Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion

IEEE Transactions on Bio-medical Engineering
B S ChandraS Jana

Abstract

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. Therefore, 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. 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. Furthermore, being data-driven, the same algorithm learns suitable features from arbitrary set of signals. Using ECG and BP signals of PhysioNet 2014 Challenge database, we obtained a score of 94%. Furthermore, ...Continue Reading

Citations

Aug 22, 2020·Yearbook of Medical Informatics·William HsuUNKNOWN Section Editors for the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics
Mar 27, 2019·Therapeutic Advances in Ophthalmology·Petteri TeikariDan Milea
Feb 19, 2020·Journal of the American Heart Association·Rahul Kumar SevakulaAntonis A Armoundas
Aug 6, 2021·Biomedical Engineering Letters·Miguel Altuve, Nelson F Monroy
Dec 3, 2021·Journal of the American Heart Association·Sandeep Chandra BollepalliAntonis A Armoundas

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