arXiv subjects

Shivam Kumar Sharma

Publications and source records attributed to Shivam Kumar Sharma.

4 recordsLinked to original sources

arXiv2026

A Proliferated Space Architecture for Time-Domain Astrophysics

Time-Domain and Multi-Messenger Astrophysics (TDAMM) is entering a discovery-rich but follow-up-limited era, creating an urgent need for responsive, multiwavelength space-based capabilities. The Hydra constellation is a concept for a proliferated space architecture for time-domain astrophysics. The constellation would act as a disaggregated observatory composed of coordinated, relatively low-cost spacecraft that collectively provide capabilities traditionally concentrated within a single large mission. The architecture would combine persistent wide-field gamma-ray monitoring, wide-field and focused X-ray observations, and rapid-response ultraviolet, optical, and infrared imaging and spectroscopy. The constellation would both discover high-energy transients and respond to external alerts from gravitational-wave detectors, neutrino observatories, and ground- and space-based surveys, using low-latency communications, automated event prioritization, and community coordination frameworks to rapidly assign observing resources. A proliferated architecture would offer operational advantages over a single larger mission, including simultaneous observations of multiple targets, graceful degradation following individual spacecraft failures, recurring technology refresh, and opportunities for commercial, international, and philanthropic contributed nodes to join the network. The constellation would address fundamental questions concerning cosmic accelerators, the origin and evolution of the elements, the behavior of matter at extreme density, and the nature of dark energy through gravitational-wave standard sirens. This white paper presents the Hydra concept description that was submitted to NASA's ASTRA initiative for consideration by the Cosmic Origins Program Analysis Group (CoPAG) and Physics of the Cosmos Program Analysis Group (PhysPAG).

astro-ph.IM↗

arXiv2026

AI Generalisation Gap In Comorbid Sleep Disorder Staging

Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep learning enables automated EEG-based sleep staging in healthy subjects, our analysis shows poor generalization to clinical populations with disrupted sleep. Using Grad-CAM interpretations, we systematically demonstrate this limitation. We introduce iSLEEPS, a newly clinically annotated ischemic stroke dataset (to be publicly released), and evaluate a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging. As expected, cross-domain performance between healthy and diseased subjects is poor. Attention visualizations, supported by clinical expert feedback, show the model focuses on physiologically uninformative EEG regions in patient data. Statistical and computational analyses further confirm significant sleep architecture differences between healthy and ischemic stroke cohorts, highlighting the need for subject-aware or disease-specific models with clinical validation before deployment. A summary of the paper and the code is available at https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/

cs.LG↗

arXiv2023

Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification

We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition. In addition, it is important to note that this approach is untapped in the field, highlighting its potential for novel insights and contributions. Our proposed SE-Resnet-Transformer model provides an accurate classification of five distinct sleep stages from raw EOG signal. Extensive validation on publically available databases (SleepEDF-20, SleepEDF-78, and SHHS) reveals noteworthy performance, with macro-F1 scores of 74.72, 70.63, and 69.26, respectively. Our model excels in identifying REM sleep, a crucial aspect of sleep disorder investigations. We also provide insight into the internal mechanisms of our model using techniques such as 1D-GradCAM and t-SNE plots. Our method improves the accessibility of sleep stage classification while decreasing the need for EEG modalities. This development will have promising implications for healthcare and the incorporation of wearable technology into sleep studies, thereby advancing the field's potential for enhanced diagnostics and patient comfort.

eess.SP↗

arXiv2022

mulEEG: A Multi-View Representation Learning on EEG Signals

Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.

cs.LG↗