Portrait of Charles Onu is unavailable

Charles Onu

Alumni
Website
Google Scholar

Publications

Learning domain-invariant classifiers for infant cry sounds
Charles Onu
Hemanth K. Sheetha
Arsenii Gorin
Doina Precup
2023-11-29
ArXiv (preprint)
arxiv.org
arxiv.org
A cry for help: Early detection of brain injury in newborns
Charles Onu
Samantha Latremouille
Arsenii Gorin
Junhao Wang
Uchenna Ekwochi
P. Ubuane
O. Kehinde
Muhammad A. Salisu
Datonye Briggs
Yoshua Bengio
Doina Precup
2023-10-11
ArXiv (preprint)
doi.org
arxiv.org
Self-Supervised Learning for Infant Cry Analysis
Arsenii Gorin
Yusuf Cem Sübakan
Sajjad Abdoli
Junhao Wang
Samantha Latremouille
Charles Onu
In this paper, we explore self-supervised learning (SSL) for analyzing a first-of-its-kind database of cry recordings containing clinical in… (see more)dications of more than a thousand newborns. Specifically, we target cry-based detection of neurological injury as well as identification of cry triggers such as pain, hunger, and discomfort. Annotating a large database in the medical setting is expensive and timeconsuming, typically requiring the collaboration of several experts over years. Leveraging large amounts of unlabeled audio data to learn useful representations can lower the cost of building robust models and, ultimately, clinical solutions. In this work, we experiment with self-supervised pre-training of a convolutional neural network on large audio datasets. We show that pre-training with SSL contrastive loss (SimCLR) performs significantly better than supervised pre-training for both neuro injury and cry triggers. In addition, we demonstrate further performance gains through SSL-based domain adaptation using unlabeled infant cries. We also show that using such SSL-based pre-training for adaptation to cry sounds decreases the need for labeled data of the overall system.
2023-06-03
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (published)
doi.org
arxiv.org
A Fully Tensorized Recurrent Neural Network
Charles Onu
Jacob Miller
Doina Precup
2020-10-07
ArXiv (preprint)
arxiv.org
arxiv.org
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
Lara Kanbar
Charles Onu
Wissam Shalish
Karen A. Brown
Guilherme M. Sant’Anna
Robert E. Kearney
Doina Precup
Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrime… (see more)ntal effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready.Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates. We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%.
2018-07-17
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (published)
doi.org
arxiv.org
Ubenwa: Cry-based Diagnosis of Birth Asphyxia
Charles Onu
Innocent Udeogu
Eyenimi Ndiomu
Urbain Kengni
Doina Precup
Guilherme M. Sant’Anna
E. Alikor
P. Opara
Every year, 3 million newborns die within the first month of life. Birth asphyxia and other breathing-related conditions are a leading cause… (see more) of mortality during the neonatal phase. Current diagnostic methods are too sophisticated in terms of equipment, required expertise, and general logistics. Consequently, early detection of asphyxia in newborns is very difficult in many parts of the world, especially in resource-poor settings. We are developing a machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia through automated analysis of the infant cry. Deployed via smartphone and wearable technology, Ubenwa will drastically reduce the time, cost and skill required to make accurate and potentially life-saving diagnoses.
2017-11-16
ArXiv (preprint)
arxiv.org
arxiv.org