Intracavitary atrial electrograms (EGMs) provide high-resolution insights into cardiac electrophysiology but are often contaminated by noise and remain high-dimensional, limiting real-time analysis. We introduce CLARAE (CLArity-preserving Reconstruction AutoEncoder), a one-dimensional encoder--decoder designed for atrial EGMs, which achieves both high-fidelity reconstruction and a compact 64-dimensional latent representation. CLARAE is designed to preserve waveform morphology, mitigate reconstruction artifacts, and produce interpretable embeddings through three principles: downsampling with pooling, a hybrid interpolation--convolution upsampling path, and a bounded latent space. We evaluated CLARAE on 495,731 EGM segments (unipolar and bipolar) from 29 patients across three rhythm types (AF, SR300, SR600). Performance was benchmarked against six state-of-the-art autoencoders using reconstruction metrics, rhythm classification, and robustness across signal-to-noise ratios from -5 to 15 dB. In downstream rhythm classification, CLARAE achieved F1-scores above 0.97 for all rhythm types, and its latent space showed clear clustering by rhythm. In denoising tasks, it consistently ranked among the top performers for both unipolar and bipolar signals. In order to promote reproducibility and enhance accessibility, we offer an interactive web-based application. This platform enables users to explore pre-trained CLARAE models, visualize the reconstructions, and compute metrics in real time. Overall, CLARAE combines robust denoising with compact, discriminative representations, offering a practical foundation for clinical workflows such as rhythm discrimination, signal quality assessment, and real-time mapping.

Atrial Fibrillation (AF) is the most prevalent sustained arrhythmia, yet current ablation therapies, including pulmonary vein isolation, are frequently ineffective in persistent AF due to the involvement of non-pulmonary vein drivers. This study proposes a deep learning framework using convolutional autoencoders for unsupervised feature extraction from unipolar and bipolar intracavitary electrograms (EGMs) recorded during AF in ablation studies. These latent representations of atrial electrical activity enable the characterization and automation of EGM analysis, facilitating the detection of AF drivers. The database consisted of 11,404 acquisitions recorded from 291 patients, containing 228,080 unipolar EGMs and 171,060 bipolar EGMs. The au-toencoders successfully learned latent representations with low reconstruction loss, preserving the morphological features. The extracted embeddings allowed downstream classifiers to detect rotational and focal activity with moderate performance (AUC 0.73-0.76) This work highlights the potential of unsupervised learning to uncover physiologically meaningful features from intracardiac signals. Introduction Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia in adults, affecting an estimated 59 million people around the world in 2019 [1]. It is defined as a supraventricular tachyarrhythmia characterized by disorganized electrical activity of the atrium and ineffective atrial contraction [2]. As life expectancy increases worldwide, the prevalence of AF is expected to rise accordingly [3]. Although some patients may be asymptomatic, many experience symptoms such as palpitations, fatigue, and dyspnea.

It's not a coincidence that two global multinational investment banks and financial services companies, Morgan Stanley and Goldman Sachs, agrees that the nascent metaverse market could be worth $8 trillion in the future. In its latest Technology Vision 2022 report, titled Meet me in the metaverse, multinational information technology services company, Accenture surveyed more than 4,600 business and technology leaders across 23 industries in 35 countries. Like an arms race, futuristic big tech companies Microsoft, Facebook (FB now Meta), and Apple Inc, Google (now Alphabet) amongst others, are scrambling to sweep up the metaverse. Facebook (now Meta) describes the metaverse as "a set of virtual spaces where you can create and explore with other people who aren't in the same physical space as you". CEO Mark Zuckerberg says Meta is working on egocentric data, which involves seeing worlds from a first-person perspective.

Mexico's increasingly militarized crackdown of powerful drug cartels has left nearly 39,000 unidentified bodies languishing in the country's morgues – a grotesque symbol of the ever-burgeoning war on drugs and rampant violence. Investigative NGO Quinto Elemento Labs, in a recent report, found that an alarming number of people have been simply buried in common graves without proper postmortems, while others were left in funeral homes. The so-called war of drugs has claimed the lives of nearly 300,000 people over the last 14 years, while another 73,000 have gone missing. All the while, these cartels have yet to be designated formal terrorist organizations despite boasting well-documented arsenals of sophisticated weaponry to rival most fear-inducing militias on battlefields abroad. Just last month, reports surfaced that Mexico's Jalisco New Generation Cartel (CJNG) now possess bomb-toting drones – which The Drive's Warzone depicts as "small quadcopter-type drones carrying small explosive devices to attack its enemies."