature extraction for signal segments with different sizes by integrating multiple convolution kernels with different receptive fields. Meanwhile, joint optimization strategy with multiple losses of different scales is designed, which not only learns scale-specific features, but also realizes cumulatively multi-scale complementary feature learning during the learning process. In our work, we demonstrate our DMSFNet on two open datasets (CPSC₂018 and PhysioNet/CinC₂017) and deliver the state-of-art performance on them. Among them, CPSC₂018 is a 12-lead ECG dataset and CinC₂017 is a single-lead dataset. For these two datasets, we achieve the F1 score Formula: see text and Formula: see text which are higher than previous state-of-art approaches respectively. The results demonstrate that our end-to-end DMSFNet has outstanding performance for feature extraction from a broad range of distinct arrhythmias and elegant generalization ability for effectively handling ECG signals with different leads.
Wang et al. (Tue,) conducted a other in Arrhythmia. Deep Multi-Scale Fusion convolutional neural network (DMSFNet) vs. Previous state-of-the-art approaches was evaluated on F1 score for multi-class arrhythmia detection. The Deep Multi-Scale Fusion convolutional neural network (DMSFNet) achieved higher F1 scores for multi-class arrhythmia detection compared to previous state-of-the-art approaches.