Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136679
Title: A novel deep learning based neural network for heartbeat detection in ballistocardiograph
Authors: Lu, Han
Zhang, Haihong
Lin, Zhiping
Ng, Soon Huat
Keywords: Engineering::Bioengineering
Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Lu, H., Zhang, H., Lin, Z., & Ng, S. H. (2018). A novel deep learning based neural network for heartbeat detection in ballistocardiograph. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2563-2566. doi:10.1109/EMBC.2018.8512771
Abstract: Ballistocardiography (BCG) is a revamped technology for cardiac function monitoring. Detecting individual heart beats in BCG remains a challenging task due to various artifacts and low signal-to-noise ratio, which are not well addressed by conventional approaches based on intuitive observations of BCG waveforms. In this paper, we propose to employ deep learning networks to capture the characteristics of the variations of BCG waveforms within and across individual subjects. Particularly, we design a neural network that combines Convolutional-Neural-Network (CNN) and Extreme Learning Machine (ELM). We test the new learning method on a real BCG data set and show better detection result compared with a state-of-the-art method. We demonstrate how the advanced machine learning technology can learn and detect BCG waveforms robustly.
URI: https://hdl.handle.net/10356/136679
ISBN: 9781538636466
DOI: 10.1109/EMBC.2018.8512771
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/EMBC.2018.8512771.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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