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

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 3; pp. 710 - 717
• Main Authors: Chandra, B. S., Sastry, C. S., Jana, S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arrhythmia; Arrhythmias, Cardiac; Artificial neural networks; Biomedical monitoring; Blood pressure; Blood Pressure - physiology; Channels; Convolutional neural networks; Coronary artery disease; Data integration; Databases, Factual; Detectors; Disease control; Echocardiography; EKG; electrocardiogram; Electrocardiography; Electrocardiography - methods; Estimates; Heart beat; Heart diseases; Heart Rate - physiology; heartbeat detection; Humans; multimodal data fusion; Multisensor fusion; Neural networks; Neural Networks, Computer; Physiology; Robustness; Signal monitoring; Signal Processing, Computer-Assisted; Training
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2018.2854899

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. 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. 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. Furthermore, 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%. Furthermore, using two ECG channels of MIT-BIH arrhythmia database, we scored 99.92%. Both those scores compare favorably 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.