Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

• URL: http://arxiv.org/abs/2602.19674v2
• Date: Wed, 25 Feb 2026 01:57:23 GMT
• Title: Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics
• Abstract summary: We propose a Longitudinal Intra-Patient Tracking (LIPT) scheme to capture the trajectory of relative symptomatic changes within individuals.<n>Central to this framework is a Sequential Personalised (PSE) which transforms longitudinal speech recordings into context-aware latent representations.<n> Experimental results from a cohort of 225 patients demonstrate that the LIPT paradigm significantly outperforms the classic cross-sectional approaches.
• Score: 17.682803546212824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote monitoring of heart failure (HF) via speech signals provides a non-invasive and cost-effective solution for long-term patient management. However, substantial inter-individual heterogeneity in vocal characteristics often limits the accuracy of traditional cross-sectional classification models. To address this, we propose a Longitudinal Intra-Patient Tracking (LIPT) scheme designed to capture the trajectory of relative symptomatic changes within individuals. Central to this framework is a Personalised Sequential Encoder (PSE), which transforms longitudinal speech recordings into context-aware latent representations. By incorporating historical data at each timestamp, the PSE facilitates a holistic assessment of the clinical trajectory rather than modelling discrete visits independently. Experimental results from a cohort of 225 patients demonstrate that the LIPT paradigm significantly outperforms the classic cross-sectional approaches, achieving a recognition accuracy of 99.7% for clinical status transitions. The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings. Furthermore, this work addresses the gap in existing literature by providing a comprehensive analysis of different speech task designs and acoustic features. Taken together, the superior performance of the LIPT framework and PSE architecture validates their readiness for integration into long-term telemonitoring systems, offering a scalable solution for remote heart failure management.

Related papers

• ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding [47.35612571473169]
  EEG visual decoding aims to recover visual semantics from non-invasive neural time-series signals.<n>Existing methods rely on fixed visual or textual anchors whose semantic relations may become misaligned with EEG representations that vary across trials, subjects, and learning stages.<n>We propose Progressive Contrastive Alignment (ProCA), a unified and model-agnostic framework for adaptive neural-semantic alignment.
  arXiv  Detail & Related papers  (2026-09-04T12:48:25Z)
• CT-$Δ$Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models [21.022823890203597]
  longitudinal imaging difference reporting is a task in which a model takes two temporally separated scans from the same patient and generates a clinically meaningful report.<n>We introduce CT-$$Bench, a dedicated benchmark for this task with patient-level splitting to prevent information leakage.<n>We also develop change-aware metrics specifically designed to capture clinically meaningful longitudinal changes, and conduct an independent physician validation.
  arXiv  Detail & Related papers  (2026-08-12T00:55:31Z)
• Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture [71.46525715889656]
  General-purpose agent memory systems optimize for coherence by overwriting older facts with the user's latest statement.<n>We introduce a Dual-Stream Memory Architecture that strictly separates the patient narrative from the structured clinical record.<n>We evaluate this architecture on 26 patients across 675 longitudinal wellness coaching sessions.
  arXiv  Detail & Related papers  (2026-04-29T17:59:28Z)
• An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis [1.2871968485402088]
  Existing vision-language models fail to address the extreme class imbalance prevalent in clinical segmentation datasets.<n>We present an end-to-end Explainable Vision-Language Model framework designed to overcome these limitations.<n>We show considerable performance: a diagnostic classification accuracy of 90.69%, a macro-averaged Dice score of 0.9512 for segmentation, and a CIDEr score of 92.80%.
  arXiv  Detail & Related papers  (2026-04-02T20:18:35Z)
• Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity [66.94391219005291]
  This paper proposes Agentic Cognitive Profiling (ACP), an agentic framework that realigns automated screening with clinical protocol logic.<n>Central to our design is decoupling semantic understanding from measurement by delegating all quantification to deterministic function calling.<n>ACP achieves 90.5% score match rate in task examination and 85.3% accuracy in AD prediction, surpassing popular baselines.
  arXiv  Detail & Related papers  (2026-03-18T06:15:35Z)
• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis [14.922065513695294]
  Resp-Agent is an autonomous multimodal system orchestrated by a novel Active Adrial Curriculum Agent (Thinker-A$2$CA)<n>To address the representation gap, we introduce a Modality-Weaving Diagnoser that weaves EHR data with audio tokens via Strategic Global Attention.<n>To address the data gap, we design a Flow Matching Generator that adapts a text-only Large Language Model (LLM) via modality injection.
  arXiv  Detail & Related papers  (2026-02-16T14:48:24Z)
• Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening [0.7136933021609076]
  This study presents a fast, non-invasive multimodal deep learning framework for automatic binary stroke screening based on data collected during the F.A.S.T. assessment.<n>The proposed approach integrates complementary information from facial expressions, speech signals, and upper-body movements to enhance diagnostic robustness.
  arXiv  Detail & Related papers  (2026-01-17T03:35:39Z)
• FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI [44.4791295950757]
  We develop an unsupervised anomaly detection (UAD) approach for brain MRI.<n>We conduct the first systematic frequency-domain analysis of pathological signatures.<n>We show that Frequency-Decomposition Preprocessing (FDP) framework can leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation.
  arXiv  Detail & Related papers  (2025-11-17T02:40:14Z)
• Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis [0.5537760992845262]
  This paper investigates the performance of the ULS23 segmentation model in a longitudinal context.<n>We identify two critical, interconnected failure modes: a sharp degradation in segmentation quality in follow-up cases due to inter-scan registration errors, and a subsequent breakdown of the lesion correspondence process.
  arXiv  Detail & Related papers  (2025-07-25T12:55:48Z)
• From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs [38.49879425944787]
  We propose a novel approach to patient pathway modeling by transforming diverse electronic health record (EHR) data into a structured representation.<n>We introduce a novel summary mechanism that embeds long-term temporal context into topic-specific summary tokens, improving performance over text-only models.
  arXiv  Detail & Related papers  (2025-06-05T09:54:01Z)
• Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia [44.39545678576284]
  This study analyzes home activity data from individuals living with dementia by proposing a two-stage, self-supervised learning approach.<n>The first stage converts time-series activities into text sequences encoded by a pre-trained language model.<n>This PageRank vector captures latent state transitions, effectively compressing complex behaviour data into a succinct form.
  arXiv  Detail & Related papers  (2025-02-13T10:57:25Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [50.56875995511431]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
  Engagement measurement finds application in healthcare, education, services. Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
  arXiv  Detail & Related papers  (2024-04-05T20:39:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.