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20152026
most citedTemporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration

40 citations · 104 across the 39 of their papers we have counts for

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9 papers · 1 filter

eess.SP2026

Multi-View Hierarchical Representation Learning of Fetal Hemodynamics for Maternal Hypertension Detection at the Edge

Alireza Rafiei, Anahí Venzor Strader, Esteban Castro Aragón +6

Hypertensive disorders of pregnancy remain a leading cause of maternal and fetal morbidity worldwide, yet diagnosis relies on intermittent cuff-based blood pressure measurements th…

eess.SP2025

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification

Weitao Tang, Johann Vargas-Calixto, Nasim Katebi +5

Introduction: This study presents FetalSleepNet, the first published deep learning approach to classifying sleep states from the ovine electroencephalogram (EEG). Fetal EEG is comp…

eess.SP2024★ 1 cited

Point-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning Approach

Mohsen Motie-Shirazi, Reza Sameni, Peter Rohloff +2

In this study, we present a deep learning framework designed to integrate with our previously developed system that facilitates large-scale 1D fetal Doppler data collection, aiming…

eess.SP2023

A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising

Mircea Dumitru, Qiao Li, Erick Andres Perez Alday +3

Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally deman…

eess.SP2022

ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation

Aref Einizade, Samaneh Nasiri, Sepideh Hajipour Sardouie +1

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert…

eess.SP2021

Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure

Ayse S. Cakmak, Samuel Densen, Gabriel Najarro +5

Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Early detection of HF symptoms and pro-active…