Key result
SIC-EDGE semantic ECG compression improves recall by ~35% versus static wavelet compression.
Why the study?
Wearable sensors and edge computing face wireless link capacity constraints when transferring information-rich data for real-time health monitoring, necessitating compression strategies that balance compression gain and detection accuracy.
SIC-EDGE provides an adaptive semantic compression strategy for ECG signals that improves disease detection accuracy over static compression methods in edge-assisted wearable systems.
Answers are grounded in this paper and cite their sources.
May enhance ECG disease detection in wearables; extends static methods but leaves open clinical validation.
Wearable sensors and Internet of Things technologies are enabling automated health monitoring applications, where signals captured by sensors are analyzed in real-time by algorithms detecting health issues and conditions. However, continuous clinical-level monitoring of patients in everyday settings often requires computation, storage and connectivity capabilities beyond those possessed by wearable sensors. While edge computing partially resolves this issue by connecting the sensors to compute-capable devices positioned at the network edge, the wireless links connecting the sensors to the edge servers may not have sufficient capacity to transfer the information-rich data that characterize these applications. A possible solution is to compress the signal to be transferred, accepting the tradeoff between compression gain and detection accuracy. In this paper, we propose SIC-EDGE: a "semantic compression" framework whose goal is to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred from a wearable sensor to an edge server to perform real-time detection of heart diseases. The core idea is to establish a collaborative control loop between the sensor and the edge server to iteratively build a semantic representation that is: (i) ECG-cycle specific; (ii) personalized, and (iii) targeted to support the classification task rather than signal reconstruction. The core of SIC-EDGE is a Sequential Hypothesis Testing (SHT) algorithm that analyzes partial representations along the iterations to determine which and how many representation layers (wavelet coefficients in our implementation) are requested. Our results on established datasets demonstrates the need for adaptive "semantic" compression, and illustrate the dynamic compression strategy realized by SIC-EDGE. We show that SIC-EDGE leads to an increase in terms of recall and F1 score of up to 35% and 26% respectively compared to an optimized but static wavelet compression for a given maximum channel usage.
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Amiri et al. (2022) studied heart diseases. SIC-EDGE framework vs. optimized but static wavelet compression was evaluated on recall and F1 score. SIC-EDGE, a semantic compression framework for ECG signals, increased recall and F1 score by up to 35% and 26% respectively compared to static wavelet compression for a given maximum channel usage.
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