Abstract
Advancements in wireless sensor networks (WSN) technology and miniaturization of wearable sensors have enabled long-term continuous pervasive biomedical signal monitoring. Wrist-worn photoplethysmography (PPG) sensors have gained popularity given their form factor. However the signal quality suffers due to motion artifacts when used in ambulatory settings, making vital parameter estimation a challenging task. In this paper, we present a novel deep learning framework, BioTranslator, for computing the instantaneous heart rate (IHR), using wrist-worn PPG signals collected during physical activity. Using one-dimensional Convolution-Deconvolution Network, we translate a single channel PPG signal to an electrocardiogram(ECG)-like time series signal, from which relevant R-peak information can be inferred enabling IHR measures. The proposed network configuration was evaluated on 12 subjects of the TROIKA dataset, involved in physical activity. The proposed network identifies 92.8% of R-peaks, besides achieving a mean absolute error of 51±6.3ms with respect to reference ECG-derived IHR.
Original language | English (US) |
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Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4241-4245 |
Number of pages | 5 |
ISBN (Electronic) | 9781538613115 |
DOIs | |
State | Published - Jul 2019 |
Externally published | Yes |
Event | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019 |
Publication series
Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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ISSN (Print) | 1557-170X |
Conference
Conference | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
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Country/Territory | Germany |
City | Berlin |
Period | 7/23/19 → 7/27/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.