A resource-efficient ECG diagnosis model for mobile health devices

Tao, Rui; Wang, Lin; Wu, Binrong

doi:10.1016/j.ins.2023.119628

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…Additionally, generalization ability of traditional ML methods cannot stand the test of data diversity, sometimes they are prone to exhibit inferior robustness verified with various data sources [17,18,50].…”

Section: Introductionmentioning

confidence: 99%

An improved SR-LSTM network for atrial fibrillation and atrial flutter signals discrimination

Wang,

Guo

2024

Preprint

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Atrial fibrillation (AF) and atrial flutter (AFL) are the most common and easily confused arrhythmias. However, the existing AF and AFL detection approaches depend on visual examination of electrocardiogram (ECG), which is taxing and time-consuming. Motivated by state refinement module for LSTM (SR-LSTM), we investigate an improved SR-LSTM (ISR-LSTM) that is capable of adaptively refining the current states of sample points in ECG leveraging a sample point-wise attention and a representation gate mechanism for automated AF and AFL signals detection. To confirm the effectiveness, we conducted ablation experiments across two public databases. The results demonstrate the effectiveness of ISR-LSTM while achieving a significant improvement than state-of-the-art algorithms with an accuracy of 99.2% and 98.5%. To our knowledge, ISR-LSTM is the first work that integrates the interaction in ECG into deep network frame providing interpretability.

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Section: Introductionmentioning

confidence: 99%