Abstract
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.
Highlights
It is reported by The World Health Organization that cardiovascular diseases are the primary cause of the world’s highest mortality, and arrhythmias are the most common [1]
Compared with a single heartbeat, the sample obtained after arbitrary segment interception of a limited amount of data is much more complex, which enables the network model to get rid of the coupling problem with the QRS detection algorithm and makes the ECG signal diagnosis process more simple and generalized
The attention residual network model proposed in this paper greatly improves the N, S, and F types of arrhythmia, optimizes the performance of the network model, and increases the robustness of the model
Summary
It is reported by The World Health Organization that cardiovascular diseases are the primary cause of the world’s highest mortality, and arrhythmias are the most common [1]. In the machine learning method, the process of the arrhythmia diagnosis algorithm usually includes three main steps: preprocessing, feature extraction, and classification. Existing work has laid a solid foundation for this field, due to the long recording time of ECG signals, low signal quality, diversity of pathological reasons, and extremely scarce data sets, how to improve the robustness of arrhythmia diagnosis results remains is a challenge. When the amount of sample data in the arrhythmia database is scarce and the number of categories is unbalanced, the existing arrhythmia diagnosis algorithms show poor performance when identifying categories with relatively small amounts of data and whose sensitivity and accuracy are both very low [16]; so, the automatic classification of ECG signals is still a difficult problem.
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