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Design and Analysis of Digital Compressed ECG Sensing Encoder for IoT Health Monitoring Devices
Published in Springer Science and Business Media Deutschland GmbH
2022
Volume: 273
   
Pages: 550 - 562
Abstract
Most wearable medical devices have stringent constraints of limited battery power, memory space and data rate, exploring lightweight efficient compression method is highly demanded to extend the battery life and seamlessly transmit electrocardiogram (ECG) signals to the edge computing and cloud computing server. In this paper, we present a design and analysis of digital compressed sensing (DCS)-based ECG encoder by exploring suitable sensing matrices such as deterministic binary block diagonal (DBBD) matrix, random Bernoulli sensing matrix (RBeSM), random sparse binary sensing matrix (RSBiSM) and random binary sensing matrix (RBiSM). The DCS-based ECG encoder was evaluated using a wide variety of ECG signals and benchmark performance metrics such as compression ratio (CR), signal-to-noise ratio (SNR), percentage root mean square difference (PRD) and reconstruction time. Effectiveness of each of the sensing matrices was studied with different sampling rates, measurement quantization bit and number of measurements. For all performance evaluation conditions, results showed that the DCS-based ECG encoder with DBBD sensing matrix can provide higher CRs with minimal reconstruction error (in terms of SNR and PRD) and reconstruction time (in ms). Our study further demonstrated a suitable selection of measurement quantization bit can significantly improve the compression efficiency of the DCS-based ECG encoder for a given PRD or SNR value. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
About the journal
JournalData powered by TypesetSmart Innovation, Systems and Technologies
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH
ISSN21903018