Reliable electrocardiogram (ECG) monitoring in wearable and space environments requires effective denoising of signals corrupted by non-stationary electromyogram (EMG) interference. This paper presents a comparative evaluation of model-based and DL-based denoising techniques for upper-arm ECG recordings acquired under real conditions. The model-based methods include three empirical mode decomposition (EMD) variants and a discrete wavelet transform (DWT) approach, while the deep learning (DL) side is represented by a stacked denoising autoencoder (SDAE) and a physics-informed neural network (PINN). All methods are evaluated on real acquisitions under both relaxed and voluntary muscle contraction conditions, using root mean squared error (RMSE), Pearson correlation, and peak-to-peak signal-to-noise ratio (PPSNR) as performance metrics. Results reveal a fundamental trade-off: DL methods achieve superior morphological reconstruction, while DWT provides the strongest noise suppression, highlighting complementary strengths for wearable cardiac monitoring applications.
Wireless observations capture radio signal responses formed through interactions with propagation environments and spatial geometry. In integrated sensing and communication, such observations have become an important basis for high-accuracy localization beyond conventional channe…
In this paper, we propose a compression framework for weighted graphs in which the graph topology is transmitted losslessly and edge weights are compressed lossily. A challenge in the lossy compression of edge weights is that the underlying relationships between edges are ambiguo…
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Accurate assessment of eating behavior is essential for understanding and managing conditions such as eating disorders, obesity, and diabetes. Wearable-based food intake detection has shown considerable promise; however, most existing approaches are trained and evaluated using in…
The emerging paradigm of Non-Conventional Internet of Things (NC IoT), which focuses on the usefulness of information rather than high-volume data collection and transmission, will be a dominant paradigm in the next generation of wireless systems. On the downside, the absence of…
Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR). Furthermore, most existing ECGAI systems are limi…