Estimating Heart Rate from Seismocardiogram Signal using a Novel Deep Dominant Frequency Regressor and Domain Adversarial Training-Reference-Cited by-同舟云学术

Estimating Heart Rate from Seismocardiogram Signal using a Novel Deep Dominant Frequency Regressor and Domain Adversarial Training

Published:2022-10-13 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)
language:
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Author:

Chan Michael¹,Gazi Asim H.²,Soliman Moamen²,Richardson Kristine L.²,Abdallah Calvin A.¹,Ozmen Goktug C.²,Nikbakht Mohammad²,Inan Omer T.³

Affiliation:

1. Georgia Institute of Technology,Coulter Department of Biomedical Engineering,Atlanta,GA,United States

2. Georgia Institute of Technology,School of Electrical and Computer Engineering,Atlanta,GA,United States

3. Georgia Institute of Technology,School of Electrical and Computer Engineering and Coulter Department of Biomedical Engineering,Atlanta,GA,Uited States

Funder

Centers for Disease Control and Prevention

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9948537/9948538/09948650.pdf?arnumber=9948650

Reference29 articles.

1. Ballistocardiography and Seismocardiography: A Review of Recent Advances

2. Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients

3. Ballistocardiography with fiber optic sensor in headrest position: A feasibility study and a new processing algorithm

4. Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks

5. End-toEnd Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms;suresh,2020

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