NDLI: Reconstruction of missing physiological signals using artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Sullivan, A.M. Xia, H. McBride, J.C. Zhao, X.
Copyright Year	2010
Description	Author affiliation: Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, USA (Sullivan, A.M.; Xia, H.; McBride, J.C.; Zhao, X.)
Abstract	Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. In response to the PhysioNet/CinC Challenge 2010: Mind the Gap, we develop an algorithm based on artificial neural networks to predict the missing signals in one channel using the measurements in other channels. An artificial neural network model is created for each record, which consists of 6, 7, or 8 signals acquired from bedside ICU patient monitors. We first train the network using data from the beginning 9.5 minutes of the record. Then, we reconstruct the missing data in the subsequent 30 seconds for a specific channel. A few techniques are utilized to improve the performance of the model. Principal component analysis is used to reduce complexity and computational cost. Noisy signals are smoothed using a wavelet-based de-noising algorithm before training and testing. We explored three different neural networks: focused time-delayed neural network, distributed time-delayed neural network, and nonlinear autoregressive network with exogenous inputs. The focused time-delayed neural network is more computationally efficient while the other two networks provide slightly more precise predictions. For highly correlated data sets, all three networks are able to produce accurate predictions; however, predictions of chaotic and highly noisy data sets are less satisfactory.
Starting Page	317
Ending Page	320
File Size	362567
Page Count	4
File Format	PDF
ISBN	9781424473182
ISSN	02766547
e-ISBN	9781424473199
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Creative Commons Attribution License 2.5 (CCAL)
Subject Keyword	Training Delay Artificial neural networks Biomedical monitoring Electrocardiography Mathematical model Neurons
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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