Abstract
Arrhythmia is a common disease in the elderly. If not found in time and without effective treatment, it will lead to serious consequences. Electrocardiogram (ECG) is a tool for recording and displaying ECG signals. In this paper, we construct a deep neural network (DNN) based on fully connected neural network to predict the arrhythmia, which has 16 layers. In the DNN, the output layer has four units, corresponding to the normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), and ventricular premature contraction (VPC) heartbeat respectively. We call the algorithm ECG Anomaly Prediction DNN (EAPD). In order to predict the types of the heartbeats that not happened yet, we classify the type of the ECG segment before a heartbeat, rather than classifying the heartbeat itself. We use two time lengths of a segment: 5.6 s and 11.2 s before a heartbeat. Experiment results show that the prediction using 5.6 s segment has better performance.
Supported by the National Natural Science Foundation of China (Grants No 61702274) and the Natural Science Foundation of Jiangsu Province (Grants No BK20170958), and PAPD.
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Wang, Y. et al. (2020). A DNN for Arrhythmia Prediction Based on ECG. In: Huang, Z., Siuly, S., Wang, H., Zhou, R., Zhang, Y. (eds) Health Information Science. HIS 2020. Lecture Notes in Computer Science(), vol 12435. Springer, Cham. https://doi.org/10.1007/978-3-030-61951-0_14
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DOI: https://doi.org/10.1007/978-3-030-61951-0_14
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