54 citations · 204 across the 16 of their papers we have counts for
17 papers
Cross-Modal Generative Framework for Signal Translation from Fetal-Maternal Electrocardiograms to Fetal Doppler Waveforms
Tongli Su, Alireza Rafiei, Marly van Assen +4
Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity while Doppler reflects mechani…
Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
Abdul Jabbar, Ethan Grooby, Yang Yi Poh +5
Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costl…
Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
Unnati Nigam, Radhendushka Srivastava, Faezeh Marzbanrad +1
Repetitive motion tasks are common in robotics, but performance can degrade over time due to environmental changes and robot wear and tear. Iterative learning control (ILC) improve…
A structural equation formulation for general quasi-periodic Gaussian processes
Unnati Nigam, Radhendushka Srivastava, Faezeh Marzbanrad +1
This paper introduces a structural equation formulation that gives rise to a new family of quasi-periodic Gaussian processes, useful to process a broad class of natural and physiol…
Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection
Talha Ilyas, Duong Nhu, Allison Thomas +13
Accurate fetal movement (FM) detection is essential for assessing prenatal health, as abnormal movement patterns can indicate underlying complications such as placental dysfunction…
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…
Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification
Weitao Tang, Johann Vargas-Calixto, Nasim Katebi +3
Study Objectives: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and dist…
Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments
Abdul Jabbar, Ethan Grooby, Jack Crozier +7
Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood. This study presents a deep learning model designed to de…
Real-time Neonatal Chest Sound Separation using Deep Learning
Yang Yi Poh, Ethan Grooby, Kenneth Tan +6
Auscultation for neonates is a simple and non-invasive method of providing diagnosis for cardiovascular and respiratory disease. Such diagnosis often requires high-quality heart an…
Neonatal Face and Facial Landmark Detection from Video Recordings
Ethan Grooby, Chiranjibi Sitaula, Soodeh Ahani +4
This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign…
Recent Advances in Scene Image Representation and Classification
Chiranjibi Sitaula, Tej Bahadur Shahi, Faezeh Marzbanrad +1
With the rise of deep learning algorithms nowadays, scene image representation methods have achieved a significant performance boost in classification. However, the performance is…
Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds
Ethan Grooby, Chiranjibi Sitaula, Kenneth Tan +6
Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope…
Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds
Ethan Grooby, Chiranjibi Sitaula, Davood Fattahi +8
Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and…
Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications
Ethan Grooby, Chiranjibi Sitaula, Davood Fattahi +8
Digital stethoscopes in combination with telehealth allow chest sounds to be easily collected and transmitted for remote monitoring and diagnosis. Chest sounds contain important in…
Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation
Emma Perkins, Chiranjibi Sitaula, Michael Burke +1
Driver drowsiness has caused a large number of serious injuries and deaths on public roads and incurred billions of taxpayer dollars in costs. Hence, monitoring of drowsiness is cr…
A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation
Ethan Grooby, Jinyuan He, Davood Fattahi +6
Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care. However, noisy chest sound recor…
Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model
Chiranjibi Sitaula, Jinyuan He, Archana Priyadarshi +6
Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care. It helps early detection of neonatal bowel dysf…