On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks

Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and mu...

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Veröffentlicht in:	IEEE transactions on biomedical circuits and systems Jg. 14; H. 6; S. 1323 - 1332
Hauptverfasser:	Singha Roy, Monalisa, Roy, Biplab, Gupta, Rajarshi, Das Sharma, Kaushik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Annotations Artificial neural networks Convolutional neural network Convolutional neural networks Latency Long short term memory Loosening Missing data Model accuracy Multilayer perceptrons Network latency Network reliability Neural networks Noise reduction on-device measurement Photoplethsymography photoplethysmogram (PPG) Prediction models prediction of missing data Predictive models Quality assessment Quality control Random access memory Reliability Reliability analysis reliability assessment Root-mean-square errors Segments stack denoising autoencoder
ISSN:	1932-4545, 1940-9990, 1940-9990
Online-Zugang:	Volltext
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