A novel deep learning based neural network for heartbeat detection in ballistocardiograph

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 B...

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Main Authors:	Lu, Han, Zhang, Haihong, Lin, Zhiping, Ng, Soon Huat
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2020
Subjects:	Engineering::Bioengineering Engineering::Electrical and electronic engineering Deep Learning Heartbeat Detection
Online Access:	https://hdl.handle.net/10356/136679
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-136679
record_format	dspace
spelling	sg-ntu-dr.10356-1366792020-01-10T03:31:18Z A novel deep learning based neural network for heartbeat detection in ballistocardiograph Lu, Han Zhang, Haihong Lin, Zhiping Ng, Soon Huat School of Electrical and Electronic Engineering 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering::Bioengineering Engineering::Electrical and electronic engineering Deep Learning Heartbeat Detection 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. Accepted version 2020-01-10T03:31:18Z 2020-01-10T03:31:18Z 2018 Conference Paper 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 9781538636466 https://hdl.handle.net/10356/136679 10.1109/EMBC.2018.8512771 30440931 2-s2.0-85056645164 2018 2563 2566 en © 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. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	Engineering::Bioengineering Engineering::Electrical and electronic engineering Deep Learning Heartbeat Detection
spellingShingle	Engineering::Bioengineering Engineering::Electrical and electronic engineering Deep Learning Heartbeat Detection Lu, Han Zhang, Haihong Lin, Zhiping Ng, Soon Huat A novel deep learning based neural network for heartbeat detection in ballistocardiograph
description	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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Lu, Han Zhang, Haihong Lin, Zhiping Ng, Soon Huat
format	Conference or Workshop Item
author	Lu, Han Zhang, Haihong Lin, Zhiping Ng, Soon Huat
author_sort	Lu, Han
title	A novel deep learning based neural network for heartbeat detection in ballistocardiograph
title_short	A novel deep learning based neural network for heartbeat detection in ballistocardiograph
title_full	A novel deep learning based neural network for heartbeat detection in ballistocardiograph
title_fullStr	A novel deep learning based neural network for heartbeat detection in ballistocardiograph
title_full_unstemmed	A novel deep learning based neural network for heartbeat detection in ballistocardiograph
title_sort	novel deep learning based neural network for heartbeat detection in ballistocardiograph
publishDate	2020
url	https://hdl.handle.net/10356/136679
_version_	1681034091280990208

A novel deep learning based neural network for heartbeat detection in ballistocardiograph

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