Abstract
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.
Highlights
Biological events can be documented by multiple signals, or modalities
recurrent neural networks (RNNs), and EN, convolutional neural networks (CNNs) boast several additional strengths: (1) there are a wide variety of filters that can be employed (2) there exists a variety of non-linearities (3) pooling separators can be applied and (4) these filters, nonlinearities, and pooling separators can be in different network layers
The CNN-gated recurrent unit (GRU) and long short-term memory (LSTM)-EN models report a 1 percentage point increase in accuracy after removing ECG, indicating that these models better predict heartbeat locations from blood pressure (BP) than ECG recordings. These findings indicate that the proposed hybrid neural networks (HNNs) and EN outperform the state-of-the-art neural networks when common modalities are not present, displaying the robustness of HNNs
Summary
Biological events can be documented by multiple signals, or modalities. If multiple modalities are recorded for an event, the existing multimodal data may reveal characteristics that each modality might not independently uncover. Many multiparameter datasets have incomplete information in the form of missing modalities and missing block-wise data. Neural networks, including convolutional neural networks (CNNs) [2], recurrent neural networks (RNNs) [3], and modular neural networks [4], have been used to analyze multimodal data These artificial neural networks, inspired by biological neural networks, learn to perform tasks through a training dataset. Several hybrid neural networks (HNNs) are proposed to analyze multiple modalities while obtaining robust results for heartbeat detection.
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