The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

The paper proposes a novel convolutional block and optimization algorithm to implement resource-efficient 1D-CNNs for atrial fibrillation detection on tiny smart sensor systems, achieving high accurac…

The paper proposes a novel, highly secure real-time ECG monitoring framework that uses a patient's own ECG signal characteristics to generate unique, dynamic encryption keys, ensuring confidential dat…

This paper introduces LVCG, a novel self-supervised framework that learns unified, view-invariant latent representations of cardiac electrical activity directly in the physically grounded Vectorcardio…

This paper evaluates unsupervised temporal learning models, specifically recurrent autoencoders, for real-time anomaly detection in vulnerable IEC-61850 GOOSE networks, demonstrating that the GRU mode…

The paper proposes an attention-guided hybrid framework combining 1D and 2D CNNs to robustly enhance ECG-based biometric recognition, achieving high accuracy across multiple datasets and demonstrating…

The paper introduces CardioLens, a rigorous evaluation testbed for multi-sequence Cardiac MRI, which reveals that current Multimodal Large Language Models (MLLMs) exhibit a significant 'clinical reali…

The paper introduces FHRFormer, a masked transformer-based autoencoder designed to accurately reconstruct missing and forecast fetal heart rate (FHR) time-series data, thereby enabling robust AI-based…

This paper presents BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge.

Adaptive data selection significantly improves wearable prediction performance, particularly for individuals with poor baseline health metrics, suggesting that selective data sampling should be tailor…

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

The paper introduces Score-Guided Classification (SGC), a novel framework that uses an unsupervised anomaly score as a 'Pathological Prior' to guide EEG-based depression detection, overcoming the limi…

VitalAgent is a novel tool-augmented agentic framework that significantly improves physiological monitoring from wearable health data by enabling both reactive question answering and proactive, long-t…

The paper proposes the Distilled Explanation Model (DEM), a novel glass-box framework that achieves high-accuracy, clinically interpretable anomaly detection in physiological sensor data by distilling…

The paper proposes 'Think Fast, Talk Smart,' a pipeline that separates deterministic data analysis from LLM generation, showing that offloading recurring, structured tasks to code significantly improv…

This paper proposes a novel, on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for IoMT environments, achieving high classification accuracy while providing transpar…