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Found 9 papers, 3 papers with code
Real-time Neonatal Chest Sound Separation using Deep Learning
Inspired by the Conv-TasNet model, the proposed model has an encoder, decoder, and mask generator.
Neonatal Face and Facial Landmark Detection from Video Recordings
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 estimation, pain assessment, sleep-wake classification, and jaundice detection.
Recent Advances in Scene Image Representation and Classification
For this, we, first, devise the taxonomy using the seminal existing methods proposed in the literature to this date {using deep learning (DL)-based, computer vision (CV)-based, and search engine (SE)-based methods}.
Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds
Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications.
Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds
The median processing time for the sound separation of a 10s recording was found to be 28. 3s for NMCF and 342ms for NMF.
Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications
In this study, a new method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time and to assess the effect of signal quality on vital sign estimation.
Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation
In particular, physiological monitoring methods such as Electroencephalography (EEG) are intrusive to drivers; while behavioural monitoring is least robust, affected by external factors such as lighting, as well as being subject to privacy concerns.
A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation
Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care.
Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model
Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care.
