  Objective: Heartbeat detection remains central to cardiac disease diagnosis
and management, and is traditionally performed based on electrocardiogram
(ECG). To improve robustness and accuracy of detection, especially, in certain
critical-care scenarios, the use of additional physiological signals such as
arterial blood pressure (BP) has recently been suggested. There, estimation of
heartbeat location requires information fusion from multiple signals. However,
reported efforts in this direction often obtain multimodal estimates somewhat
indirectly, by voting among separately obtained signal-specific intermediate
estimates. In contrast, we propose to directly fuse information from multiple
signals without requiring intermediate estimates, and thence estimate heartbeat
location in a robust manner. Method: We propose as a heartbeat detector, a
convolutional neural network (CNN) that learns fused features from multiple
physiological signals. This method eliminates the need for hand-picked
signal-specific features and ad hoc fusion schemes. Further, being data-driven,
the same algorithm learns suitable features from arbitrary set of signals.
Results: Using ECG and BP signals of PhysioNet 2014 Challenge database, we
obtained a score of 94%. Further, using two ECG channels of MIT-BIH arrhythmia
database, we scored 99.92\%. Both those scores compare favourably with
previously reported database-specific results. Also, our detector achieved high
accuracy in a variety of clinical conditions. Conclusion: The proposed
CNN-based information fusion (CIF) algorithm is generalizable, robust and
efficient in detecting heartbeat location from multiple signals. Significance:
In medical signal monitoring systems, our technique would accurately estimate
heartbeat locations even when only a subset of channels are reliable.
