Arrhythmia detection based on multi-scale fusion of hybrid deep models from single lead ECG recordings: A multicenter dataset study. (August 2022) - Ma, Chenbin; Lan, Ke; Wang, Jing; Yang, Zhicheng; Zhang, Zhengbo
Highlights: The effectiveness of multi-scale temporal fusion in sequence learning was proposed. We use inter-patient paradigms to avoid skewing the classifications. We tested the model on 3 datasets, showing strong robustness. The model detected 5 arrhythmias with 99.57% F1-score, surpassing SOTA works. Quarter memory consumption and more than 76.37% computation overhead reduction. Abstract: Background: Electrocardiogram automated arrhythmia detection plays a crucial role in the early prevention and diagnosis of cardiovascular diseases. However, previous research relies on noise removal algorithms and extracting solid features from raw ECGs. Besides, existing heartbeat classifiers ignore underlying complementary information of various scales, and intra-patient paradigms often lead to biased results. Methods: We constructed a novel end-to-end Multi-Scale Convolutional Neural Network-Sequence to Sequence architecture for heartbeat classification to address these issues. We have verified this approach on the clinical data collected by wearable devices and two heterogeneous datasets. Results: The proposed model can effectively capture multi-granularity frequency and longitudinal temporal information by fusion representation and sequence learning. The overall F1 score of our approach was achieved at 99.57%, which exceeded the reference pure cascade model by 4.36%.