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Biosignal Compression Toolbox for Digital Biomarker Discovery

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization...

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Autores principales: Bent, Brinnae, Lu, Baiying, Kim, Juseong, Dunn, Jessilyn P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828339/
https://www.ncbi.nlm.nih.gov/pubmed/33450898
http://dx.doi.org/10.3390/s21020516
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author Bent, Brinnae
Lu, Baiying
Kim, Juseong
Dunn, Jessilyn P.
author_facet Bent, Brinnae
Lu, Baiying
Kim, Juseong
Dunn, Jessilyn P.
author_sort Bent, Brinnae
collection PubMed
description A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.
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spelling pubmed-78283392021-01-25 Biosignal Compression Toolbox for Digital Biomarker Discovery Bent, Brinnae Lu, Baiying Kim, Juseong Dunn, Jessilyn P. Sensors (Basel) Letter A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data. MDPI 2021-01-13 /pmc/articles/PMC7828339/ /pubmed/33450898 http://dx.doi.org/10.3390/s21020516 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Bent, Brinnae
Lu, Baiying
Kim, Juseong
Dunn, Jessilyn P.
Biosignal Compression Toolbox for Digital Biomarker Discovery
title Biosignal Compression Toolbox for Digital Biomarker Discovery
title_full Biosignal Compression Toolbox for Digital Biomarker Discovery
title_fullStr Biosignal Compression Toolbox for Digital Biomarker Discovery
title_full_unstemmed Biosignal Compression Toolbox for Digital Biomarker Discovery
title_short Biosignal Compression Toolbox for Digital Biomarker Discovery
title_sort biosignal compression toolbox for digital biomarker discovery
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828339/
https://www.ncbi.nlm.nih.gov/pubmed/33450898
http://dx.doi.org/10.3390/s21020516
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