Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA.
Sensors (Basel). 2021 Jan 13;21(2):516. doi: 10.3390/s21020516.
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.
将纵向可穿戴传感器生物信号数据用于医疗保健应用和数字生物标志物开发面临的一个关键挑战是加剧医疗保健“数据泛滥”，导致新的数据存储和组织挑战及成本增加。数据聚合、采样率最小化和有效数据压缩都是整合可穿戴传感器数据以减少数据量的方法。对于生物信号数据，合适、有效和高效的数据压缩方法的研究有限。在这里，我们研究了使用基于算法和基于编码的方法组合构建的不同数据压缩管道在可穿戴传感器生物信号数据中的应用，并探讨了这些实现如何影响数据可恢复性和存储占用。测试的算法方法包括奇异值分解、离散余弦变换和双正交离散小波变换。测试的编码方法包括游程长度编码和哈夫曼编码。我们将这些方法应用于常见的可穿戴传感器数据，包括心电图 (ECG)、光电容积脉搏波 (PPG)、加速度计、皮肤电活动 (EDA) 和皮肤温度测量。在本研究中检查的方法与不同数据类型的特征一致，我们建议对 ECG 和 PPG 直接使用哈夫曼编码进行数据压缩，对 EDA 和加速度计使用哈夫曼编码的奇异值分解，对皮肤温度使用哈夫曼编码的双正交离散小波变换，以最大程度地提高压缩后数据的可恢复性。我们还报告了最大化压缩比的最佳方法。最后，我们为这里测试的每个压缩方法开发并记录了开源代码和数据，这些代码和数据可以通过数字生物标志物发现管道访问，作为“生物信号数据压缩工具箱”，这是一个用于压缩生物信号数据的开源、可访问的软件平台。
