SearcharxivSearch

arXiv · 2304.14583

\'Epilexie: A digital therapeutic approach for treating intractable epilepsy via Amenable Neurostimulation

Abstract

Epilepsy is a neurological illness that is characterised by continuous spasms of shaking, sometimes known as convulsions. Although there are effective treatments for epilepsy, such as drugs and surgery, there is still a group of individuals who have intractable epilepsy that fails to respond to standard methods. Intractable epilepsy is a severe neurological illness that ripples across the globe and impacts millions of individuals. It is extremely difficult to control intractable epilepsy, which is defined as the lack of response to two or more standard antiepileptic medication treatments. In recent years, the use of programmable electrical stimulation of the brain has shown promise as a digital treatment strategy for lowering seizure frequency in individuals with intractable epilepsy. In this research, the use of Amenable Neurostimulation (ANS) as part of a digital treatment strategy to intractable epilepsy is investigated. When applied to the brain, ANS uses a closed-loop system to selectively stimulate neurons in the affected areas, therefore lowering the frequency of seizures. In addition, the report describes the design and execution of a pilot research employing ANS to treat intractable epilepsy, including patient selection criteria, device settings, and outcome measures. The findings of this pilot research point to the possibility that ANS might be a realistic and successful therapy option for people afflicted with intractable epilepsy. This paper demonstrated the prospects of digital medicines in treating complicated neurological illnesses and recommends future routes for research and development in this field.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ishan Shivansh Bangroo, Samia Tahzeen. 2023-04-28. \'Epilexie: A digital therapeutic approach for treating intractable epilepsy via Amenable Neurostimulation. https://doi.org/10.35629/5252-0502327334

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows

Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on inference speed but also on when ready turns are released. Most runtimes release each turn immediately upon readiness. Under contention, this eager release policy can accumulate released but unfinished work; once submitted, those turns can no longer be reordered by the workflow-level policy, increasing tail latency. We present a tail-risk-aware turn release scheduling method that jointly decides which ready turn to release next and how much released but unfinished work to maintain. The method uses a mean--Conditional Value-at-Risk (CVaR) objective to capture the evolving tail risk of unfinished workflows, incorporates online estimates of turn work when prioritizing ready turns, and adapts the released work budget to observed queue pressure. We evaluate the method using real agent execution traces from software engineering tasks across multiple LLMs and workflow arrival rates. The method performs comparably to eager release under light load and substantially reduces the P95 of workflow flow time under contention, achieving up to a \(3.50\times\) speedup.

cs.AI

Demystifying the Privacy-Utility Trade-off in LLM Interactions

The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules, causing severe utility degradation. However, the specific mechanisms governing how sanitization impacts downstream performance remain largely underexplored. To address this, we conduct a systematic analysis to deconstruct the privacy-utility trade-off, uncovering three underlying mechanisms: (1) Context-Dependent Utility, which first establishes when to sanitize by revealing that data value shifts from critical constraints to dispensable noise based on user intent; (2) Strategic Adaptation, which subsequently determines how to sanitize by dictating that the choice between removal and replacement depends on the task's reliance on factual integrity versus structural coherence; and (3) Combinatorial Interplay, which finally extends the protection scope by demonstrating that attributes form a semantic web of synergistic dependencies or antagonistic redundancies. Guided by these insights, we introduce an intent-driven local protection framework. By distilling a lightweight model Veilmind-4B to drive a dynamic extraction-sanitization-restoration pipeline, our approach reaches a low-leakage privacy point while preserving substantially higher response utility than existing privacy-oriented baselines, advancing the privacy-utility trade-off toward the Pareto frontier.

cs.AI

Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity through a survey organized around five dimensions of agenticness: environmental interaction, learning and adaptation, autonomy, goal-directed behavior, and temporal coherence. For each dimension, we examine how the underlying capability has been conceptualized across prior work and synthesize the metrics, benchmarks, and evaluation frameworks used to assess it. This review provides a structured account of the current landscape of agent evaluation, highlighting both established approaches and areas where evaluation remains limited or inconsistent. We additionally introduce the Agent Compendium, a public-facing digital resource that organizes and extends the evaluation methods identified through this review. Together, the survey and compendium provide a common structure for evaluating and comparing agent capabilities across AI systems, supporting more reproducible research, clearer communication, and more systematic study of artificial agents.

cs.AI