A CHF Detection Method based on Deep Learning with RR Intervals

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 2017, pp. 3369 - 3472
Issue Date:
2017-07-11
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There are extensive studies investigating congestive heart failure (CHF) detection based on heart rate variability. Although a high level of accuracy has been achieved, its robustness under different conditions is not guaranteed. To improve the robustness, we applied sparse auto-encoder-based deep learning algorithm in CHF detection with RR intervals. A total data size of 30,592 (5-min RR interval) was obtained from 72 healthy persons and 44 CHF patients. The deep learning algorithm first extracts unsupervised features using a sparse auto-encoder from raw RR intervals, then constructs a deep neural network model with various hidden nodes combinations. Results showed that the model achieved 72.41% accuracy. This demonstrated that RR intervals have potential in CHF detection but cannot fully reflect dynamic change in 24-h.
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