The burgeoning necessity for copious and diverse electrocardiogram (ECG) datasets for deep learning applications in clinical diagnostics has been impeded by the confidential nature of patient data. Related works have shown the effectiveness of additi...
Advanced large-scale neural interfaces call for efficient algorithms to automatically process and optimally exploit the richness of their heavy continuous flow of data. In this context, we introduce here a very frugal generic single-layer Spiking Neu...
Spike sorting, which involves detecting and attributing spikes to their putative neurons from extracellular recordings, is a common process in electrophysiology and brain-computer interface systems. Recent advances in large-scale neural recording tec...
Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structu...
The increasing adoption of prosthetic devices in medical applications introduces complex and variable load conditions, particularly due to the diverse nature of user disabilities. To address the resulting control challenges, this paper proposes a nov...
For crisp graphs, the notion of edge geodesic numbers has been known for a long time. But lately, the focus has shifted to investigating this idea in fuzzy graphs, which has resulted in studies of a number of properties. Determining a strong edge geo...
The environmental impact of Bitcoin (BTC) has been a source of concern due to its substantial energy consumption, which is a result of its proof-of-work mining algorithm and transaction processes. The global usage levels of Bitcoin are comparable to ...
Echocardiographic left ventricular hypertrophy (Echo-LVH) is frequently underdetected by traditional electrocardiogram (ECG) criteria due to limited sensitivity. We investigated whether integrating ECG with vectorcardiography (VCG) using a clinically...
Although goal-concordant care is central to patient-centered medicine, determining treatment preferences for incapacitated patients remains a challenge. Nearly two decades ago, algorithms were proposed to estimate the most likely treatment preference...
The automatic diagnosis model of medical image based on deep learning can improve the diagnosis efficiency and reduce the diagnosis cost. At present, there is a lack of research on special artificial intelligence models for medical image analysis of ...
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