Why the study?
Arrhythmia is often associated with morbidity and mortality, prompting investigation into incorporating intelligence into human-centric IoT edges for detection.
A novel deep learning algorithm using edge computing demonstrated high accuracy (99.03%) for real-time arrhythmia classification on the MIT-BIH database.
May enable edge-based arrhythmia detection in IoT; leaves open prospective clinical validation before practice adoption.
In this paper, we investigate how to incorporate intelligence into the human‐centric IoT edges to detect arrhythmia, a heart condition often associated with morbidity and even mortality. We propose a classification algorithm based on the intrapatient convolutional neural network model and the interpatient attention residual network model to automatically identify the type of arrhythmia in the edges. As the imbalance categories in the MIT‐BIH arrhythmia database which needs to be used in the algorithm, we slice and overlap the original ECG signal to homogenize the heartbeat sets of different types, and then the preprocessed data was used to train the two proposed network models; the results reached an overall accuracy rate of 99.03% and an F1 value of 0.87, respectively. The proposed algorithm model can be used as a real‐time diagnostic tool for the remote E‐health system in next generation wireless communication networks.
No takes yet. Share an insight, caveat, or question.
Liu et al. (2021) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: