Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices

Valentina Vadori; Enrico Grisan; Michele Rossi

doi:10.1109/MLSP.2016.7738820

Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices

Valentina Vadori, Enrico Grisan, Michele Rossi

University of Padua

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

Wearable devices allow the seamless and inexpensive gathering of biomedical signals such as electrocardiograms (ECG), photoplethysmograms (PPG), and respiration traces (RESP). They are battery operated and resource constrained, and as such need dedicated algorithms to optimally manage energy and memory. In this work, we design SAM, a Subject-Adaptive (lossy) coMpression technique for physiological quasi-periodic signals. It achieves a substantial reduction in their data volume, allowing efficient storage and transmission, and thus helping extend the devices' battery life. SAM is based upon a subject-adaptive dictionary, which is learned and refined at runtime exploiting the time-adaptive self-organizing map (TASOM) unsupervised learning algorithm. Quantitative results show the superiority of our scheme against state-of-the-art techniques: compression ratios of up to 35-, 70- and 180-fold are generally achievable respectively for PPG, ECG and RESP signals, while reconstruction errors (RMSE) remain within 2% and 7% and the input signal morphology is preserved.

Original language	English
Title of host publication	2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Editors	Kostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509007462
DOIs	https://doi.org/10.1109/MLSP.2016.7738820
Publication status	Published - 10 Nov 2016
Externally published	Yes
Event	26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy Duration: 13 Sept 2016 → 16 Sept 2016

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2016-November
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Country/Territory	Italy
City	Vietri sul Mare, Salerno
Period	13/09/16 → 16/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

energy efficiency
signal compression
SOM
unsupervised learning
wearable devices

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MLSP.2016.7738820

Cite this

Vadori, V., Grisan, E., & Rossi, M. (2016). Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices. In K. Diamantaras, A. Uncini, F. A. N. Palmieri, & J. Larsen (Eds.), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings Article 7738820 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2016.7738820

Vadori, Valentina ; Grisan, Enrico ; Rossi, Michele. / Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices. 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings. editor / Kostas Diamantaras ; Aurelio Uncini ; Francesco A. N. Palmieri ; Jan Larsen. IEEE Computer Society, 2016. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

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abstract = "Wearable devices allow the seamless and inexpensive gathering of biomedical signals such as electrocardiograms (ECG), photoplethysmograms (PPG), and respiration traces (RESP). They are battery operated and resource constrained, and as such need dedicated algorithms to optimally manage energy and memory. In this work, we design SAM, a Subject-Adaptive (lossy) coMpression technique for physiological quasi-periodic signals. It achieves a substantial reduction in their data volume, allowing efficient storage and transmission, and thus helping extend the devices' battery life. SAM is based upon a subject-adaptive dictionary, which is learned and refined at runtime exploiting the time-adaptive self-organizing map (TASOM) unsupervised learning algorithm. Quantitative results show the superiority of our scheme against state-of-the-art techniques: compression ratios of up to 35-, 70- and 180-fold are generally achievable respectively for PPG, ECG and RESP signals, while reconstruction errors (RMSE) remain within 2% and 7% and the input signal morphology is preserved.",

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Vadori, V, Grisan, E & Rossi, M 2016, Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices. in K Diamantaras, A Uncini, FAN Palmieri & J Larsen (eds), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings., 7738820, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, vol. 2016-November, IEEE Computer Society, 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings, Vietri sul Mare, Salerno, Italy, 13/09/16. https://doi.org/10.1109/MLSP.2016.7738820

Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices. / Vadori, Valentina; Grisan, Enrico; Rossi, Michele.
2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings. ed. / Kostas Diamantaras; Aurelio Uncini; Francesco A. N. Palmieri; Jan Larsen. IEEE Computer Society, 2016. 7738820 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Grisan, Enrico

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N2 - Wearable devices allow the seamless and inexpensive gathering of biomedical signals such as electrocardiograms (ECG), photoplethysmograms (PPG), and respiration traces (RESP). They are battery operated and resource constrained, and as such need dedicated algorithms to optimally manage energy and memory. In this work, we design SAM, a Subject-Adaptive (lossy) coMpression technique for physiological quasi-periodic signals. It achieves a substantial reduction in their data volume, allowing efficient storage and transmission, and thus helping extend the devices' battery life. SAM is based upon a subject-adaptive dictionary, which is learned and refined at runtime exploiting the time-adaptive self-organizing map (TASOM) unsupervised learning algorithm. Quantitative results show the superiority of our scheme against state-of-the-art techniques: compression ratios of up to 35-, 70- and 180-fold are generally achievable respectively for PPG, ECG and RESP signals, while reconstruction errors (RMSE) remain within 2% and 7% and the input signal morphology is preserved.

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Vadori V, Grisan E, Rossi M. Biomedical signal compression with time- and subject-adaptive dictionary for wearable devices. In Diamantaras K, Uncini A, Palmieri FAN, Larsen J, editors, 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings. IEEE Computer Society. 2016. 7738820. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP.2016.7738820