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Biosignal Compression Toolbox for Digital Biomarker Discovery.

Brinnae Bent1, Baiying Lu1, Juseong Kim1

  • 1Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.

Sensors (Basel, Switzerland)
|January 16, 2021
PubMed
Summary
This summary is machine-generated.

Compressing wearable sensor biosignal data is key to managing the healthcare data deluge. This study recommends specific compression methods for different biosignals to improve storage efficiency and data recoverability.

Keywords:
accelerometrybiosignaldatadata compressiondata compression algorithmselectrocardiogramelectrodermal activityphotoplethysmographywearables

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Digital Health

Background:

  • Longitudinal wearable sensor data presents significant storage and organizational challenges due to the healthcare data deluge.
  • Effective data compression is crucial for managing large volumes of biosignal data from wearable devices.
  • Limited research exists on optimal data compression techniques for biosignal data.

Discussion:

  • This study evaluates various algorithmic (SVD, DCT, Biorthogonal DWT) and encoding (RLE, Huffman) methods for compressing diverse biosignal data (ECG, PPG, accelerometry, EDA, skin temperature).
  • The research investigates the impact of different compression pipelines on data recoverability and storage footprint.
  • Performance is assessed across multiple wearable sensor data types to identify optimal compression strategies.

Key Insights:

  • Recommended compression strategies include Huffman encoding for ECG and PPG, Singular Value Decomposition (SVD) with Huffman encoding for EDA and accelerometry, and Biorthogonal Discrete Wavelet Transform (DWT) with Huffman encoding for skin temperature.
  • Specific methods are identified to maximize both data recoverability and compression ratio for different biosignal types.
  • The study provides a comparative analysis of compression effectiveness for common wearable biosignals.

Outlook:

  • The development of an open-source "Biosignal Data Compression Toolbox" facilitates accessible implementation of these compression methods.
  • This toolbox aims to support the Digital Biomarker Discovery Pipeline by providing efficient data management solutions.
  • Future work could explore advanced compression algorithms and their application to novel biosignal data streams.