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

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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Bibliographic Details
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
Description
Summary: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.

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