arXiv:2607. 14747v1 Announce Type: cross Abstract: Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective detection and diagnosis.
By Floriaan Bulten, Yawar Rasheed, Arlene John, Vincenzo Stoico, Ghayoor Gillani
arXiv:2606. 02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems.
By Nagarajan S, Kurian Polachan
arXiv:2601.11433v2 Announce Type: replace
Abstract: Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification o...
By Wout Mommen, Lars Keuninckx, Paul Detterer, Achiel Colpaert, Piet Wambacq
arXiv:2508. 11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia.
By Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi
arXiv:2510. 18668v4 Announce Type: replace Abstract: Wearable cardiovascular sensor patches promise continuous, unobtrusive monitoring, but their tight energy, memory, and compute budgets make it unclear whether physiological signals should be analyzed on the device or streamed to the cloud for processing.
By Mustafa Fuad Rifet Ibrahim, Tunc Alkanat, Felix Manthey, Maurice Meijer, Alexander Schlaefer, Peer Stelldinger
The paper introduces R‑U‑Net, an ECG delineation model that combines a ResNet‑18 encoder with a U‑Net decoder. It demonstrates that this decoder design outperforms a ResNet‑18 + fully convolutional network baseline across 16 in‑domain settings and improves cross‑domain performance by 8.1 mIoU. Ablation studies reveal that the decoder contributes more to performance gains than the evaluated semi‑supervised learning methods.
By Joseph Scharpf, William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, Ding Zhao