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 in [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.

SURF: Subject-Adaptive Unsupervised ECG Signal Compression for Wearable Fitness Monitors

Hooshmand, Mohsen
;
Zordan, Davide;Rossi, Michele
2017

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 in [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.
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3270158
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