Paper Info
Title
SURF: Subject-Adaptive Unsupervised ECG Signal Compression for Wearable Fitness Monitors.
Abstract
Recent advances in wearable devices allow non-invasive and inexpensive collection of biomedical signals including electrocardiogram (ECG), blood pressure, respiration, among others. Collection and processing of various biomarkers are expected to facilitate preventive healthcare through personalized medical applications. Since wearables are based on size- and resource -constrained hardware, and are battery operated, they need to run lightweight algorithms to efficiently manage energy and memory. To accomplish this goal, this paper proposes SURF, a subject -adaptive unsupervised signal compressor for wearable fitness monitors. The core idea is to perform a specialized lossy compression algorithm on the ECG signal at the source (wearable device), to decrease the energy consumption required for wireless transmission and thus prolong the battery lifetime. SURF leverages unsupervised learning techniques to build and maintain, at runtime, a subject -adaptive dictionary without requiring any prior information on the signal. Dictionaries are constructed within a suitable feature space, allowing the addition and removal of code words according to the signal's dynamics (for given target fidelity and energy consumption objectives). Extensive performance evaluation results, obtained with reference ECG traces and with our own measurements from a commercial wearable wireless monitor, show the superiority of SURF against state-of-the-art techniques, including: 1) compression ratios up to 90 -times; 2) reconstruction errors between 2% and 7% of the signal's range (depending on the amount of compression sought); and 3) reduction in energy consumption of up to two orders of magnitude with respect to sending the signal uncompressed, while preserving its morphology. SURF, with artifact prone ECG signals, allows for typical compression efficiencies (CE) in the range CE E [40, 50], which means that the data rate of 3 kbit/s that would be required to send the uncompressed ECG trace is lowered to 60 and 75 bit/s for CE = 40 and CE = 50, respectively.
Year
DOI
Venue
2017
10.1109/ACCESS.2017.2749758
IEEE ACCESS
Keywords
Field
DocType
Biomedical signal processing,data compression,energy efficiency,self-organizing feature maps,unsupervised learning,wearable sensors
Lossy compression,Computer science,Wearable computer,Real-time computing,Unsupervised learning,Vector quantization,Data compression,Wearable technology,Signal compression,Uncompressed video
Journal
Volume
ISSN
Citations 
5
2169-3536
1
PageRank 
References 
Authors
0.35
12
4
Name
Order
Citations
PageRank
Mohsen Hooshmand160.82
Davide Zordan21017.67
Tommaso Melodia34398290.59
Michele Rossi422826.33