Robust Multimodal Heartbeat Detection Using Hybrid Neural Networks

• Main Authors: Michael R. Schwob, Aeren Dempsey, Felix Zhan, Justin Zhan, Asif Mehmood
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Multimodal; heartbeat detection; deep learning; neural networks
• Online Access: https://ieeexplore.ieee.org/document/9078797/

Many arrhythmia datasets are multimodal due to the simultaneous collection of physiological signals of a subject. These datasets frequently have missing modalities or missing block-wise data, a characteristic that various recent applications of neural networks fail to consider. Most arrhythmic detection models only use electrocardiogram and blood pressure recordings. Unconsidered physiological signals may be strongly correlated with other modalities despite having missing data. To improve robustness and accuracy of heartbeat detection, all available modalities should be considered in multimodal arrhythmia datasets. Several hybrid neural networks are proposed to robustly analyze heartbeats by considering every available physiological signal. These networks combine elements from convolutional neural networks, recurrent neural networks, and a deep learning architecture. This enables researchers to analyze every signal of subjects while the set of signals collected among subjects may differ. The proposed hybrid neural networks provide more robust results in heartbeat detection when utilizing missing data modalities.