Biosignal Compression Toolbox for Digital Biomarker Discovery

Abstract

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.

Peak Detection and HRV Feature Evaluation on ECG and PPG Signals

“…The PPG is a convenient way to measure the HRV using a home-based device or in a clinical environment due to being a noninvasive, low intrusive single sensor system with competitive production cost [77]. Data compression algorithms may be used to reduce data sizes, an interesting feature for homebased monitoring systems [78].…”

Optimizing Real-Time ECG Data Transmission in Constrained Environments

“…They detect abnormal patterns in genetic files while reducing the computational power due to the compression output. The biosignal compression toolbox is introduced in [39] to support data recoverability. The authors merge transformation methods and encoding techniques, such as Huffman encoding and RLE, to create a compression pipeline on wearable ECG sensors, photoplethysmography (PPG), accelerometry, and skin temperature.…”

On Blurry Boundaries When Defining Digital Biomarkers: How Much Biology Needs to Be in a Digital Biomarker?

“…1 Of note, BioMeTs is the abbreviation for Biometric Monitoring Technologies. In a newer work Bent et al (24) mention that "Digital biomarkers are digitally collected data (e.g., a heart rate biosignal from a wrist-worn wearable) that are transformed into indicators of health outcomes (e.g., risk of cardiovascular disease)" (p. 1). In both defining sentences clearly the link between digital and biological data is obvious, although it is debatable if the biological variable needs to be directly analyzed or if its sufficient when the biological state of the human organism can be predicted in a reliable and valid way via a third (non-biological) variable.…”